> ## Documentation Index
> Fetch the complete documentation index at: https://docs.datafold.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Bring Your Own LLM Provider

> Run Datafold's AI features on your own LLM inference provider and credentials.

With an **LLM Provider integration**, your organization brings its own inference provider — your API keys, your endpoints, your cloud account — and Datafold's AI features run through it.

* **Datafold Assistant** (in the app and in [Slack](https://docs.datafold.com/integrations/agents/slack-bot.md)) runs only on your organization's own provider. It does not answer until an admin adds one.
* **Migrations delivered by Datafold** run on Datafold-managed model infrastructure by default. Add a provider if your migration must use your own inference account.

> [!NOTE]
>
> **NOTE**
>
> To set up an LLM Provider, navigate to **Settings** → **Integrations** → **LLM Providers** and click **Add new integration**. LLM Provider integrations are managed by organization admins.

## How it works

* **Register a provider** with its endpoint and credentials, and select which models to expose. For most providers, Datafold fetches the available model list automatically once credentials are entered.
* **Models appear in Datafold's model pickers**, labeled with the integration name.
* **Credentials are resolved at request time.** Editing an integration's key or endpoint takes effect on the next request.
* **Credentials are encrypted at rest** and never returned by the API — secret fields are masked on read and kept unchanged when a masked value is re-submitted.
* **Costs are tracked automatically.** Datafold tracks token usage and estimated spend per model.

## Supported providers

> [card] [OpenAI-compatible (universal)](https://docs.datafold.com/integrations/llm-providers/openai-compatible.md)

> [card] [OpenAI](https://docs.datafold.com/integrations/llm-providers/openai.md)

> [card] [Anthropic](https://docs.datafold.com/integrations/llm-providers/anthropic.md)

> [card] [Google Gemini](https://docs.datafold.com/integrations/llm-providers/google-gemini.md)

> [card] [Azure AI Foundry](https://docs.datafold.com/integrations/llm-providers/azure-ai-foundry.md)

> [card] [AWS Bedrock](https://docs.datafold.com/integrations/llm-providers/aws-bedrock.md)

> [card] [Google Vertex AI](https://docs.datafold.com/integrations/llm-providers/google-vertex-ai.md)

> [card] [Databricks](https://docs.datafold.com/integrations/llm-providers/databricks.md)

> [card] [Snowflake Cortex](https://docs.datafold.com/integrations/llm-providers/snowflake-cortex.md)

Anything that speaks the OpenAI `/chat/completions` protocol — Together, Fireworks, Groq, OpenRouter, Mistral, vLLM, LM Studio, Ollama, and most other inference services — works through the universal **OpenAI-compatible** provider.

## Common configuration

Every provider shares a few fields:

| Field | Description |
| - | - |
| **Name** | A display name for the integration (e.g. `Azure Prod`). Shown next to its models in pickers. |
| **Models** | The model ids this integration exposes. Fetched automatically where the provider supports listing; models can always be added by id manually. |
| **Max Output Tokens** | Per-response output cap. Pre-filled with a family-appropriate default; override if your deployment differs. |
| **Context Window** | Model context size in tokens. Pre-filled with a default; set it to your model's context size if it differs. |
| **Default model** | The model used when this is the default provider and no model is picked. Falls back to the first model when unset. |
| **Default provider** | Makes this the organization's default provider. Only one provider can be the default. A single provider is used automatically without this setting. |

## Model quality matters

Small models, or models without tool calling, perform poorly with the Datafold Migration Agent. We recommend frontier-class models (Claude Sonnet/Opus, GPT-5 family, Gemini Pro/Flash, Mistral Large).


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