upstash--context7/docs/enterprise/on-premise.mdx

---
title: "On-Premise Deployment"
sidebarTitle: "Getting Started"
description: "Run the full Context7 stack inside your own infrastructure, so code and documentation never leave your environment"
---

Context7 On-Premise lets you run the full Context7 stack inside your own infrastructure. Your code, documentation, and embeddings never leave your environment.

## What's Included

- Full Context7 parsing and indexing pipeline
- Local vector storage (no external vector DB required)
- Built-in MCP server. Works with any MCP-compatible AI client
- Web UI for managing indexed libraries and configuration
- REST API compatible with the public Context7 API
- Private GitHub and GitLab repository ingestion

<Frame>
  ![On-Premise Architecture](/images/on-premise-architecture.png)
</Frame>

## Setup

<Steps>

<Step title="Request a trial">

Go to [context7.com/plans](https://context7.com/plans) and click **On-Premise Trial**. Fill out the request form. No credit card required. You'll receive a 30-day full-featured license key via email once approved.

</Step>

<Step title="Deploy">

Follow the deployment guide for your platform:

<CardGroup cols={2}>
  <Card title="Docker" icon="docker" href="/enterprise/deployment/docker">
    Deploy with Docker Compose
  </Card>
  <Card title="Kubernetes" icon="dharmachakra" href="/enterprise/deployment/kubernetes">
    Deploy on Kubernetes with raw manifests
  </Card>
</CardGroup>

</Step>

<Step title="Complete the setup wizard">

Open `http://localhost:3000` in your browser. On first launch, the setup wizard guides you through configuring:

1. **AI Provider** - Choose OpenAI, Anthropic, Gemini, or a custom OpenAI-compatible endpoint. Enter your API key and model name.
2. **Embedding Provider** - Use the same provider as your LLM, or configure a separate one for embeddings.
3. **Git Tokens** - Add a GitHub and/or GitLab token for the platforms you use.

All configuration is stored locally in the embedded database and can be updated later from the Settings page.

</Step>

<Step title="Ingest your first repository">

From the dashboard, click **Add Repository** and enter a GitHub or GitLab URL. Once ingestion completes, your private docs are ready to query.

You can also add libraries via the REST API:

```bash
curl -X POST http://localhost:3000/api/parse \
  -H "Content-Type: application/json" \
  -d '{"url": "https://github.com/your-org/your-repo"}'
```

</Step>

</Steps>

## Connecting Your AI Client

Point your MCP client at your deployment URL. Replace `https://context7.internal.yourcompany.com` with your actual host.

### Claude Code

```bash
claude mcp add --scope user --transport http context7 https://context7.internal.yourcompany.com/mcp
```

### Cursor

Add to `~/.cursor/mcp.json`:

```json
{
  "mcpServers": {
    "context7": {
      "url": "https://context7.internal.yourcompany.com/mcp"
    }
  }
}
```

### Opencode

```json
{
  "mcp": {
    "context7": {
      "type": "remote",
      "url": "https://context7.internal.yourcompany.com/mcp",
      "enabled": true
    }
  }
}
```

For other clients, see [All Clients](/resources/all-clients).

## Configuration

### Environment Variables

These are set in your `docker-compose.yml` or `.env` file before starting the container.

| Variable | Required | Description |
|---|---|---|
| `LICENSE_KEY` | Yes | License key issued by Upstash |
| `PORT` | No | HTTP port (default: `3000`) |
| `DATA_DIR` | No | Data directory inside the container (default: `/data`) |

<Note>
AI provider keys, model settings, and git tokens are **not** set via environment variables. They are configured through the setup wizard and can be updated anytime from the Settings page in the web UI.
</Note>

### AI Provider Settings

Configured via the **Settings** page in the web UI.

| Setting | Description |
|---|---|
| LLM Provider | `openai`, `anthropic`, `gemini`, or custom |
| LLM API Key | API key for your chosen provider |
| LLM Model | Model name (e.g. `gpt-4o`, `claude-sonnet-4-5`, `gemini-2.5-flash`) |
| LLM Base URL | Custom OpenAI-compatible endpoint (for local models or proxies) |

#### Examples

<Tabs>
  <Tab title="OpenRouter">
    ```
    Provider: custom
    Base URL: https://openrouter.ai/api/v1
    Model: openai/gpt-4o
    API Key: sk-or-v1-...
    ```
  </Tab>
  <Tab title="Local Model (Ollama, vLLM)">
    ```
    Provider: custom
    Base URL: http://host.docker.internal:11434/v1
    Model: llama3.2
    API Key: ollama
    ```
  </Tab>
</Tabs>

### Embedding Settings

By default, Context7 uses the same provider as your LLM for generating embeddings. You can configure a separate embedding provider if needed.

| Setting | Description |
|---|---|
| Embedding Provider | `openai` or `gemini` |
| Embedding API Key | Separate API key for embeddings (falls back to LLM API key) |
| Embedding Model | Embedding model name (e.g. `text-embedding-3-small`) |
| Embedding Base URL | Custom embedding endpoint |

### Git Access Tokens

Configured via the **Settings** page in the web UI.

| Setting | Description |
|---|---|
| GitHub Token | GitHub Personal Access Token. Required for GitHub repositories |
| GitLab Token | GitLab token. Required for GitLab repositories |

You only need tokens for the platforms you use. If you only parse GitLab repos, you don't need a GitHub token, and vice versa. Create tokens with `repo` scope (GitHub) or `read_repository` scope (GitLab) for private repository access.

## Access Control

Admin credentials are set during first login (default: `admin` / `admin`). Change these immediately after setup via **Settings > Change Credentials**.

The Settings page lets you control which operations are available without authentication.

| Permission | Default | Description |
|---|---|---|
| Allow anonymous parse | Off | Allow unauthenticated users to trigger parsing |
| Allow anonymous refresh | Off | Allow unauthenticated users to refresh libraries |
| Allow anonymous delete | Off | Allow unauthenticated users to delete libraries |
| Allow anonymous support bundle | Off | Allow unauthenticated support bundle downloads |

When a permission is off, the operation requires admin login. The MCP endpoint and search API are always publicly accessible.

## Policies

Policies let you control which public documentation from the Context7 cloud is accessible to your on-premise instance. They do not affect locally parsed on-premise content.

Access Policies from **Settings > Policies** tab. Requires admin login and a valid `LICENSE_KEY`.

For details on source type toggles and library filters, see [Customizing What Is Retrieved](/security/data-privacy#customizing-what-is-retrieved).

## Web UI

Open your deployment URL in a browser to access the dashboard. From here you can:

- Add and remove libraries
- Trigger re-indexing
- Monitor parsing status and logs
- Update AI provider settings, git tokens, and permissions
- Configure policies for public cloud documentation access
- Test MCP connectivity
- Change admin credentials

## Operations

For updating, health checks, and other operational tasks, see the deployment guide for your platform:

- [Docker Operations](/enterprise/deployment/docker#operations)
- [Kubernetes Operations](/enterprise/deployment/kubernetes#operations)

## Support

For license issues, upgrade requests, or deployment questions, contact [context7@upstash.com](mailto:context7@upstash.com).