Coder powers secure, scalable development across key industries — automotive, finance, government, and technology — enabling faster builds, tighter compliance, and seamless AI adoption in enterprise-grade cloud environments.
We scale-test Coder with the same utility that can be used in your environment for insights into how Coder scales with your infrastructure.
General concepts
Coder runs workspace operations in a queue. The number of concurrent builds will be limited to the number of provisioner daemons across all coderd replicas.
Workspace builds are CPU-intensive, as it relies on Terraform. Various Terraform providers have different resource requirements. When tested with our kubernetes template, coderd will consume roughly 8 cores per 30 concurrent workspace builds. For effective provisioning, our helm chart prefers to schedule one coderd replica per-node.
To support 120 concurrent workspace builds, for example:
Create a cluster/nodepool with 4 nodes, 8-core each (AWS: t3.2xlarge GCP: e2-highcpu-8)
Run coderd with 4 replicas, 30 provisioner daemons each. (CODER_PROVISIONER_DAEMONS=30)
Since Coder's performance is highly dependent on the templates and workflows you support, we recommend using our scale testing utility against your own environments.
The following command will run our scale test against your own Coder deployment. You can also specify a template name and any parameter values.
To avoid potential outages and orphaned resources, we recommend running scale tests on a secondary "staging" environment.
The test does the following:
create 1000 workspaces
establish SSH connection to each workspace
run sleep 3 && echo hello on each workspace via the web terminal
close connections, attempt to delete all workspaces
return results (e.g. 998 succeeded, 2 failed to connect)
Concurrency is configurable. concurrency 0 means the scaletest test will attempt to create & connect to all workspaces immediately.
Troubleshooting
If a load test fails or if you are experiencing performance issues during day-to-day use, you can leverage Coder's prometheus metrics to identify bottlenecks during scale tests. Additionally, you can use your existing cloud monitoring stack to measure load, view server logs, etc.