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Providers

altimate supports 35+ LLM providers. Configure them in the provider section of your config file.

Provider Configuration

Each provider has a key in the provider object:

{
  "provider": {
    "<provider-name>": {
      "apiKey": "{env:API_KEY}",
      "baseURL": "https://custom.endpoint.com/v1",
      "headers": {
        "X-Custom-Header": "value"
      }
    }
  }
}

!!! tip Use {env:...} substitution for API keys so you never commit secrets to version control.

Altimate LLM Gateway

Managed LLM access with dynamic routing across Sonnet 4.6, Opus 4.6, GPT-5.4, GPT-5.3, and more. No API keys to manage — 10M tokens free to get started.

{
  "provider": {
    "altimate": {}
  },
  "model": "altimate/auto"
}

For pricing, security, and data handling details, see the Altimate LLM Gateway guide.

Anthropic

{
  "provider": {
    "anthropic": {
      "apiKey": "{env:ANTHROPIC_API_KEY}"
    }
  },
  "model": "anthropic/claude-sonnet-4-6"
}

Available models: claude-opus-4-6, claude-sonnet-4-6, claude-haiku-4-5-20251001

OpenAI

{
  "provider": {
    "openai": {
      "apiKey": "{env:OPENAI_API_KEY}"
    }
  },
  "model": "openai/gpt-4o"
}

AWS Bedrock

{
  "provider": {
    "bedrock": {
      "region": "us-east-1",
      "accessKeyId": "{env:AWS_ACCESS_KEY_ID}",
      "secretAccessKey": "{env:AWS_SECRET_ACCESS_KEY}"
    }
  },
  "model": "bedrock/anthropic.claude-sonnet-4-6-v1"
}

Uses the standard AWS credential chain. Set AWS_PROFILE or provide credentials directly.

!!! note If you have AWS SSO or IAM roles configured, Bedrock will use your default credential chain automatically, so no explicit keys are needed.

Azure OpenAI

{
  "provider": {
    "azure": {
      "apiKey": "{env:AZURE_OPENAI_API_KEY}",
      "baseURL": "https://your-resource.openai.azure.com/openai/deployments/your-deployment"
    }
  },
  "model": "azure/gpt-4o"
}

Google (Gemini)

{
  "provider": {
    "google": {
      "apiKey": "{env:GOOGLE_API_KEY}"
    }
  },
  "model": "google/gemini-2.5-pro"
}

Google Vertex AI

{
  "provider": {
    "google-vertex": {
      "project": "my-gcp-project",
      "location": "us-central1"
    }
  },
  "model": "google-vertex/gemini-2.5-pro"
}

Uses Google Cloud Application Default Credentials. Authenticate with:

gcloud auth application-default login

The project and location fields can also be set via environment variables:

Field Environment Variables (checked in order)
project GOOGLE_CLOUD_PROJECT, GCP_PROJECT, GCLOUD_PROJECT
location GOOGLE_VERTEX_LOCATION, GOOGLE_CLOUD_LOCATION, VERTEX_LOCATION

If location is not set, it defaults to us-central1.

!!! tip You can also access Anthropic models through Vertex AI using the google-vertex provider (e.g., google-vertex/claude-sonnet-4-6).

Ollama (Local)

{
  "provider": {
    "ollama": {
      "baseURL": "http://localhost:11434"
    }
  },
  "model": "ollama/llama3.1"
}

No API key needed. Runs entirely on your local machine.

!!! info Make sure Ollama is running before starting altimate. Install it from ollama.com and pull your desired model with ollama pull llama3.1.

