CircleCI Status Page

Oct 14, 2026 · resolved Oct 14

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.

Oct 9, 2026 · resolved Oct 10

## 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.

Oct 8, 2026 · resolved Oct 8

## **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.

Oct 7, 2026 · resolved Oct 7

## **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.

Oct 7, 2026 · resolved Oct 7

## **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.

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