Many customers operate Coder in complex organizational environments, consisting

of multiple business units, agencies, and/or subsidiaries. This can lead to

numerous Coder deployments,caused by discrepancies in regulatory compliance,

datasovereignty, and level of funding across groups. The Coder Validated

numerous Coder deployments,due to discrepancies in regulatory compliance, data

sovereignty, and level of funding across groups. The Coder Validated

Architecture (CVA) prescribes a Kubernetes-based deployment approach, enabling

your organization to deploy a stable Coder instance that is easier to maintain

and troubleshoot.

-**Reduce server load**: External provisioners reduce load and build queue

times from the Coder server. See

[Scaling Coder](./scale.md#concurrent-workspace-builds) for more details.

[Scaling Coder](scaling/scale-utility.md#recent-scale-tests) for more details.

Each provisioner can run a single

[concurrent workspace build](./scale.md#concurrent-workspace-builds). For

example, running 30 provisioner containers will allow 30 users to start

[concurrent workspace build](scaling/scale-testing.md#control-plane-provisionerd).

Forexample, running 30 provisioner containers will allow 30 users to start

workspaces at the same time.

Provisioners are started with the

Scaling Coder involves planning and testing to ensure it can handle more load

without compromising service. This process encompasses infrastructure setup,

traffic projections, and aggressive testing to identify and mitigate potential

bottlenecks.

A dedicated Kubernetes cluster for Coder isKubernetes cluster specifically

configured to host andmanage Coder workloads. Kubernetes provides container

orchestrationcapabilities, allowing Coder to efficiently deploy, scale, and

manage workspacesacross a distributed infrastructure. This ensures high

availability, faulttolerance, and scalability for Coder deployments. Coder is

deployed on thiscluster using the

A dedicated Kubernetes cluster for Coder isrecommended to configure, host and

manage Coder workloads. Kubernetes provides container orchestration

capabilities, allowing Coder to efficiently deploy, scale, and manage workspaces

across a distributed infrastructure. This ensures high availability, fault

tolerance, and scalability for Coder deployments. Coder is deployed on this

cluster using the

[Helm chart](../../install/kubernetes.md#install-coder-with-helm).

Our scale tests include the following stages:

1. Prepare environment: create expected users and provision workspaces.

6. Cleanup: delete workspaces and users created in step 1.

The scale tests runner can distribute the workload to overlap single scenarios

based on the workflow configuration:

workflows,`coderd` did not crash or require restarts, and no other internal

errors were observed.

In our scale tests, we simulate activity from 2000 users, 2000 workspaces, and

2000 agents, with two items of workspace agent metadata being sent every 10

[Up to 1,000 users](1k-users.md)

[Up to 1,000 users](../architectures/1k-users.md)

[Up to 2,000 users](2k-users.md)

[Up to 2,000 users](../architectures/2k-users.md)

[Up to 3,000 users](3k-users.md)

[Up to 3,000 users](../architectures/3k-users.md)

**Node Autoscaling**

We recommend disabling the autoscaling for`coderd` nodes. Autoscaling can cause

interruptions for user connections, see[Autoscaling](../scale.md#autoscaling)

for more details.

interruptions for user connections, see

[Autoscaling](scale-utility.md#autoscaling)for more details.

We scale-test Coder with[a built-in utility](#scale-testing-utility) that can

be used in your environment for insights into how Coder scales with your

infrastructure. For scale-testing Kubernetes clusters we recommend to install

and use the dedicated Coder template,

[scaletest-runner](https://github.com/coder/coder/tree/main/scaletest/templates/scaletest-runner).

Learn more about[Coder’s architecture](architectures/architecture.md) and our

[scale-testing methodology](architectures/scale-testing.md).

Learn more about[Coder’s architecture](../architectures/architecture.md) and

our[scale-testing methodology](scale-testing.md).

| Environment| Coder CPU| Coder RAM| Coder Replicas| Database| Users| Concurrent builds| Concurrent connections (Terminal/SSH)| Coder Version| Last tested|

If a load test fails or if you are experiencing performance issues during

day-to-day use, you can leverage Coder's[Prometheus metrics](./prometheus.md)

day-to-day use, you can leverage Coder's[Prometheus metrics](../prometheus.md)

to identify bottlenecks during scale tests. Additionally, you can use your

existing cloud monitoring stack to measure load, view server logs, etc.

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memory) if you plan on provisioning Docker containers as workspaces on this EC2

instance. Keep in mind this platforms is intended for proof-of-concept

deployments and you should adjust your infrastructure when preparing for

production use. See:[Scaling Coder](../admin/scale.md)

production use. See:[Scaling Coder](../admin/scaling/scale-testing.md)

Be sure to add a keypair so that you can connect over SSH to further

[configure Coder](../admin/configure.md).

GB memory) if you plan on provisioning Docker containers as workspaces on this

VM instance. Keep in mind this platforms is intended for proof-of-concept

deployments and you should adjust your infrastructure when preparing for

production use. See:[Scaling Coder](../admin/scale.md)

production use. See:[Scaling Coder](../admin/scaling/scale-testing.md)

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