Unified API

Overview

The Unified API standardises how your AI applications interact with leading AI models. It exposes a single OpenAI compatible endpoint for accessing 7 major AI providers eliminating vendor lock-in and enabling easy switching between providers. You can use the familiar OpenAI API format, request structure, and response schema regardless of the underlying provider. This approach delivers:

Vendor Independence

Swap providers instantly without code changes. Avoid vendor lock in and maintain flexibility in your AI strategy.

Reduced Development Cost

Integrate once and access many models. Minimise engineering effort and lower ongoing maintenance costs.

Operational Simplicity

Centralised management and monitoring for all AI providers. Streamline operations and simplify troubleshooting.

Strategic Flexibility

Leverage each provider’s unique strengths. Easily adapt to new capabilities and optimise for your use cases.

By adopting the Unified API, organisations can focus on building innovative AI applications rather than managing complex integrations, ultimately accelerating their AI transformation journey while maintaining full control over their AI strategy.

How it Works

Supported Providers

Provider	Description
OpenAI	Access OpenAI’s GPT models made available by OpenAI API
Anthropic	Connect to Anthropic’s Claude models available in Anthropic API
Amazon Bedrock	Integrate AWS Bedrock models Claude, Llama, Titan etc. available in AWS Bedrock API
Azure OpenAI	Access Azure hosted OpenAI models available in Azure OpenAI API
Azure AI Inference	Leverage custom Azure AI Inference models available in Azure AI Inference API
Google AI	Integrate Google Gemini models available in Google AI API
Google Vertex AI	Access custom deployed Vertex AI models available in Google Vertex Model Garden

Implementation Examples (for Each Provider)

All examples use the OpenAI SDK with provider specific headers to route requests through the AI Gateway.

OpenAI

Access OpenAI’s GPT models made available by OpenAI API

openai_example.py

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_OPENAI_API_KEY",
    base_url="https://gateway.altrum.ai/v1",
    default_headers={
        "x-provider-name": "openai",
        "x-altrumai-key": "YOUR_ALTRUMAI_API_KEY"
    }
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "What is the capital of France?"}
    ]
)

print(response.choices[0].message.content)

curl -X POST "https://gateway.altrum.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_OPENAI_API_KEY" \
  -H "x-provider-name: openai" \
  -H "x-altrumai-key: YOUR_ALTRUMAI_API_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      {
        "role": "user",
        "content": "What is the capital of France?"
      }
    ]
  }'

Anthropic

Access Anthropic Claude models made available by Anthropic API

anthropic_example.py

from openai import OpenAI

client = OpenAI(
    api_key="",
    base_url="https://gateway.altrum.ai/v1",
    default_headers={
        "x-api-key": "YOUR_ANTHROPIC_API_KEY",
        "x-provider-name": "anthropic",
        "x-altrumai-key": "YOUR_ALTRUMAI_API_KEY"
    }
)

response = client.chat.completions.create(
    model="claude-3-opus-20240229",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ]
)

print(response.choices[0].message.content)

curl -X POST "https://gateway.altrum.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer " \
  -H "x-api-key: YOUR_ANTHROPIC_API_KEY" \
  -H "x-provider-name: anthropic" \
  -H "x-altrumai-key: YOUR_ALTRUMAI_API_KEY" \
  -d '{
    "model": "claude-3-opus-20240229",
    "messages": [
      {
        "role": "user",
        "content": "Explain quantum computing in simple terms."
      }
    ]
  }'

Amazon Bedrock

Access Amazon Bedrock models made available by AWS Bedrock API

bedrock_example.py

from openai import OpenAI

client = OpenAI(
    api_key="unused-placeholder",  # Not used, credentials via headers
    base_url="https://gateway.altrum.ai/v1",
    default_headers={
        "x-provider-name": "bedrock",
        "x-bedrock-access-key-id": "YOUR_AWS_ACCESS_KEY_ID",
        "x-bedrock-secret-access-key": "YOUR_AWS_SECRET_ACCESS_KEY",
        "x-bedrock-region": "YOUR_AWS_REGION",
        "x-bedrock-session-token": "YOUR_AWS_SESSION_TOKEN",  # Optional
        "x-altrumai-key": "YOUR_ALTRUMAI_API_KEY"
    }
)

response = client.chat.completions.create(
    model="anthropic.claude-v2",
    messages=[
        {"role": "user", "content": "Give me a list of 5 creative startup ideas in the AI space."}
    ]
)

print(response.choices[0].message.content)

curl -X POST "https://gateway.altrum.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-provider-name: bedrock" \
  -H "x-bedrock-access-key-id: YOUR_AWS_ACCESS_KEY_ID" \
  -H "x-bedrock-secret-access-key: YOUR_AWS_SECRET_ACCESS_KEY" \
  -H "x-bedrock-region: YOUR_AWS_REGION" \
  -H "x-bedrock-session-token: YOUR_AWS_SESSION_TOKEN" \
  -H "x-altrumai-key: YOUR_ALTRUMAI_API_KEY" \
  -d '{
    "model": "anthropic.claude-v2",
    "messages": [
      {
        "role": "user",
        "content": "Give me a list of 5 creative startup ideas in the AI space."
      }
    ]
  }'

