--- title: "Azure Anthropic (Claude via Azure Foundry)" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/azure-anthropic source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:08:09.213Z license: CC-BY-4.0 attribution: "Azure Anthropic (Claude via Azure Foundry) — Claudary (https://claudary.paisolsolutions.com/skills/azure-anthropic)" --- # Azure Anthropic (Claude via Azure Foundry) import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Azure Anthropic (Claude via Azure Foundry) LiteLLM supports Claude models deployed via Microsoft Azure Foundry, including Claude Sonnet 4.5, Claude Haiku 4.5, and Claude Opus 4.1. ## Available Models Azure Foundry supports the following Claude models: - `claude-sonnet-4-5` - Anthropic's most capable model for building real-world agents and handling complex, long-horizon tasks - `claude-haiku-4-5` - Near-frontier performance with the right speed and cost for high-volume use cases - `claude-opus-4-1` - Industry leader for coding, delivering sustained performance on long-running tasks | Property | Details | |-------|-------| | Description | Claude models deployed via Microsoft Azure Foundry. Uses the same API as Anthropic's Messages API but with Azure authentication. | | Provider Route on LiteLLM | `azure_ai/` (add this prefix to Claude model names - e.g. `azure_ai/claude-sonnet-4-5`) | | Provider Doc | [Azure Foundry Claude Models ↗](https://learn.microsoft.com/en-us/azure/ai-services/foundry-models/claude) | | API Endpoint | `https://.services.ai.azure.com/anthropic/v1/messages` | | Supported Endpoints | `/chat/completions`, `/anthropic/v1/messages`| ## Key Features - **Extended thinking**: Enhanced reasoning capabilities for complex tasks - **Image and text input**: Strong vision capabilities for analyzing charts, graphs, technical diagrams, and reports - **Code generation**: Advanced thinking with code generation, analysis, and debugging (Claude Sonnet 4.5 and Claude Opus 4.1) - **Same API as Anthropic**: All request/response transformations are identical to the main Anthropic provider ## Authentication Azure Anthropic supports two authentication methods: 1. **API Key**: Use the `api-key` header 2. **Azure AD Token**: Use `Authorization: Bearer ` header (Microsoft Entra ID) ## API Keys and Configuration ```python import os # Option 1: API Key authentication os.environ["AZURE_API_KEY"] = "your-azure-api-key" os.environ["AZURE_API_BASE"] = "https://.services.ai.azure.com/anthropic" # Option 2: Azure AD Token authentication os.environ["AZURE_AD_TOKEN"] = "your-azure-ad-token" os.environ["AZURE_API_BASE"] = "https://.services.ai.azure.com/anthropic" # Optional: Azure AD Token Provider (for automatic token refresh) os.environ["AZURE_TENANT_ID"] = "your-tenant-id" os.environ["AZURE_CLIENT_ID"] = "your-client-id" os.environ["AZURE_CLIENT_SECRET"] = "your-client-secret" os.environ["AZURE_SCOPE"] = "https://cognitiveservices.azure.com/.default" ``` ## Usage - LiteLLM Python SDK ### Basic Completion ```python from litellm import completion # Set environment variables os.environ["AZURE_API_KEY"] = "your-azure-api-key" os.environ["AZURE_API_BASE"] = "https://.services.ai.azure.com/anthropic" # Make a completion request response = completion( model="azure_ai/claude-sonnet-4-5", messages=[ {"role": "user", "content": "What are 3 things to visit in Seattle?"} ], max_tokens=1000, temperature=0.7, ) print(response) ``` ### Completion with API Key Parameter ```python import litellm response = litellm.completion( model="azure_ai/claude-sonnet-4-5", api_base="https://.services.ai.azure.com/anthropic", api_key="your-azure-api-key", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1000, ) ``` ### Completion with Azure AD Token ```python import litellm response = litellm.completion( model="azure_ai/claude-sonnet-4-5", api_base="https://.services.ai.azure.com/anthropic", azure_ad_token="your-azure-ad-token", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1000, ) ``` ### Streaming ```python from litellm import completion response = completion( model="azure_ai/claude-sonnet-4-5", messages=[ {"role": "user", "content": "Write a short story"} ], stream=True, max_tokens=1000, ) for chunk in response: if chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) ``` ### Tool Calling ```python from litellm import completion response = completion( model="azure_ai/claude-sonnet-4-5", messages=[ {"role": "user", "content": "What's the weather in Seattle?"