title: Use a Gateway - Zed description: Configure OpenRouter, Vercel AI Gateway, Amazon Bedrock, and other gateway or cloud model platforms in Zed.

Use a Gateway

Use a gateway when you route model requests through a platform such as OpenRouter, Vercel AI Gateway, Amazon Bedrock, or another OpenAI-compatible service.

OpenRouter {#openrouter}

Use OpenRouter when you want to route Zed AI features through OpenRouter.

1. Visit OpenRouter and create an account.
2. Generate an API key from your OpenRouter keys page.
3. Open Settings → AI → LLM Providers with {#action agent::OpenSettings} and find the OpenRouter row.
4. Enter your OpenRouter API key.

Zed also reads OPENROUTER_API_KEY from the local Zed process environment.

When using OpenRouter as your assistant provider, explicitly select a model in your settings:

{
  "agent": {
    "default_model": {
      "provider": "openrouter",
      "model": "openrouter/auto"
    }
  }
}

The openrouter/auto model routes requests to an available model selected by OpenRouter. You can also specify any model available through OpenRouter's API.

OpenRouter Custom Models {#openrouter-custom-models}

You can add custom models to the OpenRouter provider in settings:

{
  "language_models": {
    "open_router": {
      "api_url": "https://openrouter.ai/api/v1",
      "available_models": [
        {
          "name": "google/gemini-2.0-flash-thinking-exp",
          "display_name": "Gemini 2.0 Flash (Thinking)",
          "max_tokens": 200000,
          "max_output_tokens": 8192,
          "supports_tools": true,
          "supports_images": true,
          "mode": {
            "type": "thinking",
            "budget_tokens": 8000
          }
        }
      ]
    }
  }
}

Custom model entries support fields such as name, display_name, max_tokens, max_output_tokens, max_completion_tokens, supports_tools, supports_images, and mode.

OpenRouter Provider Routing {#openrouter-provider-routing}

You can control how OpenRouter routes a custom model request among upstream providers with the provider object on each model entry.

Supported fields include order, allow_fallbacks, require_parameters, data_collection, only, ignore, quantizations, and sort.

{
  "language_models": {
    "open_router": {
      "available_models": [
        {
          "name": "openrouter/auto",
          "display_name": "Auto Router",
          "max_tokens": 2000000,
          "supports_tools": true,
          "provider": {
            "order": ["anthropic", "openai"],
            "allow_fallbacks": true,
            "require_parameters": true,
            "data_collection": "allow"
          }
        }
      ]
    }
  }
}

Vercel AI Gateway {#vercel-ai-gateway}

Use Vercel AI Gateway when you want to route Zed AI features through Vercel.

1. Create an API key from your Vercel AI Gateway keys page.
2. Open Settings → AI → LLM Providers with {#action agent::OpenSettings} and find the Vercel AI Gateway row.
3. Enter your Vercel AI Gateway API key.

Zed also reads VERCEL_AI_GATEWAY_API_KEY from the local Zed process environment.

You can set a custom endpoint for Vercel AI Gateway in settings:

{
  "language_models": {
    "vercel_ai_gateway": {
      "api_url": "https://ai-gateway.vercel.sh/v1"
    }
  }
}

Amazon Bedrock {#amazon-bedrock}

Use Amazon Bedrock when you want model access through AWS.

Bedrock supports models that support streaming tool use. See Amazon Bedrock's Tool Use documentation.

Your AWS credentials need these permissions:

• bedrock:InvokeModelWithResponseStream
• bedrock:InvokeModel

Bedrock supports Zed-prefixed AWS environment variables so Zed does not override or consume your normal AWS credentials:

• ZED_ACCESS_KEY_ID
• ZED_SECRET_ACCESS_KEY
• ZED_SESSION_TOKEN
• ZED_AWS_PROFILE
• ZED_AWS_REGION
• ZED_AWS_ENDPOINT
• ZED_BEDROCK_BEARER_TOKEN

Bedrock Authentication {#bedrock-authentication}

You can authenticate with a named profile, static credentials, or a Bedrock API key.

For a named profile, configure Bedrock in settings:

{
  "language_models": {
    "bedrock": {
      "authentication_method": "named_profile",
      "region": "your-aws-region",
      "profile": "your-profile-name"
    }
  }
}

For static credentials, open Agent Settings with {#action agent::OpenSettings}, go to the Amazon Bedrock section, and enter the access key ID, secret access key, and region.

For a Bedrock API key, choose API key authentication:

{
  "language_models": {
    "bedrock": {
      "authentication_method": "api_key",
      "region": "your-aws-region"
    }
  }
}

The API key itself is stored in the system keychain, not in settings.json.

Bedrock Cross-Region Inference {#bedrock-cross-region-inference}

Zed uses Cross-Region inference for Bedrock on a best-effort basis.

By default, Zed uses regional inference profiles. To opt into global profiles, add allow_global:

{
  "language_models": {
    "bedrock": {
      "authentication_method": "named_profile",
      "region": "your-aws-region",
      "profile": "your-profile-name",
      "allow_global": true
    }
  }
}

Only some models support global inference profiles. See the AWS Bedrock supported models documentation for the current list.

Bedrock Guardrails {#bedrock-guardrails}

Some AWS environments require a guardrail on every Bedrock API call. Add guardrail_identifier to apply a guardrail to all Bedrock requests:

{
  "language_models": {
    "bedrock": {
      "guardrail_identifier": "arn:aws:bedrock:us-east-1:123456789012:guardrail/abc123",
      "guardrail_version": "DRAFT"
    }
  }
}

Bedrock Mantle Models {#bedrock-mantle-models}

Some models, such as the GPT-5.6 family (Sol, Terra, and Luna), GPT-5.5, GPT-5.4, and Grok 4.3, aren't available through Bedrock's Converse API and are only reachable through bedrock-mantle, AWS's OpenAI-compatible inference endpoint. Zed routes these models through bedrock-mantle automatically; they appear alongside the rest of the Bedrock models in the model picker once you're authenticated, with no extra configuration required.

Mantle models require IAM permissions for the bedrock-mantle endpoint (for example via the AmazonBedrockMantleInferenceAccess managed policy) in addition to whatever permissions your existing Bedrock credentials already have, and bedrock-mantle is only available in some AWS Regions. Zed surfaces an error naming the current Region and the supported ones if you try to use a Mantle model outside of them.

Custom Bedrock Mantle Models {#bedrock-mantle-custom-models}

You can add custom models served through bedrock-mantle with mantle_available_models:

{
  "language_models": {
    "bedrock": {
      "mantle_available_models": [
        {
          "name": "openai.gpt-oss-120b",
          "display_name": "GPT-OSS 120B",
          "max_tokens": 128000,
          "protocol": "chat_completions",
          "supports_tools": true,
          "supports_images": false,
          "supports_thinking": true
        }
      ]
    }
  }
}

protocol selects which OpenAI-compatible API the model is called through, and must be either chat_completions or responses. Set supports_thinking to true for custom Mantle models that accept OpenAI reasoning effort parameters; Zed will then expose low, medium, high, and xhigh in the thinking effort picker, while disabling thinking sends none.

OpenAI-Compatible Gateways {#openai-compatible}

If your gateway exposes an OpenAI-compatible API, configure it with Use API Access.