Circle Ci outage history

Some users might see increased wait times for gen2 Docker jobs due to capacity issues.

We are adding additional capacity to help mitigate the issue. Note: We are still seeing delays up to 20 minutes on 2xlarge.

Wait times for 2xlarge docker jobs are recovering. We will continue to update as capacity comes online.

Wait times for 2xlarge docker jobs are now under 60 seconds. Wait times for other resource sizes were not impacted by this incident and remain under 10 seconds.

We have seen a full recovery of wait times for docker jobs.

Wait times for gen2 Docker jobs have recovered and jobs are running within normal wait times.

## Summary From 15:44 to 20:55 UTC on October 21, customers running Gen 2 Docker jobs saw increased wait times for their jobs to start. Delays were longest on the 2xlarge resource class, where jobs waited up to 20 minutes. The incident was caused by limited availability of the cloud instances we had configured to meet demand, particularly for 2xlarge jobs. In response, our engineering team configured additional instance types, configured additional Gen 2 Docker clusters. By 20:55 UTC, wait times had returned to normal and the incident was resolved. After the incident was resolved, we expanded our Gen 2 Docker fleet into additional cloud regions, and we continue actively standing up additional cloud regions to prevent similar incidents in the future. The original status page can be found here: [October 21 - Increased Docker gen2 wait times](https://status.circleci.com/incidents/5jb50gh55gtj). ## Background When CircleCI executes a Docker job, we schedule it on one of several clusters of cloud instances, and each instance is capable of executing multiple isolated jobs. Scheduling a job on a cloud instance is a classic [“bin packing problem”](https://en.wikipedia.org/wiki/Bin_packing_problem): finding available capacity that fits the requested resource class’ CPU, memory, and storage. Larger resource classes like 2xlarge are harder to fit than smaller ones, which pack together more tightly. We provision cloud instances using a ranked list of instance types. If the cloud provider cannot supply our first choice, we fall back to the next type on the list. We further maintain a minimum fleet size: a minimum number of instances stay running, so that we do not give up capacity when demand is low. Above that minimum fleet size, we add instances as demand rises. When there is insufficient capacity to execute a workload, we queue that work until capacity becomes available. ## What Happened _\(All times UTC\)_ Starting at 15:44 on October 21, demand for the Gen 2 2xlarge resource class rose sharply, and we did not provision additional capacity quickly enough to keep up. Our engineering team began investigating. By 19:25, wait times for the Gen 2 2xlarge resource class had reached 20 minutes, and wait times for other resource classes had increased as well. By 19:56, our engineering team had configured additional instance types to the Gen 2 fleet in order to get more capacity online, and by 19:59, we were provisioning those instance types. At 20:11, we added several additional Gen 2 clusters, and increased the minimum fleet size on those clusters. By 20:35, the number of queued jobs returned to normal levels, and by 20:41, 95% of 2xlarge jobs were starting within 4 seconds. We resolved the incident at 20:55. After resolving this incident, we added additional cloud regions for Docker jobs to make more capacity available. ## Future Prevention and Process Improvement We have already taken the following steps to improve job start times, including sourcing capacity from more places, reserving more capacity for large resource classes, and implementing more rapid provisioning. **We have added additional regions for Docker jobs.** We recently added additional cloud regions for Machine jobs, and we have now done the same for Docker jobs. **We will actively continue adding additional cloud regions for both Docker and Machine jobs**. **We have added additional Gen 2 clusters**. We configured several additional Gen 2 clusters to schedule jobs more quickly, and to make more total capacity available. **We have expanded the set of cloud instance types we use and have raised our minimum fleet size.** We have configured additional instance types. We have also increased the minimum fleet size for Gen 2 to meet current baseline demand. **We have modified our job scheduler to prioritize scheduling of larger resource classes.** We have changed how we schedule jobs to balance wait times more fairly across resource classes, and to bin-pack jobs more efficiently onto available instances. ## Conclusion Customer experience is our top priority, and we commit to continually improving the reliability of our systems to match the trust that our customers place in us. We thank our customers for their patience while our team worked to resolve this incident. Please reach out to our support team with any questions or concerns.

We're seeing increased wait times for some macOS jobs using the m4pro.large resource class. We're investigating and will provide another update soon. Thank you for your patience.

Customers running macOS jobs on the m4pro.large resource class are still seeing increased wait times, with some jobs waiting up to 30 minutes to start. We're continuing to monitor this and will share another update shortly.

Wait times for m4pro.large are still elevated due to heavy large demand. All m4pro.medium are normal. We're actively monitoring, we appreciate patience.

Wait times for macOS jobs on the m4pro.large resource class are recovering. Jobs using Xcode 27.0 are now waiting about ~3.5 minutes on average, down from about ~40 minutes at the peak. Jobs using Xcode 26.6 and 26.4.1 are still working through a backlog, which is clearing. Wait times on m4pro.medium are normal. We will share another update soon as we continue to monitor.

Wait times for macOS jobs on the m4pro.large resource class have returned to normal. We thank you for your patience while the backlogs cleared.

Since approximately 14:00 UTC, customers running macOS jobs that use Xcode 26.6.0 on the M4 Pro large resource class are experiencing delays starting jobs. Wait times have reached 30 minutes or more. Thank you for your patience while our engineers investigate. We will provide another update by 15:30 UTC.

Delays starting macOS jobs that use Xcode 26.6.0 have recovered. Since approximately 15:11 UTC, macOS jobs that use Xcode 26.4 are experiencing delays starting, with wait times of about 30 minutes. Thank you for your patience while our engineers work to resolve this. We will provide another update by 15:45 UTC.

Between approximately 14:00 UTC and 15:51 UTC on October 14, customers running macOS jobs that use Xcode 26.6.0 or Xcode 26.4 experienced delays starting jobs. From approximately 14:00 UTC, jobs that use Xcode 26.6.0 on the M4 Pro large resource class waited 30 minutes or more to start, and these delays recovered at approximately 15:12 UTC. From approximately 15:11 UTC, jobs that use Xcode 26.4 waited about 30 minutes to start. The issue has been resolved and all affected functionality has returned to normal. We thank you for your patience while our team worked on implementing a fix.

There is a delay in our data infrastructure causing delays before build data and some notifications become visible. We are working on a mitigation.

What's impacted: Customers triggering pipelines, and customers relying on outbound webhooks and email and Slack notifications. What can you expect Since 11:15 UTC: - Pipelines are still being created while this is ongoing but not yet processed, so retriggering may result in duplicate pipelines. - Outbound webhooks and transactional notifications (Slack and email) are not being sent. - Pipeline statuses may not be visible. - Usage charge data on the Project Usage page will be stale. Thank you for your patience while our engineers work to resolve this. Next update: We will provide another update by 13:30 UTC.

Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. What's impacted: Customers triggering pipelines, and customers relying on outbound webhooks and email and Slack notifications. What can you expect: - Since about 13:00 UTC, pipelines are being processed again and jobs are starting. - Most jobs are currently starting within about 4 minutes, and some are waiting up to about 10 minutes. Pipelines triggered during the incident are still being processed and may take longer to start. - Outbound webhooks and notifications have resumed. - Pipelines are still being processed from the backlog, so retriggering may result in duplicate pipelines. Thank you for your patience while our engineers work to resolve this. Next update: We will provide another update by 14:00 UTC.

Since 11:15 UTC, customers have experienced disruption to pipelines and notifications. Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. What's impacted Customers running workflows and jobs, and customers viewing usage data on the Project Usage page. What can you expect - Pipelines are being processed again, including those triggered during the incident. - Since about 13:25 UTC, workflows and jobs have been starting with increasing delays as we work through the backlog. Most jobs are currently starting within about 10 minutes, and some are waiting up to about 25 minutes. - We expect to work through this backlog by about 14:35 UTC. - Workflows from pipelines triggered during the incident are still waiting to start, so retriggering may result in duplicate workflows. - Usage charge data on the Project Usage page is currently stale. Thank you for your patience while our engineers work to resolve this. Next update We will provide another update by 14:30 UTC.

Since 11:15 UTC, customers have experienced disruption to pipelines and notifications. Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. What's impacted Customers running workflows and jobs, and customers viewing usage data on the Project Usage page. What can you expect - Delays starting workflows and jobs are improving. Most jobs are now starting within about 3 minutes, down from about 10 minutes at 13:50 UTC. - Some workflows from pipelines triggered during the incident are still waiting up to about 40 minutes to start. - Working through the backlog is taking slightly longer than expected. We now expect to clear it by about 14:45 UTC. - Workflows from pipelines triggered during the incident are still waiting to start, so retriggering may result in duplicate workflows. - Usage charge data on the Project Usage page is currently stale. Thank you for your patience while our engineers work to resolve this. Next update We will provide another update by 15:00 UTC.

Since 11:15 UTC, customers have experienced disruption to pipelines and notifications. Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. Between about 13:00 UTC and 14:40 UTC, workflows and jobs started with delays while we worked through the backlog. What’s impacted Customers running workflows and jobs, and customers viewing usage data on the Project Usage page. What can you expect - The backlog of workflows from pipelines triggered during the incident cleared at about 14:40 UTC. - Most jobs are now starting within about 30 seconds, and some are waiting up to about 3 minutes. - Usage charge data on the Project Usage page is currently stale. Thank you for your patience while our engineers continue to work on the underlying issue. Next update We will provide another update by 15:10 UTC.

Since 11:15 UTC, customers have experienced disruption to pipelines and notifications. Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. Between about 13:00 UTC and 14:40 UTC, workflows and jobs started with delays while we worked through the backlog. What’s impacted Customers running workflows and jobs, and customers viewing usage data on the Project Usage page. What can you expect - The backlog of pipelines and workflows from the incident has cleared. - Since about 14:45 UTC, most workflows and jobs are starting within about 15 seconds, and start times are continuing to return to normal. - Usage charge data on the Project Usage page is currently stale. Thank you for your patience while our engineers monitor the recovery. Next update We will provide another update by 15:20 UTC.

Since 11:15 UTC, customers have experienced disruption to pipelines and notifications. Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. Between about 13:00 UTC and 14:40 UTC, workflows and jobs started with delays while we worked through the backlog. What's impacted Customers viewing pipelines, workflows and jobs in the CircleCI UI, and customers viewing usage data on the Project Usage page. What can you expect - Workflows and jobs are running on time. - The CircleCI UI is about 30 minutes behind. New workflows and status changes may take that long to appear. - Our engineers are actively working to speed up processing so the UI can catch up. - Please avoid retriggering workflows that don't yet appear in the UI, as this may create duplicates. - Usage charge data on the Project Usage page is currently stale. Thank you for your patience while our engineers work to reduce this delay. Next update We will provide another update by 16:00 UTC.

Since 11:15 UTC, customers have experienced disruption to pipelines and notifications. Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. Between about 13:00 UTC and 14:40 UTC, workflows and jobs started with delays while we worked through the backlog. What's impacted Customers running workflows and jobs, customers viewing pipelines, workflows and jobs in the CircleCI UI, and customers viewing usage data on the Project Usage page. What can you expect - Since about 15:15 UTC, workflows and jobs have been starting with increasing delays. Most jobs are currently starting within about 4 minutes, and some are waiting up to about 20 minutes. - The CircleCI UI remains about 30 minutes behind on average, and some updates are taking longer. New workflows and status changes may take that long to appear. - Please avoid retriggering workflows that don't yet appear in the UI, as this may create duplicates. - Usage charge data on the Project Usage page is currently stale. Thank you for your patience while our engineers work to reduce these delays. Next update We will provide another update by 16:30 UTC.

