opensource/coder
3
0
Fork 0
mirror of https://github.com/coder/coder.git synced 2026-09-24 15:04:27 +08:00
Code Issues Packages Projects Releases Wiki Activity

docs: update scale architecture and add 10k user doc (#21454)

Browse Source 
Updates 2k, 3k docs to match previous changes to 1k ( #21362), including new database recommendations.

Adds a 10k doc.
This commit is contained in:
Spike Curtis
2026-01-09 08:16:11 +04:00
committed by GitHub
parent 1e8c292855
commit 4bc49ed6eb
7 changed files with 245 additions and 66 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/admin/infrastructure/scale-testing.md
+2
View File
@@ -100,6 +100,8 @@ Database:
- [Up to 2,000 users](./validated-architectures/2k-users.md)
- [Up to 3,000 users](./validated-architectures/3k-users.md)
-
- [Up to 10,000 users](./validated-architectures/10k-users.md)
## Hardware recommendation
docs/admin/infrastructure/validated-architectures/10k-users.md
+97
View File
@@ -0,0 +1,97 @@
# Reference Architecture: up to 10,000 users
The 10,000 users architecture targets enterprises with an extremely large global workforce of technical professionals or
applications requiring lots of simultaneous workspaces (for example, Agentic AI).
The recommendations on this page apply to deployments with up to the following limits. If your needs
exceed any of these limits, consider increasing deployment resources.
| Users | Concurrent Running Workspaces | Concurrent Builds |
|-------|-------------------------------|-------------------|
| 10000 | 6000 | 600 |
**Observability**: Deploy monitoring solutions to gather Prometheus metrics and
visualize them with Grafana to gain detailed insights into infrastructure and
application behavior. This allows operators to respond quickly to incidents and
continuously improve the reliability and performance of the platform.
## Hardware recommendations
### Coderd
| vCPU | Memory | Replicas |
|------|--------|----------|
| 4 | 12 GB | 10 |
**Notes**:
- "General purpose" virtual machines, such as N4-series in GCP or M8-series in AWS work well.
- If deploying on Kubernetes:
- Set CPU request and limit to `4000m`
- Set Memory request and limit to `12Gi`
- Coderd does not typically benefit from high performance disks like SSDs (unless you are co-locating provisioners).
- Coderd instances should be deployed in the same region as the database.
### Workspace Proxies
If you choose to deploy workspaces in multiple geographic regions, provision
[Workspace Proxies](../../networking/workspace-proxies.md) in each region.
| vCPU | Memory | Replicas |
|------|--------|----------|
| 4 | 12 GB | 10 |
**Notes**:
- "General purpose" virtual machines, such as N4-series in GCP or M8-series in AWS work well.
- If deploying on Kubernetes:
- Set CPU request and limit to `4000m`
- Set Memory request and limit to `12Gi`
- Workspace Proxies do not typically benefit from high performance disks like SSDs.
### Provisioners
| vCPU | Memory | Replicas |
|------|--------|----------|
| 1 | 1 GB | 180 |
**Notes**:
- "General purpose" virtual machines, such as N4-series in GCP or M8-series in AWS work well.
- If deploying on Kubernetes:
- Set CPU request and limit to `1000m`
- Set Memory request and limit to `1Gi`
- If deploying on virtual machines, stack up to 30 provisioners per machine with a commensurate amount of memory and CPU.
- Provisioners benefit from high performance disks like SSDs.
- [Do not run provisioners on Coderd nodes](../../provisioners/index.md#disable-built-in-provisioners) at this scale.
- If deploying workspaces to multiple clouds or multiple Kubernetes clusters, divide the provisioner replicas among the
clouds or clusters according to expected usage.
### Database
| vCPU | Memory | Replicas |
|------|--------|----------|
| 64 | 240 GB | 1 |
**Notes**:
- "General purpose" virtual machines, such as the M8-series in AWS work well.
- Deploy in the same region as `coderd`
### Workspaces
The following resource requirements are for the Coder Workspace Agent, which runs alongside your end users work, and as
such should be interpreted as the _bare minimum_ requirements for a Coder workspace. Size your workspaces to fit the use
case your users will be undertaking. If in doubt, chose sizes based on the development environments your users are
migrating from onto Coder.
| vCPU | Memory |
|------|--------|
| 0.1 | 128 MB |
## Footnotes for AWS instance types
- For production deployments, we recommend using non-burstable instance types,
such as `m5` or `c5`, instead of burstable instances, such as `t3`.