} ], tools=[ { "type": "function", "function": { "name": "get_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" } }, "required": ["location"] } } } ], tool_choice="auto", max_tokens=1000, ) print(response) ``` ## Usage - LiteLLM Proxy Server ### 1. Save key in your environment ```bash export AZURE_API_KEY="your-azure-api-key" export AZURE_API_BASE="https://.services.ai.azure.com/anthropic" ``` ### 2. Configure the proxy ```yaml model_list: - model_name: claude-sonnet-4-5 litellm_params: model: azure_ai/claude-sonnet-4-5 api_base: https://.services.ai.azure.com/anthropic api_key: os.environ/AZURE_API_KEY ``` ### 3. Test it ```bash curl --location 'http://0.0.0.0:4000/chat/completions' \\ --header 'Content-Type: application/json' \\ --data '{ "model": "claude-sonnet-4-5", "messages": [ { "role": "user", "content": "Hello!" } ], "max_tokens": 1000 }' ``` ```python from openai import OpenAI client = OpenAI( api_key="anything", base_url="http://0.0.0.0:4000" ) response = client.chat.completions.create( model="claude-sonnet-4-5", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1000 ) print(response) ``` ## Messages API Azure Anthropic also supports the native Anthropic Messages API. The endpoint structure is the same as Anthropic's `/v1/messages` API. ### Using Anthropic SDK ```python from anthropic import Anthropic client = Anthropic( api_key="your-azure-api-key", base_url="https://.services.ai.azure.com/anthropic" ) response = client.messages.create( model="claude-sonnet-4-5", max_tokens=1000, messages=[ {"role": "user", "content": "Hello, world"} ] ) print(response) ``` ### Using LiteLLM Proxy ```bash curl --request POST \\ --url http://0.0.0.0:4000/anthropic/v1/messages \\ --header 'accept: application/json' \\ --header 'content-type: application/json' \\ --header "Authorization: bearer sk-anything" \\ --data '{ "model": "claude-sonnet-4-5", "max_tokens": 1024, "messages": [ {"role": "user", "content": "Hello, world"} ] }' ``` ## Supported OpenAI Parameters Azure Anthropic supports the same parameters as the main Anthropic provider: ``` "stream", "stop", "temperature", "top_p", "max_tokens", "max_completion_tokens", "tools", "tool_choice", "extra_headers", "parallel_tool_calls", "response_format", "user", "thinking", "reasoning_effort" ``` :::info Azure Anthropic API requires `max_tokens` to be passed. LiteLLM automatically passes `max_tokens=4096` when no `max_tokens` are provided. ::: ## Differences from Standard Anthropic Provider The only difference between Azure Anthropic and the standard Anthropic provider is authentication: - **Standard Anthropic**: Uses `x-api-key` header - **Azure Anthropic**: Uses `api-key` header or `Authorization: Bearer ` for Azure AD authentication All other request/response transformations, tool calling, streaming, and feature support are identical. ## API Base URL Format The API base URL should follow this format: ``` https://.services.ai.azure.com/anthropic ``` LiteLLM will automatically append `/v1/messages` if not already present in the URL. ## Example: Full Configuration ```python import os from litellm import completion # Configure Azure Anthropic os.environ["AZURE_API_KEY"] = "your-azure-api-key" os.environ["AZURE_API_BASE"] = "https://my-resource.services.ai.azure.com/anthropic" # Make a request response = completion( model="azure_ai/claude-sonnet-4-5", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Explain quantum computing in simple terms."} ], max_tokens=1000, temperature=0.7, stream=False, ) print(response.choices[0].message.content) ``` ## Troubleshooting ### Missing API Base Error If you see an error about missing API base, ensure you've set: ```python os.environ["AZURE_API_BASE"] = "https://.services.ai.azure.com/anthropic" ``` Or pass it directly: ```python response = completion( model="azure_ai/claude-sonnet-4-5", api_base="https://.services.ai.azure.com/anthropic", # ... ) ``` ### Authentication Errors - **API Key**: Ensure `AZURE_API_KEY` is set or passed as `api_key` parameter - **Azure AD Token**: Ensure `AZURE_AD_TOKEN` is set or passed as `azure_ad_token` parameter - **Token Provider**: For automatic token refresh, configure `AZURE_TENANT_ID`, `AZURE_CLIENT_ID`, and `AZURE_CLIENT_SECRET` ## Related Documentation - [Anthropic Provider Documentation](../anthropic.md) - For standard Anthropic API usage - [Azure OpenAI Documentation](./azure.md) - For Azure OpenAI models - [Azure Authentication Guide](../../secret_managers/azure_key_vault.md) - For Azure AD token setup --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/azure-anthropic) · https://claudary.paisolsolutions.com