Since 11:15 UTC, customers have experienced disruption to pipelines and notifications. Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. Between about 13:00 UTC and 14:40 UTC, workflows and jobs started with delays while we worked through the backlog. Since about 15:15 UTC, workflows and jobs have again been starting with delays. What's impacted Customers running workflows and jobs, customers viewing pipelines, workflows and jobs in the CircleCI UI, and customers viewing usage data on the Project Usage page. What can you expect - Delays starting workflows and jobs have stopped increasing and are beginning to ease. Most jobs are starting within about 4 minutes, and some are waiting up to about 25 minutes. - The CircleCI UI is now about 20 minutes behind, down from about 30 minutes. New workflows and status changes may take that long to appear. - Please avoid retriggering workflows that don't yet appear in the UI, as this may create duplicates. - Usage charge data on the Project Usage page is currently stale. Thank you for your patience while our engineers work to reduce these delays. Next update We will provide another update by 17:00 UTC.

Since 11:15 UTC, customers have experienced disruption to pipelines and notifications. Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. Between about 13:00 UTC and 14:40 UTC, workflows and jobs started with delays while we worked through the backlog. Since about 15:15 UTC, workflows and jobs have again been starting with delays. What’s impacted Customers running workflows and jobs, customers viewing pipelines, workflows and jobs in the CircleCI UI, and customers viewing usage data on the Project Usage page. What can you expect - Delays starting workflows and jobs are holding steady. Most jobs are starting within about 4 minutes, and some are waiting up to about 8 minutes, down from about 25 minutes. - The CircleCI UI remains about 20 minutes behind on average, and some updates are taking longer. New workflows and status changes may take that long to appear. - Please avoid retriggering workflows that don’t yet appear in the UI, as this may create duplicates. - Usage charge data on the Project Usage page is currently stale. Thank you for your patience while our engineers work to reduce these delays. Next update We will provide another update by 17:30 UTC.

Since 11:15 UTC, customers have experienced disruption to pipelines and notifications. Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. Between about 13:00 UTC and 14:40 UTC, workflows and jobs started with delays while we worked through the backlog. Since about 15:15 UTC, workflows and jobs have again been starting with delays. What’s impacted Customers triggering pipelines and running workflows and jobs, customers viewing pipelines, workflows and jobs in the CircleCI UI, and customers viewing usage data on the Project Usage page. What can you expect - Since about 17:00 UTC, delays have increased significantly. Most jobs are now taking about 12 minutes to start, and some are waiting up to about 30 minutes. - Since about 17:15 UTC, some jobs are failing to start. - Pipelines are being processed more slowly, so some may take longer to start. - The CircleCI UI is more than an hour behind. New workflows and status changes may take that long to appear. - Please avoid retriggering workflows that don’t yet appear in the UI, as this may create duplicates. - Usage charge data on the Project Usage page is currently stale. Thank you for your patience while our engineers work to resolve this. Next update We will provide another update by 18:00 UTC.

Since 11:15 UTC, customers have experienced disruption to pipelines and notifications. Between 11:15 UTC and about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and transactional notifications (Slack and email) were not sent. Between about 13:00 UTC and 14:40 UTC, workflows and jobs started with delays while we worked through the backlog. Since about 15:15 UTC, workflows and jobs have again been starting with delays. What’s impacted Customers triggering pipelines and running workflows and jobs, customers viewing pipelines, workflows and jobs in the CircleCI UI, and customers viewing usage data on the Project Usage page. What to expect - Since about 17:00 UTC, we are seeing an increase in delays. Most jobs are now taking about 25 minutes to start, and some are waiting up to about an hour. - Since about 17:15 UTC, some in-progress workflows have failed and a small number of jobs are failing to start. - New pipelines are being queued and are taking longer to start. - The CircleCI UI is more than an hour behind. New workflows and status changes may take that long to appear. - Please avoid retriggering workflows that don't yet appear in the UI, as this may create duplicates. - Usage charge data on the Project Usage page is currently stale. Our engineers continue to work to resolve the issue at hand. Next update We will provide another update by 18:30 UTC.

What's changed since our last update * The CircleCI UI is about an hour behind, and up to about 90 minutes for some updates. Workflows and jobs may already have run, or may be running, even if the UI shows them as not started or does not show them at all. * Jobs that are running are starting faster. Most are now starting within about 15 minutes, down from about 28 minutes, though some are still waiting over an hour. * New pipelines are increasingly delayed. * Some workflows are still failing. Workflows that could not be processed in time are being marked as failed. * Please avoid retriggering workflows that don't yet appear in the UI, as this may create duplicates. Still ongoing: Usage charge data on the Project Usage page is stale. Next update We will provide another update by 19:00 UTC.

What's changed since our last update * Jobs are starting faster. Most are now starting within about 8 minutes, down from about 15 minutes, and the longest waits have dropped from over an hour to about 45 minutes. * The CircleCI UI is beginning to catch up, but it is still behind. Workflows and jobs may already have run, or may be running, even if the UI shows them as not started or does not show them at all. * Pipelines are being processed faster, but new pipelines are still delayed. Still ongoing * Some workflows and jobs are still failing. * Please avoid retriggering workflows that don't yet appear in the UI, as this may create duplicates. * Usage charge data on the Project Usage page is stale. Next update We will provide another update by 19:30 UTC.

Since our last update, jobs are starting faster: most are now starting within about 6 minutes, down from about 8 minutes. The CircleCI UI is catching up but still about an hour behind. New pipelines are being processed as they arrive. Jobs may already have run even if the UI does not show them yet, so please avoid retriggering workflows that don't appear in the UI. We will provide another update by 20:00 UTC.

Jobs are starting faster and fewer are failing: most jobs are now starting within about 4 minutes, down from about 6 minutes, and the longest waits have dropped from about 30 minutes to about 20 minutes. Pipelines from earlier in the incident are now being worked through. The CircleCI UI is still behind, and some older updates may take longer to appear. Jobs may already have run even if the UI does not show them yet, so please avoid retriggering workflows that don't appear in the UI. We will provide another update by 20:30 UTC.

Since about 20:48 UTC, new workflows and jobs are not starting. We have temporarily paused starting new work so that our systems can catch up on work already in progress, including delayed updates to the CircleCI UI and API. Older updates are now appearing in the UI and API as this backlog clears. New pipelines are still being accepted and queued Please avoid retriggering pipelines or workflows during this time, as this may create duplicates once work resumes. We will provide another update by 22:00 UTC.

Since our last update, most of the pipelines that were delayed earlier in the incident have now been processed, and their workflows are queued to start. New workflows and jobs remain paused while we prepare to resume work safely. Delayed updates continue to appear in the CircleCI UI and API. Please continue to avoid retriggering pipelines or workflows, as this may create duplicates once work resumes. We will provide another update by 22:30 UTC.

Since our last update, we have begun gradually resuming work, and some queued workflows and jobs are starting again. Most queued work is still waiting, and jobs that do start may have waited over an hour. We are increasing the rate carefully to avoid overloading our systems. Delayed updates continue to appear in the CircleCI UI and API. Please continue to avoid retriggering pipelines or workflows, as this may create duplicates as queued work resumes. We will provide another update by 23:00 UTC.

A large number of queued workflows and jobs have started, and the CircleCI UI and API have caught up and are showing updates normally with few delays. We are continuing to work through the remaining queued work. Jobs from the queue may have waited about an hour or more to start, and some Linux jobs are waiting up to about 10 minutes for capacity. Please continue to avoid retriggering while queued work continues to process. We will provide another update by 23:30 UTC.

We are still working through a significant backlog of queued workflows and jobs. Wait times are improving but they remain longer than normal and some jobs have waited over an hour. The CircleCI UI and API remain up to date. Please avoid retriggering pipelines or workflows while the backlog clears, as this may create duplicates. We will provide another update by 00:00 UTC.

Since our last update, the backlog of queued workflows and jobs has cleared, as of about 23:47 UTC. Workflows and jobs are now starting within seconds, and the CircleCI UI and API are up to date. We are monitoring our systems closely to make sure they remain stable. We will provide another update by 00:30 UTC.

Workflows and jobs have continued to start normally, within seconds, and the CircleCI UI and API remain up to date. Slack and email notifications delayed earlier in the incident are still being delivered. Usage charge data on the Project Usage page is stale. We are continuing to monitor our systems closely and will provide another update by 01:15 UTC.

Workflows and jobs are starting and running normally, and the CircleCI UI and API are up to date. We are continuing to monitor our systems closely to make sure they remain stable. We will provide another update by 02:00 UTC.

Between 11:15 UTC and 23:47 UTC on October 9, customers experienced delays and failures across pipelines, workflows and jobs, along with delays in the CircleCI UI, API and notifications. From 11:15 to about 13:00 UTC, pipelines were created but not processed, and outbound webhooks and Slack and email notifications were not sent. From about 13:00 to 14:40 UTC, workflows and jobs started with delays while we worked through the backlog. From about 15:15 to 20:48 UTC, workflows and jobs started with increasing delays, of up to about an hour, and the CircleCI UI and API fell behind by up to about 2 hours. During this time, some jobs failed to start and some in-progress workflows failed. From 20:48 to about 22:10 UTC, we temporarily paused starting new workflows and jobs so our systems could recover. New pipelines were accepted and queued during this time. From about 22:10 to 23:47 UTC, queued workflows and jobs resumed and were processed. Some of them had waited up to about 2 hours. The issue has been resolved and all affected functionality has returned to normal. Usage charge data on the Project Usage page is not yet up to date and will be updated once our data backfills complete. Customers whose jobs failed during this window may rerun affected jobs. We thank you for your patience while our engineers worked on implementing a fix.