Burstable instances can experience significant performance degradation once
CPU credits are exhausted, leading to poor user experience under sustained load.
docs/admin/infrastructure/validated-architectures/1k-users.md
+1 -1
View File
@@ -43,7 +43,7 @@ architectural tier](./2k-users.md).
- If deploying on Kubernetes:
- Set CPU request and limit to `1000m`
- Set Memory request and limit to `1Gi`
- If deploying on virtual machines, stack up to 30 provisioners per machine with a cummensurate amount of memory and CPU.
- If deploying on virtual machines, stack up to 30 provisioners per machine with a commensurate amount of memory and CPU.
- Provisioners benefit from high performance disks like SSDs.
- For small deployments (ca. 100 users, 10 concurrent workspace builds), it is
acceptable to deploy provisioners on `coderd` nodes.
docs/admin/infrastructure/validated-architectures/2k-users.md
+71 -32
View File
@@ -5,55 +5,94 @@ suggesting a growing user base or expanding operations. This setup is
well-suited for mid-sized companies experiencing growth or for universities
seeking to accommodate their expanding user populations.
Users can be evenly distributed between 2 regions or be attached to different
clusters.
The recommendations on this page apply to deployments with up to the following limits. If your needs
exceed any of these limits, consider increasing deployment resources or moving to the [next-higher
architectural tier](./3k-users.md).
**Target load**: API: up to 300 RPS
| Users | Concurrent Running Workspaces | Concurrent Builds |
|-------|-------------------------------|-------------------|
| 2000 | 1200 | 120 |
**High Availability**: The mode is _enabled_; multiple replicas provide higher
deployment reliability under load.
**Observability**: Deploy monitoring solutions to gather Prometheus metrics and
visualize them with Grafana to gain detailed insights into infrastructure and
application behavior. This allows operators to respond quickly to incidents and
continuously improve the reliability and performance of the platform.
## Hardware recommendations
### Coderd nodes
### Coderd
| Users | Node capacity | Replicas | GCP | AWS | Azure |
|-------------|----------------------|------------------------|-----------------|-------------|-------------------|
| Up to 2,000 | 4 vCPU, 16 GB memory | 2 nodes, 1 coderd each | `n1-standard-4` | `m5.xlarge` | `Standard_D4s_v3` |
| vCPU | Memory | Replicas |
|------|--------|----------|
| 4 | 12 GB | 3 |
### Provisioner nodes
**Notes**:
| Users | Node capacity | Replicas | GCP | AWS | Azure |
|-------------|----------------------|-------------------------------|------------------|--------------|-------------------|
| Up to 2,000 | 8 vCPU, 32 GB memory | 4 nodes, 30 provisioners each | `t2d-standard-8` | `c5.2xlarge` | `Standard_D8s_v3` |
- "General purpose" virtual machines, such as N4-series in GCP or M8-series in AWS work well.
- If deploying on Kubernetes:
- Set CPU request and limit to `4000m`
- Set Memory request and limit to `12Gi`
- Coderd does not typically benefit from high performance disks like SSDs (unless you are co-locating provisioners).
- Coderd instances should be deployed in the same region as the database.
**Footnotes**:
### Workspace Proxies
- An external provisioner is deployed as Kubernetes pod.
- It is not recommended to run provisioner daemons on `coderd` nodes.
- Consider separating provisioners into different namespaces in favor of
zero-trust or multi-cloud deployments.
If you choose to deploy workspaces in multiple geographic regions, provision
[Workspace Proxies](../../networking/workspace-proxies.md) in each region.
### Workspace nodes
| vCPU | Memory | Replicas |
|------|--------|----------|
| 4 | 12 GB | 3 |
| Users | Node capacity | Replicas | GCP | AWS | Azure |
|-------------|----------------------|-------------------------------|------------------|--------------|-------------------|
| Up to 2,000 | 8 vCPU, 32 GB memory | 128 nodes, 16 workspaces each | `t2d-standard-8` | `m5.2xlarge` | `Standard_D8s_v3` |
**Notes**:
**Footnotes**:
- "General purpose" virtual machines, such as N4-series in GCP or M8-series in AWS work well.