Azure OpenAI

Access Azure OpenAI models made available by Azure OpenAI API

azure_openai_example.py

from openai import OpenAI

client = OpenAI(
    api_key="unused-placeholder",  # Authentication via headers
    base_url="https://gateway.altrum.ai/v1",
    default_headers={
        "x-provider-name": "azure_openai",
        "x-azure-api-key": "YOUR_AZURE_API_KEY",
        "x-azure-resource-name": "YOUR_AZURE_RESOURCE_NAME",
        "x-azure-deployment-id": "YOUR_AZURE_DEPLOYMENT_ID",
        "x-azure-api-version": "2024-02-15-preview",
        "x-altrumai-key": "YOUR_ALTRUMAI_API_KEY"
    }
)

response = client.chat.completions.create(
    model="gpt-35-turbo",
    messages=[
        {"role": "user", "content": "What are the advantages of using managed Kubernetes services?"}
    ]
)

print(response.choices[0].message.content)

curl -X POST "https://gateway.altrum.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-provider-name: azure_openai" \
  -H "x-azure-api-key: YOUR_AZURE_API_KEY" \
  -H "x-azure-resource-name: YOUR_AZURE_RESOURCE_NAME" \
  -H "x-azure-deployment-id: YOUR_AZURE_DEPLOYMENT_ID" \
  -H "x-azure-api-version: 2024-02-15-preview" \
  -H "x-altrumai-key: YOUR_ALTRUMAI_API_KEY" \
  -d '{
    "model": "gpt-35-turbo",
    "messages": [
      {
        "role": "user",
        "content": "What are the advantages of using managed Kubernetes services?"
      }
    ]
  }'

Azure AI Inference

Access Azure AI Inference models made available by Azure AI Inference API

azure_ai_inference_example.py

from openai import OpenAI

client = OpenAI(
    api_key="unused-placeholder",  # Authentication via headers
    base_url="https://gateway.altrum.ai/v1",
    default_headers={
        "x-provider-name": "azure_ai_inference",
        "x-altrumai-key": "YOUR_ALTRUMAI_API_KEY",
        "x-azure-ai-token": "YOUR_AZURE_AI_INFERENCE_API_KEY",
        "x-azure-ai-endpoint": "YOUR_AZURE_AI_INFERENCE_ENDPOINT"
    }
)

response = client.chat.completions.create(
    model="phi-2",
    messages=[
        {"role": "user", "content": "Summarise the main benefits of using serverless architectures."}
    ]
)

print(response.choices[0].message.content)

curl -X POST "https://gateway.altrum.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-provider-name: azure_ai_inference" \
  -H "x-altrumai-key: YOUR_ALTRUMAI_API_KEY" \
  -H "x-azure-ai-token: YOUR_AZURE_AI_INFERENCE_API_KEY" \
  -H "x-azure-ai-endpoint: YOUR_AZURE_AI_INFERENCE_ENDPOINT" \
  -d '{
    "model": "phi-2",
    "messages": [
      {
        "role": "user",
        "content": "Summarise the main benefits of using serverless architectures."
      }
    ]
  }'

Google AI

Access Google Gemini models made available by Google AI API

google_ai_example.py

from openai import OpenAI

client = OpenAI(
    api_key="unused-placeholder",  # Authentication via headers
    base_url="https://gateway.altrum.ai/v1",
    default_headers={
        "x-provider-name": "google",
        "x-goog-api-key": "YOUR_GOOGLE_API_KEY",
        "x-altrumai-key": "YOUR_ALTRUMAI_API_KEY"
    }
)

response = client.chat.completions.create(
    model="gemini-1.5-pro",
    messages=[
        {"role": "user", "content": "How can AI help improve energy efficiency in smart buildings?"}
    ]
)

print(response.choices[0].message.content)

curl -X POST "https://gateway.altrum.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-provider-name: google" \
  -H "x-goog-api-key: YOUR_GOOGLE_API_KEY" \
  -H "x-altrumai-key: YOUR_ALTRUMAI_API_KEY" \
  -d '{
    "model": "gemini-1.5-pro",
    "messages": [
      {
        "role": "user",
        "content": "How can AI help improve energy efficiency in smart buildings?"
      }
    ]
  }'

Google Vertex AI

Access Google Vertex AI models made available by Google Vertex AI API

google_vertex_ai_example.py

from openai import OpenAI

client = OpenAI(
    api_key="unused-placeholder",  # Authentication via headers
    base_url="https://gateway.altrum.ai/v1",
    default_headers={
        "x-provider-name": "google_vertex_ai",
        "x-api-key": "YOUR_GOOGLE_VERTEX_AI_API_KEY",
        "x-endpoint-base": "YOUR_GOOGLE_VERTEX_AI_ENDPOINT_BASE",
        "x-project-id": "YOUR_GOOGLE_VERTEX_AI_PROJECT_ID",
        "x-location": "YOUR_GOOGLE_VERTEX_AI_LOCATION",
        "x-altrumai-key": "YOUR_ALTRUMAI_API_KEY"
    }
)