## Summary Between 11:15 UTC and 23:47 UTC on October 9, CircleCI customers experienced hours-long delays and failures across pipelines, workflows and jobs, along with delays in the CircleCI UI, API and notifications. * From 11:15 to 13:00 UTC, pipelines were created but not processed, and outbound webhooks and Slack and email notifications were not sent. * From 13:00 to 14:40 UTC, workflows and jobs started with delays while we worked through the backlog. * From 15:15 to 20:48 UTC, workflows and jobs started with increasing delays, of up to about an hour, and the CircleCI UI and API fell behind by up to about 2 hours. During this time, some jobs failed to start and some in-progress workflows failed. * From 20:48 to 22:10 UTC, we temporarily paused starting new workflows and jobs so our systems could recover. New pipelines were accepted and queued during this time. * From 22:10 to 23:47 UTC, queued workflows and jobs resumed and were successfully processed. Some of them had waited up to about 2 hours. * By 23:47, the incident was mitigated. The status page for this incident can be found at [October 9 - Delay in data appearing in UI and API](https://status.circleci.com/incidents/6cfhhlr4rmm0). Customers experienced a similar incident earlier in the week at [October 6 - Delays in pipelines, workflows, UI data, and notifications](https://status.circleci.com/incidents/z4hbq8h49j8c). Multiple CircleCI services use Temporal backed by Postgres as a reliable queueing mechanism, and both incidents had the same cause: the rate of writes to Temporal's Postgres instance were faster than Postgres’s transaction log could archive those writes. In both cases, this caused multi-hour delays in processing both pipelines and status notifications. We evaluated several mitigations, then paused new workflow starts so the system could recover safely, and ramped back up gradually. We then throttled work back up until our systems returned to normal. To permanently resolve the bottleneck, we have split work across multiple Temporal instances, each with its own independent database and worker clusters, and we load-balance across them. We had already scheduled infrastructure work starting the week of October 12 that would have removed this exact bottleneck and prevented these incidents. So starting 19:11 on October 9 our engineering team implemented, tested, and rolled out a simpler version of these changes within a few hours. We further added new alerting and tuned the underlying databases for better performance under high load. Customers whose jobs failed during this window may rerun them. ## Background To run customer jobs, CircleCI executes a series of steps: we accept customer pipelines; we assign jobs for those pipelines to workers; we deliver status updates for those pipelines and jobs; and we charge customers for the credits those jobs consume. Each of these steps is implemented by an independent service, with reliable queues in between. Historically we have implemented these reliable queues with RabbitMQ on top of durable storage, with exponential backoff and dead letter queues for reliability. Over the last year, we have increasingly reimplemented our reliable queues on [Temporal](https://temporal.io/), which provides far better durability and reliability guarantees over a simpler and more powerful programming model. Our build service \(which turns a pipeline config into an executable plan\), our workflow orchestrator service \(which distributes tasks onto workers\), our notification service \(which delivers status updates\), and our charging service \(which charges customers for credits\) all now use Temporal. Temporal stores the state for its workflows and activities \(called “history”\) in a database, and this is the only component in a Temporal instance that is synchronous and stateful. As a consequence, a Temporal instance’s ability to accept and execute workflows is dependent on the write rate of its backing database. We have also configured Temporal to delete completed workflows and activities from its history 3 days after they complete. At the time of this incident, we used a single clustered Temporal instance backed by a Multi-AZ Postgres RDS instance for all of these reliable queues. While a Multi-AZ RDS instance is resilient to zone failures, it maintains database consistency via a single write-ahead-log \(WAL\). At the time we had configured our Postgres instance to store its WAL on a Dedicated Log Volume \(DLV\) so log writes did not compete with data writes. The database archives the log continuously to backup storage, and it can only reuse space on the volume after the log has been archived. If the database writes logs faster than it archives, the volume fills and Postgres has no space to the write its WAL log. Then the database stops accepting incoming writes and goes entirely read-only. An RDS DLV is limited to 1TB of storage and 3000 IOPS and per AWS these limits are hardcoded and cannot be increased. ## What Happened _\(All times UTC\)_ One of the contributing factors to the October 6 incident was that we were processing a high rate of Temporal history deletions at the same time as incoming work, which in turn put pressure on the backing database. To resolve the October 6 incident, we changed cleanup settings on Temporal’s largest tables and moved the transaction log back to its dedicated volume. These changes eliminated the major source of transaction log writes, but did not consider the additional log writes from scheduled deletions. Because we have configured Temporal to delete completed workflows and activities from its history 3 days after they complete, the October 6 history was scheduled to be deleted on October 9. Starting at 08:00 on October 9, Temporal began processing those scheduled deletions, and as a consequence free space on the Postgres DLV began dropping precipitously, from 0.8TB at 08:00 to 0.1TB by 10:10. Our monitoring alerted at 10:28 and we declared an incident at 10:31. While our infrastructure team was investigating, at 11:15 the DLV ran out of space and the Postgres instance went read-only. This stopped all Temporal processing in its tracks. At 11:42, we restarted Postgres to move the WAL log from the DLV onto the data volume and use the spare disk space and IOPS there. This maneuver had mitigated the incident on October 6, and we knew it would take roughly 60 minutes. While the database was restarting, we began implementing several load-shedding techniques upstream of Temporal: * We began investigating whether we should shed incoming workloads altogether to keep the rest moving, but ultimately decided that this would provide no value. * At 12:29, we flagged off credit charging updates, which represented 12% of Temporal activities. This left the Pipeline Usage page with stale charging data, until we would backfill it, but would not lose data or prevent pipelines from running. At 12:59, the database was back up and began processing work that had queued since 11:15 and by 14:40, this backlog had cleared and jobs were starting on time. However, by 15:15, delays returned as database’s routing cleanup had fallen far behind on the tables Temporal uses to track queued and scheduled work. Those tables normally hold tens of thousands of deleted rows awaiting cleanup, but by late afternoon, they held tens of millions of rows. Every Temporal operation had to read past those rows, so the database spent much more processing time on each one and Temporal ran slower. This resulted in customers continuing to see hour-long delays in starting pipelines and in status updates. For the next several hours we attempted various mitigations, including: * Manipulating Temporal configuration values to reduce database pressure. This had minimal effect. * Running manual vacuum jobs to clean up dead rows in Postgres. This ended up competing for CPU with queries themselves, and was ultimately counterproductive. We cancelled them at 17:45 to relieve the pressure on the database load. * We began trying to move all of the notification usecases to a separate Temporal instance. This represented 18% of Temporal activities. We successfully finished moving notifications by 17:07. * At 17:16, we successfully implemented an optimization in our representation of pipeline state, which reduced it by 5%. That in turn reduced the size of database writes and therefore data pressure on the Postgres WAL log. None of these fully mitigated the issue. By 20:48, we realized that Postgres was now thrashing on database CPU and was no longer bottlenecked on log writes. We further realized that our upstream systems were both submitting new work and also retrying work that had earlier been submitted and failed. Recognizing that we were in a classic retry storm, we paused all incoming and retry work and let it queue up, in order to allow the database to fully complete in-process work. We then slowly throttled up queued work from 22:07 to keep the database at a sustainable load. This fully succeeded by 23:47 when all queued work had started, and we closed the incident at 02:08 on October 10. ## Future Prevention and Process Improvement ### Multiple Temporal Instances In parallel with incident mitigation and resolution, starting at 19:11 on October 9, we also began standing up multiple sharded Temporal instances and implementing the ability to load-balance incoming work between them. Each of the multiple Temporal instances maintains its own independent database and worker clusters, and so is isolated from the others. As a postmortem action item from the October 6 incident, we had already scheduled infrastructure work that would have removed the bottleneck of a single Temporal instance and Postgres database, starting October 12 and expecting to take 2 weeks. On October 9, we realized that this work simply could not wait, so our engineering team implemented, tested, and rolled out a simpler version of the planned changes within a few hours. These changes completed successfully at 01:45 on October 10. **In addition, we are changing how we store and monitor transaction logs.** Our Temporal database no longer uses the fixed-size log volume. We are adding alerts on how far log archiving falls behind and how fast the database writes logs, so we can act hours earlier than we did on October 9. **We are also revisiting the Temporal's deletion schedule.** Temporal deletes finished work a few days after it runs, so a busy day returns as database load later in the week. We are changing when and how quickly those deletions run. **We are changing how our systems retry when Temporal is slow.** On October 9, our systems retried failed work against Temporal and added to its load. As we committed after the October 6 incident, we are changing our systems to wait for Temporal to recover so work is not dropped. ## Conclusion Customer experience is our top priority, and we commit to continually improving the reliability of our systems to match the trust that our customers place in us. We thank our customers for their patience while our team worked to resolve these incidents. Please reach out to our support team with any questions or concerns.

Customers may be experiencing elevated wait times for machine jobs. We are working to resolve this.

Customers using Linux machine jobs are experiencing elevated wait times, averaging about 11 minutes, with the longest waits exceeding 30 minutes on the medium, arm.medium and arm.large resource classes. Our engineers have identified the issue and are working on a fix. We will provide another update by 14:00 UTC.

Customers using Linux machine jobs are experiencing elevated wait times, averaging about 40 minutes, with the longest waits exceeding 50 minutes. Most Linux machine resource classes are affected, including medium, large, xlarge, 2xlarge and their Arm equivalents. Our engineers have identified the issue and are working on a fix. We will provide another update by 14:30 UTC.

Customers using Linux machine jobs and remote Docker are experiencing elevated wait times. Wait times have started to decrease and now average about 20 minutes, with the longest waits exceeding 40 minutes on some resource classes. Our engineers have identified the issue and are working on a fix. We will provide another update by 15:00 UTC.

A fix has been deployed and wait times are decreasing, but customers using Linux machine jobs and Remote Docker are still experiencing delays. Wait times currently average about 11 minutes, with the longest waits exceeding 35 minutes on some resource classes. We are working to add capacity as quickly as possible. We will provide another update by 15:30 UTC.

Wait times continue to decrease, but customers using Linux machine jobs and remote Docker are still experiencing delays. Wait times currently average about 6 minutes. The longest waits, up to about 20 minutes, are on the 2xlarge, arm.2xlarge and gpu.nvidia.small resource classes. We are working to add capacity as quickly as possible. We will provide another update by 16:00 UTC.

Wait times continue to decrease, but customers using Linux machine jobs and remote Docker are still experiencing delays. Wait times currently average about 90 seconds, with the longest waits up to about 6 minutes on some resource classes. We are working to add capacity as quickly as possible. We will provide another update by 16:30 UTC.

Wait times for customers using Linux machine jobs and remote Docker have returned to normal. We are monitoring to confirm wait times remain stable while we continue to add capacity. We will provide another update by 16:30 UTC.

Between 12:40 UTC and 16:00 UTC on October 8, customers using Linux machine jobs and remote Docker experienced elevated wait times. The issue has been resolved and wait times have returned to normal. We thank you for your patience while our team worked on implementing a fix.