- If deploying on Kubernetes:
- Set CPU request and limit to `4000m`
- Set Memory request and limit to `12Gi`
- Workspace Proxies do not typically benefit from high performance disks like SSDs.
- Assumed that a workspace user needs 2 GB memory to perform
- Maximum number of Kubernetes workspace pods per node: 256
- Nodes can be distributed in 2 regions, not necessarily evenly split, depending
on developer team sizes
### Provisioners
### Database node
| vCPU | Memory | Replicas |
|------|--------|----------|
| 1 | 1 GB | 120 |
| Users | Node capacity | Storage | GCP | AWS | Azure |
|-------------|----------------------|---------|---------------------|----------------|-------------------|
| Up to 2,000 | 4 vCPU, 16 GB memory | 1 TB | `db-custom-4-15360` | `db.m5.xlarge` | `Standard_D4s_v3` |
**Notes**:
**Footnotes for AWS instance types**:
- "General purpose" virtual machines, such as N4-series in GCP or M8-series in AWS work well.
- If deploying on Kubernetes:
- Set CPU request and limit to `1000m`
- Set Memory request and limit to `1Gi`
- If deploying on virtual machines, stack up to 30 provisioners per machine with a commensurate amount of memory and CPU.
- Provisioners benefit from high performance disks like SSDs.
- [Do not run provisioners on Coderd nodes](../../provisioners/index.md#disable-built-in-provisioners) at this scale.
- If deploying workspaces to multiple clouds or multiple Kubernetes clusters, divide the provisioner replicas among the
clouds or clusters according to expected usage.
### Database
| vCPU | Memory | Replicas |
|------|--------|----------|
| 16 | 60 GB | 1 |
**Notes**:
- "General purpose" virtual machines, such as the M8-series in AWS work well.
- Deploy in the same region as `coderd`
### Workspaces
The following resource requirements are for the Coder Workspace Agent, which runs alongside your end users work, and as
such should be interpreted as the _bare minimum_ requirements for a Coder workspace. Size your workspaces to fit the use
case your users will be undertaking. If in doubt, chose sizes based on the development environments your users are
migrating from onto Coder.
| vCPU | Memory |
|------|--------|
| 0.1 | 128 MB |
## Footnotes for AWS instance types
- For production deployments, we recommend using non-burstable instance types,
such as `m5` or `c5`, instead of burstable instances, such as `t3`.
docs/admin/infrastructure/validated-architectures/3k-users.md
+67 -33
View File
@@ -3,11 +3,13 @@
The 3,000 users architecture targets large-scale enterprises, possibly with
on-premises network and cloud deployments.
**Target load**: API: up to 550 RPS
The recommendations on this page apply to deployments with up to the following limits. If your needs
exceed any of these limits, consider increasing deployment resources or moving to the [next-higher
architectural tier](./10k-users.md).
**High Availability**: Typically, such scale requires a fully-managed HA
PostgreSQL service, and all Coder observability features enabled for operational
purposes.
| Users | Concurrent Running Workspaces | Concurrent Builds |
|-------|-------------------------------|-------------------|
| 3000 | 1800 | 180 |
**Observability**: Deploy monitoring solutions to gather Prometheus metrics and
visualize them with Grafana to gain detailed insights into infrastructure and
@@ -16,47 +18,79 @@ continuously improve the reliability and performance of the platform.
## Hardware recommendations
### Coderd nodes
### Coderd
| Users | Node capacity | Replicas | GCP | AWS | Azure |
|-------------|----------------------|-----------------------|-----------------|-------------|-------------------|
| Up to 3,000 | 8 vCPU, 32 GB memory | 4 node, 1 coderd each | `n1-standard-4` | `m5.xlarge` | `Standard_D4s_v3` |
| vCPU | Memory | Replicas |
|------|--------|----------|
| 4 | 12 GB | 4 |
### Provisioner nodes
**Notes**:
| Users | Node capacity | Replicas | GCP | AWS | Azure |
|-------------|----------------------|-------------------------------|------------------|--------------|-------------------|
| Up to 3,000 | 8 vCPU, 32 GB memory | 8 nodes, 30 provisioners each | `t2d-standard-8` | `c5.2xlarge` | `Standard_D8s_v3` |
- "General purpose" virtual machines, such as N4-series in GCP or M8-series in AWS work well.
