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This guide shows how to set up a minimal deployment to use the TensorZero Gateway with the Anthropic API.

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Simple Setup

You can use the short-hand anthropic::model_name to use an Anthropic model with TensorZero, unless you need advanced features like fallbacks or custom credentials. You can use Anthropic models in your TensorZero variants by setting the model field to anthropic::model_name. For example: 
[functions.my_function_name.variants.my_variant_name]
type = "chat_completion"
model = "anthropic::claude-haiku-4-5"
Additionally, you can set model_name in the inference request to use a specific Anthropic model, without having to configure a function and variant in TensorZero. 
curl -X POST http://localhost:3000/inference \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "anthropic::claude-haiku-4-5",
    "input": {
      "messages": [
        {
          "role": "user",
          "content": "What is the capital of Japan?"
        }
      ]
    }
  }'

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Advanced Setup

In more complex scenarios (e.g. fallbacks, custom credentials), you can configure your own model and Anthropic provider in TensorZero. For this minimal setup, you’ll need just two files in your project directory: 
- config/
  - tensorzero.toml
- docker-compose.yml
You can also find the complete code for this example on GitHub.
For production deployments, see our Deployment Guide.

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Configuration

Create a minimal configuration file that defines a model and a simple chat function:
config/tensorzero.toml
[models.claude_haiku_4_5]
routing = ["anthropic"]

[models.claude_haiku_4_5.providers.anthropic]
type = "anthropic"
model_name = "claude-haiku-4-5"

[functions.my_function_name]
type = "chat"

[functions.my_function_name.variants.my_variant_name]
type = "chat_completion"
model = "claude_haiku_4_5"
See the list of models available on Anthropic.

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Credentials

You must set the ANTHROPIC_API_KEY environment variable before running the gateway. You can customize the credential location by setting the api_key_location to env::YOUR_ENVIRONMENT_VARIABLE or dynamic::ARGUMENT_NAME. See the Credential Management guide and Configuration Reference for more information.

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Deployment (Docker Compose)

Create a minimal Docker Compose configuration:
docker-compose.yml
# This is a simplified example for learning purposes. Do not use this in production.
# For production-ready deployments, see: https://www.tensorzero.com/docs/deployment/tensorzero-gateway

services:
  gateway:
    image: tensorzero/gateway
    volumes:
      - ./config:/app/config:ro
    command: --config-file /app/config/tensorzero.toml
    environment:
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:?Environment variable ANTHROPIC_API_KEY must be set.}
    ports:
      - "3000:3000"
    extra_hosts:
      - "host.docker.internal:host-gateway"
You can start the gateway with docker compose up.

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Inference

Make an inference request to the gateway: 
curl -X POST http://localhost:3000/inference \
  -H "Content-Type: application/json" \
  -d '{
    "function_name": "my_function_name",
    "input": {
      "messages": [
        {
          "role": "user",
          "content": "What is the capital of Japan?"
        }
      ]
    }
  }'

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Enable Anthropic’s prompt caching capability

You can enable Anthropic’s prompt caching capability with TensorZero’s extra_body. For example, to enable caching on your system prompt: 
curl -X POST "http://localhost:3000/inference" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "anthropic::claude-haiku-4-5",
    "input": {
      "system": "... very long prompt ...",
      "messages": [
        {
          "role": "user",
          "content": "Write a haiku about TensorZero."
        }
      ]
    },
    "extra_body": [
        {
            "pointer": "/system/0/cache_control",
            "value": {"type": "ephemeral"}
        }
    ]
  }'
Similarly, to enable caching on a message: 
curl -X POST "http://localhost:3000/inference" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "anthropic::claude-haiku-4-5",
    "input": {
      "system": "... very long prompt ...",
      "messages": [
        {
          "role": "user",
          "content": "Write a haiku about TensorZero."
        }
      ]
    },
    "extra_body": [
        {
            "pointer": "/messages/0/content/0/cache_control",
            "value": {"type": "ephemeral"}
        }
    ]
  }'
You can specify extra_body in the configuration or at inference time. If you’re using the OpenAI-Compatible Inference API, use tensorzero::extra_body instead.
You can retrieve prompt caching usage information with include_raw_usage. See the API Reference for more information.

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Use Anthropic models on third-party platforms

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Use Anthropic models on AWS Bedrock

You can use Anthropic models on AWS Bedrock with the aws_bedrock model provider. 
[models.claude_haiku_4_5.providers.aws]
type = "aws_bedrock"
model_id = "us.anthropic.claude-haiku-4-5-20251001-v1:0"
region = "us-east-1"  # TODO: set your AWS region
Read more about the AWS Bedrock model provider.

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Use Anthropic models on Azure

You can use Anthropic models on Azure AI Foundry by overriding the API base in your configuration: 
[models.claude_haiku_4_5.providers.azure]
type = "anthropic"
model_name = "claude-haiku-4-5"
api_base = "https://YOUR-RESOURCE-NAME.services.ai.azure.com/anthropic/v1/"  # TODO: set your resource name
api_key_location = "env::AZURE_API_KEY"  # optional

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Use Anthropic models on GCP Vertex AI

You can use Anthropic models on GCP Vertex AI with the gcp_vertex_anthropic model provider. 
[models.claude_haiku_4_5.providers.gcp]
type = "gcp_vertex_anthropic"
model_id = "claude-haiku-4-5@20251001"
location = "us-east5"  # TODO: set your GCP region
project_id = "YOUR-PROJECT-ID"  # TODO: set your GCP project ID
Read more about the GCP Vertex AI Anthropic model provider.

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Other Features

See Extend TensorZero for information about Anthropic Computer Use and other beta features.