## **Summary** Between October 1 and October 8, 2026, CircleCI customers experienced five separate incidents in which jobs waited longer than normal to start, or failed to start. Three of the incidents were driven by limited instance availability from our cloud provider. One was caused by a configuration issue in our scheduling infrastructure, and one by memory limits on an internal service, which we raised during the incident. * **October 1, 07:57 to 10:24 UTC:** Docker Gen 2 jobs waited up to 30 minutes to start. A configuration issue prevented the Gen 2 scheduling servers from using their high-performance local disks. During a burst in traffic, the fallback disks saturated and job placement slowed. * **October 7, 13:27 to 14:06 UTC:** Machine jobs waited longer than normal because we could not acquire enough virtual machines from our cloud provider. * **October 7, 14:24 to 15:21 UTC:** Some jobs failed with an infrastructure error, and workflows were delayed. The service that gives each job its configuration at startup ran out of memory and restarted. * **October 7, 18:49 to 20:05 UTC:** Docker Gen 2 jobs waited longer than normal because we could not acquire enough instances from our cloud provider. * **October 8, 12:48 to 16:00 UTC:** Linux machine jobs and remote Docker jobs waited up to 50 minutes to start. Our cloud provider did not have enough of the instance type we request, and a defect in the logic that picks a different instance type kept us from using alternatives. Jobs that waited during these incidents were delayed, not lost. Customers whose jobs failed with an infrastructure error during the October 7 incident from 14:24 to 15:21 UTC can rerun them. The original status pages can be found below: * [October 1 - Delays starting Gen 2 Docker Jobs](https://status.circleci.com/incidents/22mrv46g6n7g) * [October 7 - Delay on starting Machine Job Tasks](https://status.circleci.com/incidents/tj6wdnfjwr64) * [October 7 - Elevated level of infra fails on customer jobs](https://status.circleci.com/incidents/0z31ldjd8m5v) * [October 7 - Increased task wait times for Docker Gen2](https://status.circleci.com/incidents/39rfrjmk5pvd) * [October 8 - Elevated wait times for machine jobs](https://status.circleci.com/incidents/jn756xtsy4xg) ## **Background** Every job that runs on CircleCI needs compute. Where that compute comes from depends on the executor. Docker jobs run on clusters of servers that schedule work onto a fleet of instances. The scheduler is the software on those servers that decides which instance runs each job. Gen 2 Docker jobs ran on one such cluster at the time of the October 1 incident. Gen 1 Docker jobs are spread across several clusters. Machine jobs, including remote Docker jobs, get a dedicated virtual machine. A remote Docker job is handled as a Linux machine job from submission through provisioning. Our machine provisioning service requests each machine from our cloud provider in one of the two regions we normally use for machine jobs. When the provider cannot supply the instance type we request, the service falls back: it requests a different instance type, or a different region, from a list we maintain. A job waits until a machine is available. When any job starts, it asks a job configuration service for its configuration. That service runs as a set of pods, which are small copies of the same program. If too many pods stop, the remaining pods carry more load, and jobs cannot start until the service recovers. These paths depend on three kinds of capacity: the throughput of our scheduling systems, the supply of instances from our cloud provider, and the memory and number of copies of the services that start jobs. A shortfall in any of them makes jobs wait or fail. ## **What Happened** _\(All times UTC\)_ ### **October 1: Docker Gen 2 jobs delayed up to 30 minutes** At 07:44, disk activity on the servers that schedule Gen 2 Docker jobs rose to 90% of capacity and stayed there. At 07:57, the number of Docker jobs waiting to start began to grow, and wait times rose with it. An automated alert paged the on-call engineer at 08:08. We declared an incident at 08:42. At 08:43, engineers identified high disk latency on the scheduling servers. They first believed the disks were too slow for the load, so they prepared a change to a faster disk type. At 09:01, the team agreed on two workstreams: move the servers to faster disks, and add a second Gen 2 cluster so that jobs would split across two clusters. At 09:41, engineers found the root cause. Each scheduling server has fast disks attached directly to it, and the scheduler is meant to keep its working data there. These servers run on a security-hardened operating system image, and on that image the fast disks were not being used. The scheduler wrote its data to slower network-attached disks instead. The script that sets up the fast disks failed without an error, and a gap in logging on these servers hid the failure. Gen 2 traffic had also become increasingly bursty. The slower disks saturated during those bursts, and the scheduler fell behind. Wait times peaked at 09:54, when jobs on the Gen 2 medium resource class waited up to 30 minutes. At 09:55, engineers applied a fix that set up the fast disks correctly. At 10:11, the scheduler began placing jobs faster, which confirmed the servers could keep up with new jobs. Wait times for Gen 2 medium fell to 13 minutes at 10:14, 7 minutes at 10:17, and 1 minute 30 seconds at 10:20. We moved the status page to monitoring at 10:24 and resolved the incident at 10:45. At 11:20, the second Gen 2 cluster began running jobs. ### **October 7, 13:27 to 14:06: Elevated wait times for machine jobs** We declared an incident at 13:27 after seeing elevated queueing and wait times. We posted to the status page at 13:31. At 13:31, engineers saw that we were struggling to acquire virtual machines for machine jobs, and that the number of jobs waiting to start was high mainly for machine jobs. At 13:34, wait times were elevated across executors. At 13:48, wait times began to recover as more machines became available, and we resolved the incident at 14:06. ### **October 7, 14:24 to 15:21: Job failures and workflow delays** Shortly after the previous incident was marked as resolved, we declared a new incident at 14:24 due to a sharp drop in the number of jobs submitted to our execution systems. At 14:26, engineers saw that pods of the job configuration service were restarting. Pods that restart leave fewer copies of the service to answer requests, and jobs that could not retrieve their configuration after repeated attempts failed with an infrastructure error. Workflows that were already running waited for updates from jobs that had not started. At 14:35, engineers increased the amount of available memory to the pods and increased the number of pods, which gave the service more copies to share the load. We also increased the amount of memory available Job submissions began recovering, and the number of messages flowing through our workflow system returned to normal levels. At 14:44, we moved the status page to monitoring. Pods in the job configuration service reached their memory limits and restarted. We increased memory allocations and pod counts, and the service recovered. At 15:13, we increased the number of instances in our Gen 2 Docker clusters so we could work through the backlog of waiting jobs quickly. We resolved the incident at 15:21. ### **October 7, 18:49 to 20:05: Elevated wait times for Docker Gen 2 jobs** We declared a new incident at 18:49 after Docker Gen 2 wait times rose. At 18:51, engineers confirmed that a shortage of available instances kept us from acquiring enough of them, which raised wait times. Wait times differed by resource class size. At 19:52, average wait times were lower than an hour earlier as more capacity became available, though brief spikes continued, and we moved the status page to monitoring. We resolved the incident at 20:05. ### **October 8: Linux machine and remote Docker jobs wait up to 50 minutes** We declared an incident at 12:48 after seeing a large number of jobs waiting to start and long wait times for machine and remote Docker jobs. At 12:49, engineers saw that our cloud provider could not supply enough instances. Delays continued to climb as we investigated. At 13:13, we began examining the logic that chooses a different instance type or region when our first choice is unavailable. At 13:24, we suspected that a defect in that logic was stopping us from using other instance types in our primary region, so we disabled our fallback region and confirmed the cause of the defect. Engineers began working on a fix and deployed it at 14:49. The service began requesting other instance types and started machines in large numbers. At 14:58, the queue of jobs waiting for a machine dropped steadily. Average wait times across resource classes continued to drop over the next hour as we worked through the backlog of waiting jobs. At 16:00, the queue of jobs waiting for a machine cleared and the number of waiting jobs returned to normal. We moved the status page to monitoring at 16:09 and resolved the incident at 16:35. ## **Future Prevention and Process Improvement** We are taking the following steps to strengthen the resilience of job start times, including supply diversification, fallback logic, and earlier detection. **We are expanding our compute regions.** In the October 7 and October 8 incidents, our cloud provider could not supply enough instances in the regions we used. We are adding new regions to our fallback logic in order handle spikes for demand that outpace our preferred regions. **We are fixing the logic that picks a different instance type.** On October 8, a defect kept the machine provisioning service from using other instance types in the same region. We deployed a fix during the incident and are now reviewing and refactoring this logic as a whole, with tests, so that a shortage of one instance type moves work to another. **We are expanding the instance types we offer and securing additional capacity.** We are working with our cloud provider to reserve additional capacity in our existing regions, and we are adding instance types that jobs can run on. Industry demand for high-performance compute is growing. We are diversifying where and how we source capacity so our customers are insulated from supply variation. **We added a second Gen 2 Docker cluster so traffic bursts are distributed across two clusters.** We are also adding alerts on scheduling server disk saturation and on how fast the scheduler places jobs, and an alert that fires when a server starts without its fast disks in use. We made the disk setup script fail with an error, and we restored log shipping from these servers so that a similar problem is visible sooner. Customer experience is our top priority, and we commit to continually improving the reliability of our systems to match the trust that our customers place in us. We thank our customers for their patience while our team worked to resolve these incidents. Please reach out to our support team with any questions or concerns.

There is an increased wait time for jobs on Docker Gen2.

Note: delays will be more noticeable for people running on Docker `xlarge` and above.

We're still seeing spikes in task wait times but on average wait times are looking better for all Docker Gen2.

This incident has been resolved.

## **Summary** Between October 1 and October 8, 2026, CircleCI customers experienced five separate incidents in which jobs waited longer than normal to start, or failed to start. Three of the incidents were driven by limited instance availability from our cloud provider. One was caused by a configuration issue in our scheduling infrastructure, and one by memory limits on an internal service, which we raised during the incident. * **October 1, 07:57 to 10:24 UTC:** Docker Gen 2 jobs waited up to 30 minutes to start. A configuration issue prevented the Gen 2 scheduling servers from using their high-performance local disks. During a burst in traffic, the fallback disks saturated and job placement slowed. * **October 7, 13:27 to 14:06 UTC:** Machine jobs waited longer than normal because we could not acquire enough virtual machines from our cloud provider. * **October 7, 14:24 to 15:21 UTC:** Some jobs failed with an infrastructure error, and workflows were delayed. The service that gives each job its configuration at startup ran out of memory and restarted. * **October 7, 18:49 to 20:05 UTC:** Docker Gen 2 jobs waited longer than normal because we could not acquire enough instances from our cloud provider. * **October 8, 12:48 to 16:00 UTC:** Linux machine jobs and remote Docker jobs waited up to 50 minutes to start. Our cloud provider did not have enough of the instance type we request, and a defect in the logic that picks a different instance type kept us from using alternatives. Jobs that waited during these incidents were delayed, not lost. Customers whose jobs failed with an infrastructure error during the October 7 incident from 14:24 to 15:21 UTC can rerun them. The original status pages can be found below: * [October 1 - Delays starting Gen 2 Docker Jobs](https://status.circleci.com/incidents/22mrv46g6n7g) * [October 7 - Delay on starting Machine Job Tasks](https://status.circleci.com/incidents/tj6wdnfjwr64) * [October 7 - Elevated level of infra fails on customer jobs](https://status.circleci.com/incidents/0z31ldjd8m5v) * [October 7 - Increased task wait times for Docker Gen2](https://status.circleci.com/incidents/39rfrjmk5pvd) * [October 8 - Elevated wait times for machine jobs](https://status.circleci.com/incidents/jn756xtsy4xg) ## **Background** Every job that runs on CircleCI needs compute. Where that compute comes from depends on the executor. Docker jobs run on clusters of servers that schedule work onto a fleet of instances. The scheduler is the software on those servers that decides which instance runs each job. Gen 2 Docker jobs ran on one such cluster at the time of the October 1 incident. Gen 1 Docker jobs are spread across several clusters. Machine jobs, including remote Docker jobs, get a dedicated virtual machine. A remote Docker job is handled as a Linux machine job from submission through provisioning. Our machine provisioning service requests each machine from our cloud provider in one of the two regions we normally use for machine jobs. When the provider cannot supply the instance type we request, the service falls back: it requests a different instance type, or a different region, from a list we maintain. A job waits until a machine is available. When any job starts, it asks a job configuration service for its configuration. That service runs as a set of pods, which are small copies of the same program. If too many pods stop, the remaining pods carry more load, and jobs cannot start until the service recovers. These paths depend on three kinds of capacity: the throughput of our scheduling systems, the supply of instances from our cloud provider, and the memory and number of copies of the services that start jobs. A shortfall in any of them makes jobs wait or fail. ## **What Happened** _\(All times UTC\)_ ### **October 1: Docker Gen 2 jobs delayed up to 30 minutes** At 07:44, disk activity on the servers that schedule Gen 2 Docker jobs rose to 90% of capacity and stayed there. At 07:57, the number of Docker jobs waiting to start began to grow, and wait times rose with it. An automated alert paged the on-call engineer at 08:08. We declared an incident at 08:42. At 08:43, engineers identified high disk latency on the scheduling servers. They first believed the disks were too slow for the load, so they prepared a change to a faster disk type. At 09:01, the team agreed on two workstreams: move the servers to faster disks, and add a second Gen 2 cluster so that jobs would split across two clusters. At 09:41, engineers found the root cause. Each scheduling server has fast disks attached directly to it, and the scheduler is meant to keep its working data there. These servers run on a security-hardened operating system image, and on that image the fast disks were not being used. The scheduler wrote its data to slower network-attached disks instead. The script that sets up the fast disks failed without an error, and a gap in logging on these servers hid the failure. Gen 2 traffic had also become increasingly bursty. The slower disks saturated during those bursts, and the scheduler fell behind. Wait times peaked at 09:54, when jobs on the Gen 2 medium resource class waited up to 30 minutes. At 09:55, engineers applied a fix that set up the fast disks correctly. At 10:11, the scheduler began placing jobs faster, which confirmed the servers could keep up with new jobs. Wait times for Gen 2 medium fell to 13 minutes at 10:14, 7 minutes at 10:17, and 1 minute 30 seconds at 10:20. We moved the status page to monitoring at 10:24 and resolved the incident at 10:45. At 11:20, the second Gen 2 cluster began running jobs. ### **October 7, 13:27 to 14:06: Elevated wait times for machine jobs** We declared an incident at 13:27 after seeing elevated queueing and wait times. We posted to the status page at 13:31. At 13:31, engineers saw that we were struggling to acquire virtual machines for machine jobs, and that the number of jobs waiting to start was high mainly for machine jobs. At 13:34, wait times were elevated across executors. At 13:48, wait times began to recover as more machines became available, and we resolved the incident at 14:06. ### **October 7, 14:24 to 15:21: Job failures and workflow delays** Shortly after the previous incident was marked as resolved, we declared a new incident at 14:24 due to a sharp drop in the number of jobs submitted to our execution systems. At 14:26, engineers saw that pods of the job configuration service were restarting. Pods that restart leave fewer copies of the service to answer requests, and jobs that could not retrieve their configuration after repeated attempts failed with an infrastructure error. Workflows that were already running waited for updates from jobs that had not started. At 14:35, engineers increased the amount of available memory to the pods and increased the number of pods, which gave the service more copies to share the load. We also increased the amount of memory available Job submissions began recovering, and the number of messages flowing through our workflow system returned to normal levels. At 14:44, we moved the status page to monitoring. Pods in the job configuration service reached their memory limits and restarted. We increased memory allocations and pod counts, and the service recovered. At 15:13, we increased the number of instances in our Gen 2 Docker clusters so we could work through the backlog of waiting jobs quickly. We resolved the incident at 15:21. ### **October 7, 18:49 to 20:05: Elevated wait times for Docker Gen 2 jobs** We declared a new incident at 18:49 after Docker Gen 2 wait times rose. At 18:51, engineers confirmed that a shortage of available instances kept us from acquiring enough of them, which raised wait times. Wait times differed by resource class size. At 19:52, average wait times were lower than an hour earlier as more capacity became available, though brief spikes continued, and we moved the status page to monitoring. We resolved the incident at 20:05. ### **October 8: Linux machine and remote Docker jobs wait up to 50 minutes** We declared an incident at 12:48 after seeing a large number of jobs waiting to start and long wait times for machine and remote Docker jobs. At 12:49, engineers saw that our cloud provider could not supply enough instances. Delays continued to climb as we investigated. At 13:13, we began examining the logic that chooses a different instance type or region when our first choice is unavailable. At 13:24, we suspected that a defect in that logic was stopping us from using other instance types in our primary region, so we disabled our fallback region and confirmed the cause of the defect. Engineers began working on a fix and deployed it at 14:49. The service began requesting other instance types and started machines in large numbers. At 14:58, the queue of jobs waiting for a machine dropped steadily. Average wait times across resource classes continued to drop over the next hour as we worked through the backlog of waiting jobs. At 16:00, the queue of jobs waiting for a machine cleared and the number of waiting jobs returned to normal. We moved the status page to monitoring at 16:09 and resolved the incident at 16:35. ## **Future Prevention and Process Improvement** We are taking the following steps to strengthen the resilience of job start times, including supply diversification, fallback logic, and earlier detection. **We are expanding our compute regions.** In the October 7 and October 8 incidents, our cloud provider could not supply enough instances in the regions we used. We are adding new regions to our fallback logic in order handle spikes for demand that outpace our preferred regions. **We are fixing the logic that picks a different instance type.** On October 8, a defect kept the machine provisioning service from using other instance types in the same region. We deployed a fix during the incident and are now reviewing and refactoring this logic as a whole, with tests, so that a shortage of one instance type moves work to another. **We are expanding the instance types we offer and securing additional capacity.** We are working with our cloud provider to reserve additional capacity in our existing regions, and we are adding instance types that jobs can run on. Industry demand for high-performance compute is growing. We are diversifying where and how we source capacity so our customers are insulated from supply variation. **We added a second Gen 2 Docker cluster so traffic bursts are distributed across two clusters.** We are also adding alerts on scheduling server disk saturation and on how fast the scheduler places jobs, and an alert that fires when a server starts without its fast disks in use. We made the disk setup script fail with an error, and we restored log shipping from these servers so that a similar problem is visible sooner. Customer experience is our top priority, and we commit to continually improving the reliability of our systems to match the trust that our customers place in us. We thank our customers for their patience while our team worked to resolve these incidents. Please reach out to our support team with any questions or concerns.