response = client.chat.completions.create(
    model="google.models.text-bison",
    messages=[
        {"role": "user", "content": "List three practical applications of generative AI in education."}
    ]
)

print(response.choices[0].message.content)

curl -X POST "https://gateway.altrum.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-provider-name: google_vertex_ai" \
  -H "x-api-key: YOUR_GOOGLE_VERTEX_AI_API_KEY" \
  -H "x-endpoint-base: YOUR_GOOGLE_VERTEX_AI_ENDPOINT_BASE" \
  -H "x-project-id: YOUR_GOOGLE_VERTEX_AI_PROJECT_ID" \
  -H "x-location: YOUR_GOOGLE_VERTEX_AI_LOCATION" \
  -H "x-altrumai-key: YOUR_ALTRUMAI_API_KEY" \
  -d '{
    "model": "google.models.text-bison",
    "messages": [
      {
        "role": "user",
        "content": "List three practical applications of generative AI in education."
      }
    ]
  }'

Request Metadata

The Unified API supports an optional metadata field in requests to enrich request context with user, team, and tenant information. This metadata enables enhanced tracking, reporting, and governance by associating requests with organisational context.

Metadata Structure

The metadata object supports the following structure:

{
  "metadata": {
    "user": {
      "user_id": "string (max 64)",
      "user_name": "string (max 128)",
      "user_email": "string (valid email)",
      "user_role": "string (max 64)",
      "user_type": "internal | external"
    },
    "team": {
      "team_id": "string (max 64)",
      "team_name": "string (max 128)"
    },
    "tenant": {
      "tenant_id": "string (max 64)",
      "tenant_name": "string (max 128)",
      "tenant_user": {
        "tenant_user_id": "string (max 64)",
        "tenant_user_name": "string (max 128)",
        "tenant_user_email": "string (valid email)",
        "tenant_user_role": "string (max 64)"
      }
    }
  }
}

Using Metadata in Requests

Metadata can be included in any API request. Here’s an example:

metadata_example.py

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_OPENAI_API_KEY",
    base_url="https://gateway.altrum.ai/v1",
    default_headers={
        "x-provider-name": "openai",
        "x-altrumai-key": "YOUR_ALTRUMAI_API_KEY"
    }
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "What is the capital of France?"}
    ],
    metadata={
        "user": {
            "user_id": "user_123",
            "user_name": "John Doe",
            "user_email": "john.doe@example.com",
            "user_role": "developer",
            "user_type": "internal"
        },
        "team": {
            "team_id": "team_456",
            "team_name": "Engineering"
        },
        "tenant": {
            "tenant_id": "tenant_789",
            "tenant_name": "Acme Corp",
            "tenant_user": {
                "tenant_user_id": "tenant_user_123",
                "tenant_user_name": "John Doe",
                "tenant_user_email": "john.doe@acme.com",
                "tenant_user_role": "admin"
            }
        }
    }
)

print(response.choices[0].message.content)

curl -X POST "https://gateway.altrum.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_OPENAI_API_KEY" \
  -H "x-provider-name: openai" \
  -H "x-altrumai-key: YOUR_ALTRUMAI_API_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      {
        "role": "user",
        "content": "What is the capital of France?"
      }
    ],
    "metadata": {
      "user": {
        "user_id": "user_123",
        "user_name": "John Doe",
        "user_email": "john.doe@example.com",
        "user_role": "developer",
        "user_type": "internal"
      },
      "team": {
        "team_id": "team_456",
        "team_name": "Engineering"
      },
      "tenant": {
        "tenant_id": "tenant_789",
        "tenant_name": "Acme Corp",
        "tenant_user": {
          "tenant_user_id": "tenant_user_123",
          "tenant_user_name": "John Doe",
          "tenant_user_email": "john.doe@acme.com",
          "tenant_user_role": "admin"
        }
      }
    }
  }'

All metadata fields are optional. You can include only the metadata fields relevant to your use case. The metadata field works with all supported providers.

Introduction

Product

Release Notes

Overview

Vendor Independence

Reduced Development Cost

Operational Simplicity

Strategic Flexibility

How it Works

Supported Providers

Implementation Examples (for Each Provider)

OpenAI

Anthropic

Amazon Bedrock

Azure OpenAI

Azure AI Inference

Google AI

Google Vertex AI

Request Metadata

Metadata Structure

Using Metadata in Requests

​Overview

Vendor Independence

Reduced Development Cost

Operational Simplicity

Strategic Flexibility

​How it Works

​Supported Providers

​Implementation Examples (for Each Provider)

​OpenAI

​Anthropic

​Amazon Bedrock

​Azure OpenAI

​Azure AI Inference

​Google AI

​Google Vertex AI

​Request Metadata

​Metadata Structure

​Using Metadata in Requests

Overview

How it Works

Supported Providers

Implementation Examples (for Each Provider)

OpenAI

Anthropic

Amazon Bedrock

Azure OpenAI

Azure AI Inference

Google AI

Google Vertex AI

Request Metadata

Metadata Structure

Using Metadata in Requests