- If deploying on Kubernetes:
- Set CPU request and limit to `4000m`
- Set Memory request and limit to `12Gi`
- Coderd does not typically benefit from high performance disks like SSDs (unless you are co-locating provisioners).
- Coderd instances should be deployed in the same region as the database.
**Footnotes**:
### Workspace Proxies
- An external provisioner is deployed as Kubernetes pod.
- It is strongly discouraged to run provisioner daemons on `coderd` nodes at
this level of scale.
- Separate provisioners into different namespaces in favor of zero-trust or
multi-cloud deployments.
If you choose to deploy workspaces in multiple geographic regions, provision
[Workspace Proxies](../../networking/workspace-proxies.md) in each region.
### Workspace nodes
| vCPU | Memory | Replicas |
|------|--------|----------|
| 4 | 12 GB | 4 |
| Users | Node capacity | Replicas | GCP | AWS | Azure |
|-------------|----------------------|-------------------------------|------------------|--------------|-------------------|
| Up to 3,000 | 8 vCPU, 32 GB memory | 256 nodes, 12 workspaces each | `t2d-standard-8` | `m5.2xlarge` | `Standard_D8s_v3` |
**Notes**:
**Footnotes**:
- "General purpose" virtual machines, such as N4-series in GCP or M8-series in AWS work well.
- If deploying on Kubernetes:
- Set CPU request and limit to `4000m`
- Set Memory request and limit to `12Gi`
- Workspace Proxies do not typically benefit from high performance disks like SSDs.
- Assumed that a workspace user needs 2 GB memory to perform
- Maximum number of Kubernetes workspace pods per node: 256
- As workspace nodes can be distributed between regions, on-premises networks
and cloud areas, consider different namespaces in favor of zero-trust or
multi-cloud deployments.
### Provisioners
### Database node
| vCPU | Memory | Replicas |
|------|--------|----------|
| 1 | 1 GB | 180 |
| Users | Node capacity | Storage | GCP | AWS | Azure |
|-------------|----------------------|---------|---------------------|-----------------|-------------------|
| Up to 3,000 | 8 vCPU, 32 GB memory | 1.5 TB | `db-custom-8-30720` | `db.m5.2xlarge` | `Standard_D8s_v3` |
**Notes**:
**Footnotes for AWS instance types**:
- "General purpose" virtual machines, such as N4-series in GCP or M8-series in AWS work well.
- If deploying on Kubernetes:
- Set CPU request and limit to `1000m`
- Set Memory request and limit to `1Gi`
- If deploying on virtual machines, stack up to 30 provisioners per machine with a commensurate amount of memory and CPU.
- Provisioners benefit from high performance disks like SSDs.
- [Do not run provisioners on Coderd nodes](../../provisioners/index.md#disable-built-in-provisioners) at this scale.
- If deploying workspaces to multiple clouds or multiple Kubernetes clusters, divide the provisioner replicas among the
clouds or clusters according to expected usage.
### Database
| vCPU | Memory | Replicas |
|------|--------|----------|
| 32 | 120 GB | 1 |
**Notes**:
- "General purpose" virtual machines, such as the M8-series in AWS work well.
- Deploy in the same region as `coderd`
### Workspaces
The following resource requirements are for the Coder Workspace Agent, which runs alongside your end users work, and as
such should be interpreted as the _bare minimum_ requirements for a Coder workspace. Size your workspaces to fit the use
case your users will be undertaking. If in doubt, chose sizes based on the development environments your users are
migrating from onto Coder.
| vCPU | Memory |
|------|--------|
| 0.1 | 128 MB |
## Footnotes for AWS instance types
- For production deployments, we recommend using non-burstable instance types,
such as `m5` or `c5`, instead of burstable instances, such as `t3`.
docs/admin/infrastructure/validated-architectures/index.md
+2
View File
@@ -220,6 +220,8 @@ For sizing recommendations, see the below reference architectures:
- [Up to 3,000 users](3k-users.md)
- [Up to 10,000 users](10k-users.md)
### AWS Instance Types
For production AWS deployments, we recommend using non-burstable instance types,