We are investigating a rise in infra fails for customer jobs.

We're seeing signs of recovery. Docker Gen2 wait times are slightly elevated and we're monitoring.

This incident has been resolved.

## **Summary** Between October 1 and October 8, 2026, CircleCI customers experienced five separate incidents in which jobs waited longer than normal to start, or failed to start. Three of the incidents were driven by limited instance availability from our cloud provider. One was caused by a configuration issue in our scheduling infrastructure, and one by memory limits on an internal service, which we raised during the incident. * **October 1, 07:57 to 10:24 UTC:** Docker Gen 2 jobs waited up to 30 minutes to start. A configuration issue prevented the Gen 2 scheduling servers from using their high-performance local disks. During a burst in traffic, the fallback disks saturated and job placement slowed. * **October 7, 13:27 to 14:06 UTC:** Machine jobs waited longer than normal because we could not acquire enough virtual machines from our cloud provider. * **October 7, 14:24 to 15:21 UTC:** Some jobs failed with an infrastructure error, and workflows were delayed. The service that gives each job its configuration at startup ran out of memory and restarted. * **October 7, 18:49 to 20:05 UTC:** Docker Gen 2 jobs waited longer than normal because we could not acquire enough instances from our cloud provider. * **October 8, 12:48 to 16:00 UTC:** Linux machine jobs and remote Docker jobs waited up to 50 minutes to start. Our cloud provider did not have enough of the instance type we request, and a defect in the logic that picks a different instance type kept us from using alternatives. Jobs that waited during these incidents were delayed, not lost. Customers whose jobs failed with an infrastructure error during the October 7 incident from 14:24 to 15:21 UTC can rerun them. The original status pages can be found below: * [October 1 - Delays starting Gen 2 Docker Jobs](https://status.circleci.com/incidents/22mrv46g6n7g) * [October 7 - Delay on starting Machine Job Tasks](https://status.circleci.com/incidents/tj6wdnfjwr64) * [October 7 - Elevated level of infra fails on customer jobs](https://status.circleci.com/incidents/0z31ldjd8m5v) * [October 7 - Increased task wait times for Docker Gen2](https://status.circleci.com/incidents/39rfrjmk5pvd) * [October 8 - Elevated wait times for machine jobs](https://status.circleci.com/incidents/jn756xtsy4xg) ## **Background** Every job that runs on CircleCI needs compute. Where that compute comes from depends on the executor. Docker jobs run on clusters of servers that schedule work onto a fleet of instances. The scheduler is the software on those servers that decides which instance runs each job. Gen 2 Docker jobs ran on one such cluster at the time of the October 1 incident. Gen 1 Docker jobs are spread across several clusters. Machine jobs, including remote Docker jobs, get a dedicated virtual machine. A remote Docker job is handled as a Linux machine job from submission through provisioning. Our machine provisioning service requests each machine from our cloud provider in one of the two regions we normally use for machine jobs. When the provider cannot supply the instance type we request, the service falls back: it requests a different instance type, or a different region, from a list we maintain. A job waits until a machine is available. When any job starts, it asks a job configuration service for its configuration. That service runs as a set of pods, which are small copies of the same program. If too many pods stop, the remaining pods carry more load, and jobs cannot start until the service recovers. These paths depend on three kinds of capacity: the throughput of our scheduling systems, the supply of instances from our cloud provider, and the memory and number of copies of the services that start jobs. A shortfall in any of them makes jobs wait or fail. ## **What Happened** _\(All times UTC\)_ ### **October 1: Docker Gen 2 jobs delayed up to 30 minutes** At 07:44, disk activity on the servers that schedule Gen 2 Docker jobs rose to 90% of capacity and stayed there. At 07:57, the number of Docker jobs waiting to start began to grow, and wait times rose with it. An automated alert paged the on-call engineer at 08:08. We declared an incident at 08:42. At 08:43, engineers identified high disk latency on the scheduling servers. They first believed the disks were too slow for the load, so they prepared a change to a faster disk type. At 09:01, the team agreed on two workstreams: move the servers to faster disks, and add a second Gen 2 cluster so that jobs would split across two clusters. At 09:41, engineers found the root cause. Each scheduling server has fast disks attached directly to it, and the scheduler is meant to keep its working data there. These servers run on a security-hardened operating system image, and on that image the fast disks were not being used. The scheduler wrote its data to slower network-attached disks instead. The script that sets up the fast disks failed without an error, and a gap in logging on these servers hid the failure. Gen 2 traffic had also become increasingly bursty. The slower disks saturated during those bursts, and the scheduler fell behind. Wait times peaked at 09:54, when jobs on the Gen 2 medium resource class waited up to 30 minutes. At 09:55, engineers applied a fix that set up the fast disks correctly. At 10:11, the scheduler began placing jobs faster, which confirmed the servers could keep up with new jobs. Wait times for Gen 2 medium fell to 13 minutes at 10:14, 7 minutes at 10:17, and 1 minute 30 seconds at 10:20. We moved the status page to monitoring at 10:24 and resolved the incident at 10:45. At 11:20, the second Gen 2 cluster began running jobs. ### **October 7, 13:27 to 14:06: Elevated wait times for machine jobs** We declared an incident at 13:27 after seeing elevated queueing and wait times. We posted to the status page at 13:31. At 13:31, engineers saw that we were struggling to acquire virtual machines for machine jobs, and that the number of jobs waiting to start was high mainly for machine jobs. At 13:34, wait times were elevated across executors. At 13:48, wait times began to recover as more machines became available, and we resolved the incident at 14:06. ### **October 7, 14:24 to 15:21: Job failures and workflow delays** Shortly after the previous incident was marked as resolved, we declared a new incident at 14:24 due to a sharp drop in the number of jobs submitted to our execution systems. At 14:26, engineers saw that pods of the job configuration service were restarting. Pods that restart leave fewer copies of the service to answer requests, and jobs that could not retrieve their configuration after repeated attempts failed with an infrastructure error. Workflows that were already running waited for updates from jobs that had not started. At 14:35, engineers increased the amount of available memory to the pods and increased the number of pods, which gave the service more copies to share the load. We also increased the amount of memory available Job submissions began recovering, and the number of messages flowing through our workflow system returned to normal levels. At 14:44, we moved the status page to monitoring. Pods in the job configuration service reached their memory limits and restarted. We increased memory allocations and pod counts, and the service recovered. At 15:13, we increased the number of instances in our Gen 2 Docker clusters so we could work through the backlog of waiting jobs quickly. We resolved the incident at 15:21. ### **October 7, 18:49 to 20:05: Elevated wait times for Docker Gen 2 jobs** We declared a new incident at 18:49 after Docker Gen 2 wait times rose. At 18:51, engineers confirmed that a shortage of available instances kept us from acquiring enough of them, which raised wait times. Wait times differed by resource class size. At 19:52, average wait times were lower than an hour earlier as more capacity became available, though brief spikes continued, and we moved the status page to monitoring. We resolved the incident at 20:05. ### **October 8: Linux machine and remote Docker jobs wait up to 50 minutes** We declared an incident at 12:48 after seeing a large number of jobs waiting to start and long wait times for machine and remote Docker jobs. At 12:49, engineers saw that our cloud provider could not supply enough instances. Delays continued to climb as we investigated. At 13:13, we began examining the logic that chooses a different instance type or region when our first choice is unavailable. At 13:24, we suspected that a defect in that logic was stopping us from using other instance types in our primary region, so we disabled our fallback region and confirmed the cause of the defect. Engineers began working on a fix and deployed it at 14:49. The service began requesting other instance types and started machines in large numbers. At 14:58, the queue of jobs waiting for a machine dropped steadily. Average wait times across resource classes continued to drop over the next hour as we worked through the backlog of waiting jobs. At 16:00, the queue of jobs waiting for a machine cleared and the number of waiting jobs returned to normal. We moved the status page to monitoring at 16:09 and resolved the incident at 16:35. ## **Future Prevention and Process Improvement** We are taking the following steps to strengthen the resilience of job start times, including supply diversification, fallback logic, and earlier detection. **We are expanding our compute regions.** In the October 7 and October 8 incidents, our cloud provider could not supply enough instances in the regions we used. We are adding new regions to our fallback logic in order handle spikes for demand that outpace our preferred regions. **We are fixing the logic that picks a different instance type.** On October 8, a defect kept the machine provisioning service from using other instance types in the same region. We deployed a fix during the incident and are now reviewing and refactoring this logic as a whole, with tests, so that a shortage of one instance type moves work to another. **We are expanding the instance types we offer and securing additional capacity.** We are working with our cloud provider to reserve additional capacity in our existing regions, and we are adding instance types that jobs can run on. Industry demand for high-performance compute is growing. We are diversifying where and how we source capacity so our customers are insulated from supply variation. **We added a second Gen 2 Docker cluster so traffic bursts are distributed across two clusters.** We are also adding alerts on scheduling server disk saturation and on how fast the scheduler places jobs, and an alert that fires when a server starts without its fast disks in use. We made the disk setup script fail with an error, and we restored log shipping from these servers so that a similar problem is visible sooner. Customer experience is our top priority, and we commit to continually improving the reliability of our systems to match the trust that our customers place in us. We thank our customers for their patience while our team worked to resolve these incidents. Please reach out to our support team with any questions or concerns.

