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docs: hardware recommendations for reference architectures
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51 changes: 51 additions & 0 deletions docs/admin/architectures/1k-users.md
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# Reference Architecture: up to 1,000 users

The 1,000 users architecture is designed to cover a wide range of workflows.
Examples of subjects that might utilize this architecture include medium-sized
tech startups, educational units, or small to mid-sized enterprises.

**Target load**: API: up to 180 RPS

**High Availability**: non-essential for small deployments

## Hardware recommendations

### Coderd nodes

| Users | Node capacity | Replicas | GCP | AWS | Azure |
| ----------- | ------------------- | -------- | --------------- | ---------- | ----------------- |
| Up to 1,000 | 2 vCPU, 8 GB memory | 1-2 | `n1-standard-2` | `t3.large` | `Standard_D2s_v3` |

**Footnotes**:

- For small deployments (ca. 100 users, 10 concurrent workspace builds), it is
acceptable to deploy provisioners on `coderd` nodes.

### Provisioner nodes

| Users | Node capacity | Replicas | GCP | AWS | Azure |
| ----------- | -------------------- | ------------------------------ | ---------------- | ------------ | ----------------- |
| Up to 1,000 | 8 vCPU, 32 GB memory | 2 nodes / 30 provisioners each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |

**Footnotes**:

- An external provisioner is deployed as Kubernetes pod.

### Workspace nodes

| Users | Node capacity | Replicas | GCP | AWS | Azure |
| ----------- | -------------------- | ----------------------- | ---------------- | ------------ | ----------------- |
| Up to 1,000 | 8 vCPU, 32 GB memory | 64 / 16 workspaces each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |

**Footnotes**:

- Assumed that a workspace user needs at minimum 2 GB memory to perform. We
recommend against over-provisioning memory for developer workloads, as this my
lead to OOMKiller invocations.
- Maximum number of Kubernetes workspace pods per node: 256

### Database nodes

| Users | Node capacity | Replicas | Storage | GCP | AWS | Azure |
| ----------- | ------------------- | -------- | ------- | ------------------ | ------------- | ----------------- |
| Up to 1,000 | 2 vCPU, 8 GB memory | 1 | 512 GB | `db-custom-2-7680` | `db.t3.large` | `Standard_D2s_v3` |
59 changes: 59 additions & 0 deletions docs/admin/architectures/2k-users.md
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# Reference Architecture: up to 2,000 users

In the 2,000 users architecture, there is a moderate increase in traffic,
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.

**Target load**: API: up to 300 RPS

**High Availability**: The mode is _disabled_, but administrators may consider
enabling it for deployment reliability.

## Hardware recommendations

### Coderd nodes

| Users | Node capacity | Replicas | GCP | AWS | Azure |
| ----------- | -------------------- | -------- | --------------- | ----------- | ----------------- |
| Up to 2,000 | 4 vCPU, 16 GB memory | 2 | `n1-standard-4` | `t3.xlarge` | `Standard_D4s_v3` |

### Provisioner nodes

| Users | Node capacity | Replicas | GCP | AWS | Azure |
| ----------- | -------------------- | ------------------------------ | ---------------- | ------------ | ----------------- |
| Up to 2,000 | 8 vCPU, 32 GB memory | 4 nodes / 30 provisioners each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |

**Footnotes**:

- 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.

### Workspace nodes

| Users | Node capacity | Replicas | GCP | AWS | Azure |
| ----------- | -------------------- | ------------------------ | ---------------- | ------------ | ----------------- |
| Up to 2,000 | 8 vCPU, 32 GB memory | 128 / 16 workspaces each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |

**Footnotes**:

- 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

### Database nodes

| Users | Node capacity | Replicas | Storage | GCP | AWS | Azure |
| ----------- | -------------------- | -------- | ------- | ------------------- | -------------- | ----------------- |
| Up to 2,000 | 4 vCPU, 16 GB memory | 1 | 1 TB | `db-custom-4-15360` | `db.t3.xlarge` | `Standard_D4s_v3` |
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We've been using a db-custom-8-32768 for our 2000 user scaletests, so these CPU number here may be slightly inaccurate. Granted, for a regular (non-scaletest) deployment, I think 4 vCPU is borderline sufficient as it's the 2000 active workspaces that push the DB CPU load up to ~80% (8 vCPU).

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On the other hand, our scaletesting methology was really aggressive, so I would expect a similar pattern on the user side 🤔
What is your recommendation @maf? Should we switch to db-custom-8-32768?


**Footnotes**:

- Consider adding more replicas if the workspace activity is higher than 500
workspace builds per day.
56 changes: 56 additions & 0 deletions docs/admin/architectures/3k-users.md
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# Reference Architecture: up to 3,000 users

The 3,000 users architecture targets large-scale enterprises, possibly with
on-premises network and cloud deployments.

**Target load**: API: up to 550 RPS

**High Availability**: Typically, such scale requires a fully-managed HA
PostgreSQL service, and all Coder observability features enabled for operational
purposes.

## Hardware recommendations

### Coderd nodes

| Users | Node capacity | Replicas | GCP | AWS | Azure |
| ----------- | -------------------- | -------- | --------------- | ----------- | ----------------- |
| Up to 3,000 | 8 vCPU, 32 GB memory | 4 | `n1-standard-4` | `t3.xlarge` | `Standard_D4s_v3` |

### Provisioner nodes

| Users | Node capacity | Replicas | GCP | AWS | Azure |
| ----------- | -------------------- | ------------------------ | ---------------- | ------------ | ----------------- |
| Up to 3,000 | 8 vCPU, 32 GB memory | 8 / 30 provisioners each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |

**Footnotes**:

- An external provisioner is deployed as Kubernetes pod.
- It is strongly discouraged to run provisioner daemons on `coderd` nodes.
- Separate provisioners into different namespaces in favor of zero-trust or
multi-cloud deployments.

### Workspace nodes

| Users | Node capacity | Replicas | GCP | AWS | Azure |
| ----------- | -------------------- | ------------------------------ | ---------------- | ------------ | ----------------- |
| Up to 3,000 | 8 vCPU, 32 GB memory | 256 nodes / 12 workspaces each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |

**Footnotes**:

- 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.

### Database nodes

| Users | Node capacity | Replicas | Storage | GCP | AWS | Azure |
| ----------- | -------------------- | -------- | ------- | ------------------- | --------------- | ----------------- |
| Up to 3,000 | 8 vCPU, 32 GB memory | 2 | 1.5 TB | `db-custom-8-30720` | `db.t3.2xlarge` | `Standard_D8s_v3` |

**Footnotes**:

- Consider adding more replicas if the workspace activity is higher than 1500
workspace builds per day.
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