LM Studio (Local)

Run local models through LM Studio's OpenAI-compatible server:

{
  "provider": {
    "lmstudio": {
      "name": "LM Studio",
      "npm": "@ai-sdk/openai-compatible",
      "env": ["LMSTUDIO_API_KEY"],
      "options": {
        "apiKey": "lm-studio",
        "baseURL": "http://localhost:1234/v1"
      },
      "models": {
        "qwen2.5-7b-instruct": {
          "name": "Qwen 2.5 7B Instruct",
          "tool_call": true,
          "limit": { "context": 131072, "output": 8192 }
        }
      }
    }
  },
  "model": "lmstudio/qwen2.5-7b-instruct"
}

Setup:

  1. Open LM Studio → Developer tab → Start Server (default port: 1234)
  2. Load a model in LM Studio
  3. Find your model ID: curl http://localhost:1234/v1/models
  4. Add the model ID to the models section in your config
  5. Use it: altimate-code run -m lmstudio/<model-id>

!!! tip The model key in your config must match the model ID returned by LM Studio's /v1/models endpoint. If you change models in LM Studio, update the config to match.

!!! note If you changed LM Studio's default port, update the baseURL accordingly. No real API key is needed — the "lm-studio" placeholder satisfies the SDK requirement.

OpenRouter

{
  "provider": {
    "openrouter": {
      "apiKey": "{env:OPENROUTER_API_KEY}"
    }
  },
  "model": "openrouter/anthropic/claude-sonnet-4-6"
}

Access 150+ models through a single API key.

Copilot

{
  "provider": {
    "copilot": {}
  },
  "model": "copilot/gpt-4o"
}

Uses your GitHub Copilot subscription. Authenticate with altimate auth.

!!! note "Codespaces & GitHub Actions" In GitHub Codespaces and GitHub Actions, the machine-scoped GITHUB_TOKEN lacks models:read permission and cannot be used for GitHub Copilot or GitHub Models inference. altimate automatically skips these providers in machine environments. To use them, authenticate explicitly with altimate auth or set a personal access token with models:read scope as a Codespace secret.

Snowflake Cortex

{
  "provider": {
    "snowflake-cortex": {}
  },
  "model": "snowflake-cortex/claude-sonnet-4-6"
}

Authenticate with altimate auth snowflake-cortex using a Programmatic Access Token (PAT). Enter credentials as account-identifier::pat-token.

Create a PAT in Snowsight: Admin > Security > Programmatic Access Tokens.

Billing flows through your Snowflake credits — no per-token costs.

Available models:

Model Tool Calling
claude-sonnet-4-6, claude-opus-4-6, claude-sonnet-4-5, claude-opus-4-5, claude-haiku-4-5, claude-4-sonnet, claude-3-7-sonnet, claude-3-5-sonnet Yes
openai-gpt-4.1, openai-gpt-5, openai-gpt-5-mini, openai-gpt-5-nano, openai-gpt-5-chat Yes
llama4-maverick, snowflake-llama-3.3-70b, llama3.1-70b, llama3.1-405b, llama3.1-8b No
mistral-large, mistral-large2, mistral-7b No
deepseek-r1 No

!!! note Model availability depends on your Snowflake region. Enable cross-region inference with ALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION' for full model access.

Custom / OpenAI-Compatible

Any OpenAI-compatible endpoint can be used as a provider:

{
  "provider": {
    "my-provider": {
      "api": "openai",
      "baseURL": "https://my-llm-proxy.example.com/v1",
      "apiKey": "{env:MY_API_KEY}"
    }
  },
  "model": "my-provider/my-model"
}

!!! tip This works with any service that exposes an OpenAI-compatible chat completions API, including vLLM, LiteLLM, and self-hosted inference servers.

Model Selection

Set your default model and a smaller model for lightweight tasks:

{
  "model": "anthropic/claude-sonnet-4-6",
  "small_model": "anthropic/claude-haiku-4-5-20251001"
}

The small_model is used for lightweight tasks like summarization and context compaction.

Provider Options Reference

Field Type Description
apiKey string API key (supports {env:...} and {file:...})
baseURL string Custom API endpoint URL
api string API type (e.g., "openai" for compatible endpoints)
headers object Custom HTTP headers to include with requests
region string AWS region (Bedrock only)
accessKeyId string AWS access key (Bedrock only)
secretAccessKey string AWS secret key (Bedrock only)
project string GCP project ID (Google Vertex AI only)
location string GCP region (Google Vertex AI only, default: us-central1)