Customers may be experiencing elevated wait times for Machine Job Tasks, we are investigating the root cause and will update when we know more.

We are seeing signs of recovery and monitoring the situation.

The incident has been resolved. Thank you for your patience

## **Summary** Between October 1 and October 8, 2026, CircleCI customers experienced five separate incidents in which jobs waited longer than normal to start, or failed to start. Three of the incidents were driven by limited instance availability from our cloud provider. One was caused by a configuration issue in our scheduling infrastructure, and one by memory limits on an internal service, which we raised during the incident. * **October 1, 07:57 to 10:24 UTC:** Docker Gen 2 jobs waited up to 30 minutes to start. A configuration issue prevented the Gen 2 scheduling servers from using their high-performance local disks. During a burst in traffic, the fallback disks saturated and job placement slowed. * **October 7, 13:27 to 14:06 UTC:** Machine jobs waited longer than normal because we could not acquire enough virtual machines from our cloud provider. * **October 7, 14:24 to 15:21 UTC:** Some jobs failed with an infrastructure error, and workflows were delayed. The service that gives each job its configuration at startup ran out of memory and restarted. * **October 7, 18:49 to 20:05 UTC:** Docker Gen 2 jobs waited longer than normal because we could not acquire enough instances from our cloud provider. * **October 8, 12:48 to 16:00 UTC:** Linux machine jobs and remote Docker jobs waited up to 50 minutes to start. Our cloud provider did not have enough of the instance type we request, and a defect in the logic that picks a different instance type kept us from using alternatives. Jobs that waited during these incidents were delayed, not lost. Customers whose jobs failed with an infrastructure error during the October 7 incident from 14:24 to 15:21 UTC can rerun them. The original status pages can be found below: * [October 1 - Delays starting Gen 2 Docker Jobs](https://status.circleci.com/incidents/22mrv46g6n7g) * [October 7 - Delay on starting Machine Job Tasks](https://status.circleci.com/incidents/tj6wdnfjwr64) * [October 7 - Elevated level of infra fails on customer jobs](https://status.circleci.com/incidents/0z31ldjd8m5v) * [October 7 - Increased task wait times for Docker Gen2](https://status.circleci.com/incidents/39rfrjmk5pvd) * [October 8 - Elevated wait times for machine jobs](https://status.circleci.com/incidents/jn756xtsy4xg) ## **Background** Every job that runs on CircleCI needs compute. Where that compute comes from depends on the executor. Docker jobs run on clusters of servers that schedule work onto a fleet of instances. The scheduler is the software on those servers that decides which instance runs each job. Gen 2 Docker jobs ran on one such cluster at the time of the October 1 incident. Gen 1 Docker jobs are spread across several clusters. Machine jobs, including remote Docker jobs, get a dedicated virtual machine. A remote Docker job is handled as a Linux machine job from submission through provisioning. Our machine provisioning service requests each machine from our cloud provider in one of the two regions we normally use for machine jobs. When the provider cannot supply the instance type we request, the service falls back: it requests a different instance type, or a different region, from a list we maintain. A job waits until a machine is available. When any job starts, it asks a job configuration service for its configuration. That service runs as a set of pods, which are small copies of the same program. If too many pods stop, the remaining pods carry more load, and jobs cannot start until the service recovers. These paths depend on three kinds of capacity: the throughput of our scheduling systems, the supply of instances from our cloud provider, and the memory and number of copies of the services that start jobs. A shortfall in any of them makes jobs wait or fail. ## **What Happened** _\(All times UTC\)_ ### **October 1: Docker Gen 2 jobs delayed up to 30 minutes** At 07:44, disk activity on the servers that schedule Gen 2 Docker jobs rose to 90% of capacity and stayed there. At 07:57, the number of Docker jobs waiting to start began to grow, and wait times rose with it. An automated alert paged the on-call engineer at 08:08. We declared an incident at 08:42. At 08:43, engineers identified high disk latency on the scheduling servers. They first believed the disks were too slow for the load, so they prepared a change to a faster disk type. At 09:01, the team agreed on two workstreams: move the servers to faster disks, and add a second Gen 2 cluster so that jobs would split across two clusters. At 09:41, engineers found the root cause. Each scheduling server has fast disks attached directly to it, and the scheduler is meant to keep its working data there. These servers run on a security-hardened operating system image, and on that image the fast disks were not being used. The scheduler wrote its data to slower network-attached disks instead. The script that sets up the fast disks failed without an error, and a gap in logging on these servers hid the failure. Gen 2 traffic had also become increasingly bursty. The slower disks saturated during those bursts, and the scheduler fell behind. Wait times peaked at 09:54, when jobs on the Gen 2 medium resource class waited up to 30 minutes. At 09:55, engineers applied a fix that set up the fast disks correctly. At 10:11, the scheduler began placing jobs faster, which confirmed the servers could keep up with new jobs. Wait times for Gen 2 medium fell to 13 minutes at 10:14, 7 minutes at 10:17, and 1 minute 30 seconds at 10:20. We moved the status page to monitoring at 10:24 and resolved the incident at 10:45. At 11:20, the second Gen 2 cluster began running jobs. ### **October 7, 13:27 to 14:06: Elevated wait times for machine jobs** We declared an incident at 13:27 after seeing elevated queueing and wait times. We posted to the status page at 13:31. At 13:31, engineers saw that we were struggling to acquire virtual machines for machine jobs, and that the number of jobs waiting to start was high mainly for machine jobs. At 13:34, wait times were elevated across executors. At 13:48, wait times began to recover as more machines became available, and we resolved the incident at 14:06. ### **October 7, 14:24 to 15:21: Job failures and workflow delays** Shortly after the previous incident was marked as resolved, we declared a new incident at 14:24 due to a sharp drop in the number of jobs submitted to our execution systems. At 14:26, engineers saw that pods of the job configuration service were restarting. Pods that restart leave fewer copies of the service to answer requests, and jobs that could not retrieve their configuration after repeated attempts failed with an infrastructure error. Workflows that were already running waited for updates from jobs that had not started. At 14:35, engineers increased the amount of available memory to the pods and increased the number of pods, which gave the service more copies to share the load. We also increased the amount of memory available Job submissions began recovering, and the number of messages flowing through our workflow system returned to normal levels. At 14:44, we moved the status page to monitoring. Pods in the job configuration service reached their memory limits and restarted. We increased memory allocations and pod counts, and the service recovered. At 15:13, we increased the number of instances in our Gen 2 Docker clusters so we could work through the backlog of waiting jobs quickly. We resolved the incident at 15:21. ### **October 7, 18:49 to 20:05: Elevated wait times for Docker Gen 2 jobs** We declared a new incident at 18:49 after Docker Gen 2 wait times rose. At 18:51, engineers confirmed that a shortage of available instances kept us from acquiring enough of them, which raised wait times. Wait times differed by resource class size. At 19:52, average wait times were lower than an hour earlier as more capacity became available, though brief spikes continued, and we moved the status page to monitoring. We resolved the incident at 20:05. ### **October 8: Linux machine and remote Docker jobs wait up to 50 minutes** We declared an incident at 12:48 after seeing a large number of jobs waiting to start and long wait times for machine and remote Docker jobs. At 12:49, engineers saw that our cloud provider could not supply enough instances. Delays continued to climb as we investigated. At 13:13, we began examining the logic that chooses a different instance type or region when our first choice is unavailable. At 13:24, we suspected that a defect in that logic was stopping us from using other instance types in our primary region, so we disabled our fallback region and confirmed the cause of the defect. Engineers began working on a fix and deployed it at 14:49. The service began requesting other instance types and started machines in large numbers. At 14:58, the queue of jobs waiting for a machine dropped steadily. Average wait times across resource classes continued to drop over the next hour as we worked through the backlog of waiting jobs. At 16:00, the queue of jobs waiting for a machine cleared and the number of waiting jobs returned to normal. We moved the status page to monitoring at 16:09 and resolved the incident at 16:35. ## **Future Prevention and Process Improvement** We are taking the following steps to strengthen the resilience of job start times, including supply diversification, fallback logic, and earlier detection. **We are expanding our compute regions.** In the October 7 and October 8 incidents, our cloud provider could not supply enough instances in the regions we used. We are adding new regions to our fallback logic in order handle spikes for demand that outpace our preferred regions. **We are fixing the logic that picks a different instance type.** On October 8, a defect kept the machine provisioning service from using other instance types in the same region. We deployed a fix during the incident and are now reviewing and refactoring this logic as a whole, with tests, so that a shortage of one instance type moves work to another. **We are expanding the instance types we offer and securing additional capacity.** We are working with our cloud provider to reserve additional capacity in our existing regions, and we are adding instance types that jobs can run on. Industry demand for high-performance compute is growing. We are diversifying where and how we source capacity so our customers are insulated from supply variation. **We added a second Gen 2 Docker cluster so traffic bursts are distributed across two clusters.** We are also adding alerts on scheduling server disk saturation and on how fast the scheduler places jobs, and an alert that fires when a server starts without its fast disks in use. We made the disk setup script fail with an error, and we restored log shipping from these servers so that a similar problem is visible sooner. Customer experience is our top priority, and we commit to continually improving the reliability of our systems to match the trust that our customers place in us. We thank our customers for their patience while our team worked to resolve these incidents. Please reach out to our support team with any questions or concerns.

What's impacted Customers using CircleCI pipelines and workflows, including UI data display, outbound notifications, and outbound webhooks. What can you expect Delays in pipelines being created and in workflows and jobs starting. Job, workflow, and pipeline data may take up to 15 minutes to appear in the UI. Outbound notifications and webhooks are delayed by up to 1 minute. Next update We will provide another update within 30 minutes or as we have more information to share.

Since 15:47 UTC, customers may also see failed jobs, in addition to the delays previously reported. Our engineers are continuing to investigate and mitigate the issue.

As of 16:02 UTC, pipelines are failing entirely, so no new workflows or jobs are running at this point. This is in addition to the delays and failed jobs reported earlier. Our engineers are continuing to work on mitigating the issue, and we will share more information as soon as we have it.

As of 16:39 UTC, we are starting to see recovery for new pipelines and workflows being submitted, and jobs are beginning to run again. Our engineers are continuing to work on the recovery and will share more information as soon as we have it.

While Pipelines, workflows, and jobs are running again, customers may continue to see longer than usual delays between jobs in a workflow, and outbound notifications are delayed by approximately 1 minute. Notifications and status updates to your version control system are beginning to go out again. Our engineers are continuing to work toward a full recovery.

Customers continue to experience longer than usual delays between jobs in a workflow, as well as delays in outbound notifications. We thank you for your patience while our engineers continue to investigate and work on mitigating the issue. We will share another update within the next 30 minutes, or sooner if more information becomes available.

Customers continue to experience longer than usual delays before workflows and jobs start, in some cases up to approximately 30 minutes. This is improved from earlier in the incident, but delays remain. Delays in outbound notifications have improved. We thank you for your patience while our engineers continue to work toward restoring normal job start times. We will share another update within the next 30 minutes, or sooner if more information becomes available.

Customers continue to experience delays. Most are now starting within approximately 20 minutes, improved from approximately 40 minutes earlier in the incident. A very small number of workflows may have waited more than 1 hour. Our engineers have made changes that have improved service stability, and the remaining delay is due to a backlog of work that built up during the incident. The backlog is decreasing, and we are focused on clearing it faster. We will share more information as progress continues. We thank you for your patience while our engineers work through the backlog. We will share another update within the next 30 minutes, or sooner if more information becomes available.

Delays for workflows and jobs to start are continuing to decrease. Most are now starting within approximately 10 minutes, improved from approximately 40 minutes earlier in the incident. A very small number of workflows may have waited more than 1 hour. If your job failed to start, please rerun the job. Delays in outbound notifications have improved. The remaining delay is due to a backlog of work that built up during the incident, and that backlog has reduced by more than half. We expect the backlog to be cleared in approximately 15 minutes. We will share another update once it has cleared, or within the next 30 minutes at the latest.

The backlog of delayed work has been cleared, and workflows are starting again with no delays in notifications. However, customers using Docker and Linux jobs across multiple resource classes, including medium, large, and 2xlarge gen2, may now see jobs waiting in the queue for few minutes before they start. Our engineers are continuing to work on mitigating this issue. If your job failed to start, please rerun the job. We thank you for your patience while our engineers work on this. We will share another update within the next 30 minutes, or sooner if more information becomes available.

Workflows and jobs are starting normally, and wait times for Docker and Linux jobs have returned to normal. Our engineers are continuing to monitor this issue. If your job failed to start, please rerun the job. We thank you for your patience while our engineers work on this. We will share another update within the next 30 minutes, or sooner if more information becomes available.

Between approximately 15:30 UTC and 20:30 UTC on October 6, 2026, customers experienced delays and failures affecting pipelines, workflows, and jobs. From approximately 16:02 UTC to 16:39 UTC, pipelines were failing and no new workflows or jobs could start. After service resumed, workflows and jobs continued to start with delays until approximately 20:00 UTC. Start times averaged up to approximately 40 minutes at their peak, and a very small number of workflows waited more than 1 hour. Outbound notifications and webhooks were also delayed until approximately 19:00 UTC, typically by about a minute. As the backlog of delayed work cleared, the resulting increase in demand placed pressure on our execution capacity, causing further delays for Docker and Linux jobs. From approximately 19:45 UTC to 20:30 UTC, these jobs waited in the queue longer than usual before starting. At the peak, around 20:05 UTC, waits reached up to approximately 8 minutes for 2xlarge gen2, 6 minutes for xlarge gen2, and less than 5 minutes for a few other resource classes. The issue has been resolved, and all affected functionality has returned to normal. Customers whose jobs failed during this window may rerun affected jobs. We thank you for your patience while our team worked on implementing a fix.

## Summary On October 6, 2026, from 15:30 to 20:30 UTC, CircleCI customers experienced delays creating pipelines and starting workflows and jobs. From 15:48 to 16:38 UTC, no workflows or jobs started, and pipelines triggered from 16:02 UTC failed. Shorter, intermittent delays began earlier, at 13:30 UTC. Data in the CircleCI UI, notifications, and commit status updates to version control providers were also delayed. The incident started in the database behind our workflow orchestration service, which coordinates every pipeline, workflow and job on CircleCI. Routine maintenance on the database's largest tables wrote transaction logs faster than the database could archive them, and the logs filled the storage volume set aside for them. To keep the database from becoming read-only, our engineers moved the logs to the database's main storage volume. That move took the database offline for 50 minutes. When the database came back, it processed work at about half its normal rate until our engineers changed a database setting at 18:19 UTC. We then worked through the backlog, and job start times returned to normal by 20:30 UTC. Customers whose jobs failed during this window may rerun them. The original status page can be found [here](https://status.circleci.com/incidents/z4hbq8h49j8c). ## Background When you trigger a pipeline, CircleCI hands it to a workflow orchestration service. That service tracks the state of every workflow and job, decides when each job is ready to run, and passes ready jobs to our execution fleet. It also drives the job data you see in the UI and the notifications and commit statuses we send when work finishes. The orchestration service stores its state in a database. The database writes every change to a transaction log before applying it, and it keeps those logs on a dedicated storage volume. It archives the logs continuously so it can reuse that space. If archiving falls behind and the volume fills, the database stops accepting writes, and no new work can move forward. The database also runs a periodic maintenance process \(autovacuums\) on each table to keep its internal records valid over time. On a very large table, this process reads every page of the table and writes a large volume of transaction logs. ## What Happened _\(All times UTC\)_ From 10:02 on October 6, our monitoring raised short-lived alerts about errors in the orchestration service. Each alert cleared on its own within about 15 minutes. Our engineers had seen similar brief alerts before, and the system showed no other signs of stress, so they followed the runbook each time, and each alert cleared on its own partway through and also started work to make the alerts less sensitive. Around 12:40, along with the regular workload which generates its own transaction logs, the maintenance process on several of the database's largest tables began writing additional transaction logs faster than the database could archive them, and the log volume started to fill. At 13:30, the orchestration service began to slow down. Through 15:30, some pipelines and jobs took longer to start, in short bursts. At 13:35, our monitoring alerted again, this time alongside related alerts from several other services. Infrastructure Engineers followed the alert runbooks, clearing up some of the alerts. The alerts returned again at around 14:30 and after investigating further, we declared an incident at 14:54. We should have recognized the combination of alerts sooner, and we have covered how we are fixing that below. After declaring the incident, our engineers traced the slowdown to the maintenance process \(anti-wraparound autovacuum\). The database restarts this process automatically if it is cancelled, so our engineers changed its settings to help it finish faster. By 15:30, the delays were continuous, and some jobs began to fail. At 15:43, the transaction log volume was close to full. If it filled, the database would stop accepting writes and no work could run. Our engineers decided to move the transaction logs onto the database's main storage volume, which had plenty of free space. The move required the database to go offline, and we could not predict in advance how long that would take. The move started at 15:47. From 15:48 to 16:38, the orchestration service could not process any work. Customers could still trigger pipelines, but no workflows or jobs started, and from 16:02 newly triggered pipelines failed. We posted to our status page at 15:58 and raised it to a major outage at 16:20. While the database was offline, our engineers prepared a replacement database as a fallback. The original database came back first, so we kept it in service. At 16:38, the database came back online and work started flowing again. With transaction logs and regular data now sharing the same storage, the database ran more slowly than before. From 16:38 to 18:19, the orchestration service processed work at about half its normal rate, and jobs waited tens of minutes to start, up to about 50 minutes at the longest. During that time, the database had to finish archiving its backlog of transaction logs before we could move them back to a dedicated volume. We also investigated and implemented several database parameter changes to stop further maintenance runs from starting and prepared ways to reduce the work reaching the service. Along with the replacement database, we also prepared a complete stack of the service during that time so that we could move to it at the risk of data loss and opted against that move. At 18:19, our engineers changed a database setting to cut the time each write spent waiting on storage. Processing speed recovered, and the backlog began to clear. To clear it faster, we added database and server capacity to the systems that hand jobs to our execution fleet. By about 19:55, new jobs were again starting on time. The backlog then reached our execution fleet. From 19:49 to 20:30, some Docker jobs on larger resource classes waited up to about 10 minutes to start while capacity scaled up. By 20:30, job start times were back to normal. We moved the status page to monitoring at 20:32 and resolved the incident at 21:19. About 3,500 jobs failed to start during the incident. Customers may rerun these jobs. We found no evidence that CircleCI ran any job more than once. Customers who reran a workflow while the original run was still delayed may have seen both runs complete. Annual plan customers can work with their account team to review usage. ## Future Prevention and Process Improvement We are taking the following steps to prevent a recurrence and improve our response time: **We are adding alerts on the conditions that caused this incident.** Our monitoring caught the slowdown, but we had no alert on how full the transaction log volume was or on how far archiving had fallen behind. We are adding alerts on both, along with alerts on the database maintenance that drives them, so we can act earlier. We are also revisiting all our alerts to ensure noisy or sensitive alerts don’t hide real issues. **We are tuning how maintenance process \(autovacuum\) runs on our database.** We are looking at the frequency and the aggressiveness with which the autovacuums run on our database. We are also tracking the size of these tables over time so they stay within safe limits. **We are removing the single transaction log bottleneck.** All of the orchestration service's writes currently go through one database and one transaction log, so when that log fell behind, every pipeline, workflow and job slowed down with it. We are evaluating two approaches: splitting the service across multiple database instances, each with its own transaction log, and moving it to a distributed database built to spread writes across many nodes. Either approach would limit the effect of a backlog to part of the workload. **We are making our systems wait for the orchestration service to recover.** During the incident, some jobs failed because our systems gave up after retrying. We are working on changing that behavior. **We are improving our status page updates during long incidents.** Customers told us our early updates did not give enough detail. We are updating our guidance so updates include specific impact and timing sooner. ‌ Customer experience is our top priority, and we commit to continually improving the reliability of our systems to match the trust that our customers place in us. We thank our customers for their patience while our team worked to resolve this incident. Please reach out to our support team with any questions or concerns.

Customers may experience delays in starting Docker Gen 2 Jobs. We are working to increase the throughput of the system to accomodate this.

We have identified the root cause and we are actively working on a fix. Thank you for your patience.

A fix has been rolled out and we're starting to observe recovery.

We're seeing queue times return to normal levels, and continue to monitor.

This incident has been resolved.

## **Summary** Between October 1 and October 8, 2026, CircleCI customers experienced five separate incidents in which jobs waited longer than normal to start, or failed to start. Three of the incidents were driven by limited instance availability from our cloud provider. One was caused by a configuration issue in our scheduling infrastructure, and one by memory limits on an internal service, which we raised during the incident. * **October 1, 07:57 to 10:24 UTC:** Docker Gen 2 jobs waited up to 30 minutes to start. A configuration issue prevented the Gen 2 scheduling servers from using their high-performance local disks. During a burst in traffic, the fallback disks saturated and job placement slowed. * **October 7, 13:27 to 14:06 UTC:** Machine jobs waited longer than normal because we could not acquire enough virtual machines from our cloud provider. * **October 7, 14:24 to 15:21 UTC:** Some jobs failed with an infrastructure error, and workflows were delayed. The service that gives each job its configuration at startup ran out of memory and restarted. * **October 7, 18:49 to 20:05 UTC:** Docker Gen 2 jobs waited longer than normal because we could not acquire enough instances from our cloud provider. * **October 8, 12:48 to 16:00 UTC:** Linux machine jobs and remote Docker jobs waited up to 50 minutes to start. Our cloud provider did not have enough of the instance type we request, and a defect in the logic that picks a different instance type kept us from using alternatives. Jobs that waited during these incidents were delayed, not lost. Customers whose jobs failed with an infrastructure error during the October 7 incident from 14:24 to 15:21 UTC can rerun them. The original status pages can be found below: * [October 1 - Delays starting Gen 2 Docker Jobs](https://status.circleci.com/incidents/22mrv46g6n7g) * [October 7 - Delay on starting Machine Job Tasks](https://status.circleci.com/incidents/tj6wdnfjwr64) * [October 7 - Elevated level of infra fails on customer jobs](https://status.circleci.com/incidents/0z31ldjd8m5v) * [October 7 - Increased task wait times for Docker Gen2](https://status.circleci.com/incidents/39rfrjmk5pvd) * [October 8 - Elevated wait times for machine jobs](https://status.circleci.com/incidents/jn756xtsy4xg) ## **Background** Every job that runs on CircleCI needs compute. Where that compute comes from depends on the executor. Docker jobs run on clusters of servers that schedule work onto a fleet of instances. The scheduler is the software on those servers that decides which instance runs each job. Gen 2 Docker jobs ran on one such cluster at the time of the October 1 incident. Gen 1 Docker jobs are spread across several clusters. Machine jobs, including remote Docker jobs, get a dedicated virtual machine. A remote Docker job is handled as a Linux machine job from submission through provisioning. Our machine provisioning service requests each machine from our cloud provider in one of the two regions we normally use for machine jobs. When the provider cannot supply the instance type we request, the service falls back: it requests a different instance type, or a different region, from a list we maintain. A job waits until a machine is available. When any job starts, it asks a job configuration service for its configuration. That service runs as a set of pods, which are small copies of the same program. If too many pods stop, the remaining pods carry more load, and jobs cannot start until the service recovers. These paths depend on three kinds of capacity: the throughput of our scheduling systems, the supply of instances from our cloud provider, and the memory and number of copies of the services that start jobs. A shortfall in any of them makes jobs wait or fail. ## **What Happened** _\(All times UTC\)_ ### **October 1: Docker Gen 2 jobs delayed up to 30 minutes** At 07:44, disk activity on the servers that schedule Gen 2 Docker jobs rose to 90% of capacity and stayed there. At 07:57, the number of Docker jobs waiting to start began to grow, and wait times rose with it. An automated alert paged the on-call engineer at 08:08. We declared an incident at 08:42. At 08:43, engineers identified high disk latency on the scheduling servers. They first believed the disks were too slow for the load, so they prepared a change to a faster disk type. At 09:01, the team agreed on two workstreams: move the servers to faster disks, and add a second Gen 2 cluster so that jobs would split across two clusters. At 09:41, engineers found the root cause. Each scheduling server has fast disks attached directly to it, and the scheduler is meant to keep its working data there. These servers run on a security-hardened operating system image, and on that image the fast disks were not being used. The scheduler wrote its data to slower network-attached disks instead. The script that sets up the fast disks failed without an error, and a gap in logging on these servers hid the failure. Gen 2 traffic had also become increasingly bursty. The slower disks saturated during those bursts, and the scheduler fell behind. Wait times peaked at 09:54, when jobs on the Gen 2 medium resource class waited up to 30 minutes. At 09:55, engineers applied a fix that set up the fast disks correctly. At 10:11, the scheduler began placing jobs faster, which confirmed the servers could keep up with new jobs. Wait times for Gen 2 medium fell to 13 minutes at 10:14, 7 minutes at 10:17, and 1 minute 30 seconds at 10:20. We moved the status page to monitoring at 10:24 and resolved the incident at 10:45. At 11:20, the second Gen 2 cluster began running jobs. ### **October 7, 13:27 to 14:06: Elevated wait times for machine jobs** We declared an incident at 13:27 after seeing elevated queueing and wait times. We posted to the status page at 13:31. At 13:31, engineers saw that we were struggling to acquire virtual machines for machine jobs, and that the number of jobs waiting to start was high mainly for machine jobs. At 13:34, wait times were elevated across executors. At 13:48, wait times began to recover as more machines became available, and we resolved the incident at 14:06. ### **October 7, 14:24 to 15:21: Job failures and workflow delays** Shortly after the previous incident was marked as resolved, we declared a new incident at 14:24 due to a sharp drop in the number of jobs submitted to our execution systems. At 14:26, engineers saw that pods of the job configuration service were restarting. Pods that restart leave fewer copies of the service to answer requests, and jobs that could not retrieve their configuration after repeated attempts failed with an infrastructure error. Workflows that were already running waited for updates from jobs that had not started. At 14:35, engineers increased the amount of available memory to the pods and increased the number of pods, which gave the service more copies to share the load. We also increased the amount of memory available Job submissions began recovering, and the number of messages flowing through our workflow system returned to normal levels. At 14:44, we moved the status page to monitoring. Pods in the job configuration service reached their memory limits and restarted. We increased memory allocations and pod counts, and the service recovered. At 15:13, we increased the number of instances in our Gen 2 Docker clusters so we could work through the backlog of waiting jobs quickly. We resolved the incident at 15:21. ### **October 7, 18:49 to 20:05: Elevated wait times for Docker Gen 2 jobs** We declared a new incident at 18:49 after Docker Gen 2 wait times rose. At 18:51, engineers confirmed that a shortage of available instances kept us from acquiring enough of them, which raised wait times. Wait times differed by resource class size. At 19:52, average wait times were lower than an hour earlier as more capacity became available, though brief spikes continued, and we moved the status page to monitoring. We resolved the incident at 20:05. ### **October 8: Linux machine and remote Docker jobs wait up to 50 minutes** We declared an incident at 12:48 after seeing a large number of jobs waiting to start and long wait times for machine and remote Docker jobs. At 12:49, engineers saw that our cloud provider could not supply enough instances. Delays continued to climb as we investigated. At 13:13, we began examining the logic that chooses a different instance type or region when our first choice is unavailable. At 13:24, we suspected that a defect in that logic was stopping us from using other instance types in our primary region, so we disabled our fallback region and confirmed the cause of the defect. Engineers began working on a fix and deployed it at 14:49. The service began requesting other instance types and started machines in large numbers. At 14:58, the queue of jobs waiting for a machine dropped steadily. Average wait times across resource classes continued to drop over the next hour as we worked through the backlog of waiting jobs. At 16:00, the queue of jobs waiting for a machine cleared and the number of waiting jobs returned to normal. We moved the status page to monitoring at 16:09 and resolved the incident at 16:35. ## **Future Prevention and Process Improvement** We are taking the following steps to strengthen the resilience of job start times, including supply diversification, fallback logic, and earlier detection. **We are expanding our compute regions.** In the October 7 and October 8 incidents, our cloud provider could not supply enough instances in the regions we used. We are adding new regions to our fallback logic in order handle spikes for demand that outpace our preferred regions. **We are fixing the logic that picks a different instance type.** On October 8, a defect kept the machine provisioning service from using other instance types in the same region. We deployed a fix during the incident and are now reviewing and refactoring this logic as a whole, with tests, so that a shortage of one instance type moves work to another. **We are expanding the instance types we offer and securing additional capacity.** We are working with our cloud provider to reserve additional capacity in our existing regions, and we are adding instance types that jobs can run on. Industry demand for high-performance compute is growing. We are diversifying where and how we source capacity so our customers are insulated from supply variation. **We added a second Gen 2 Docker cluster so traffic bursts are distributed across two clusters.** We are also adding alerts on scheduling server disk saturation and on how fast the scheduler places jobs, and an alert that fires when a server starts without its fast disks in use. We made the disk setup script fail with an error, and we restored log shipping from these servers so that a similar problem is visible sooner. Customer experience is our top priority, and we commit to continually improving the reliability of our systems to match the trust that our customers place in us. We thank our customers for their patience while our team worked to resolve these incidents. Please reach out to our support team with any questions or concerns.

Customers may experience delays in new signups and plan changes being reflected in their account.

This incident has been resolved.

Customers may experience delays in starting jobs on Linux Machine. We are investigating.

Task wait times for Linux Machine are returning to normal.

This incident has been resolved.

We are investigating a delay in pipeline data becoming available. Pipelines are still running, but data about them may be delayed in the UI or API.

We have identified the issue and are working on a remediation. Thank you for your patience.

Pipeline data is now available as normal, with the exception of test results, which may still be delayed. We are continuing to work on this, thank you for your patience.

All pipeline data is now available as normal. We will continue to monitor recovery.

This incident has been resolved.

We are currently seeing a delay in pipeline data surfacing in the UI.

We are starting to see some recovery.

The incident impacting pipeline data in the UI has now been resolved. We thank you for your patience while our engineers worked on reducing the delays.

Due to increased network errors and latencies we are seeing increased wait times for macOS jobs. We are investigating this with our macOS hosting provider.

Wait times for macOS jobs are reducing and we are continuing to investigate.

Our macOS hosting provider has implemented a mitigation and we have seen improved latencies. We will continue to monitor.

Wait times for macOS jobs are back to normal.

A small number of customers may see errors or be unable to load the pipelines page for projects with deprecated-config warnings, when using the classic pipelines view. Customers on the new pipelines view are not affected. We’ve identified the cause and deployed a fix.

A fix has been implemented and we are monitoring the results.

This incident has been resolved. A small number of customers who created pipelines between 18:53 UTC and 19:24 UTC that contained config using deprecated config syntax will have seen their build-type jobs fail to start. Customers whose jobs or pipelines failed may rerun them.

We have detected network issues affecting our Mac infrastructure. We're investigating and will provide updates shortly. Customers may experience queuing during this time.

We've identified the networking issue with our Mac infrastructure provider, and are working with them to resolve the issue. We're starting to see some recovery, but we expect customers may still see some queueing. We'll update again shortly.

Our Mac infrastructure provider has applied a fix to this networking issue and we're seeing queue times return to normal. We're going to continue keeping an eye on things for a little while longer. We'll update again shortly.

We've seen no further impact to queue times and everything has returned to normal. We appreciate your patience while we worked to resolve and monitor this issue.

Customers may experience intermittent errors when viewing plan usage UIs or calling plan usage APIs. We have identified the issue and we are actively working to mitigate these errors.

We have mitigated the issue with plan usage APIs and the relevant pages in the UI are loading correctly.

This incident has been resolved.

Between 19:49 UTC and 21:07 UTC on August 25, 2026, customers attempting to log in to CircleCI using GitHub were unable to sign in, and received an error from GitHub stating the callback URL was invalid. Customers who were already logged in were not affected. The issue has been resolved and GitHub login is functioning normally. We thank you for your patience while our team worked on implementing a fix.

The cause of the issue has been identified and we are at work fixing it.

A fix has been implemented and we are monitoring the results.

Insights data is good once more. Thank you for your patience.