LiteLLM: The Open-Source AI Gateway for 100+ LLM Providers

The rapid proliferation of large language model (LLM) providers has created a new challenge for developers: each provider has its own API format, authentication method, pricing model, and feature set. Integrating with multiple providers – or even switching between them – traditionally required rewriting substantial amounts of integration code. LiteLLM solves this problem by providing a unified, OpenAI-compatible interface that works with over 100 LLM providers.

Developed by BerriAI, LiteLLM has become one of the most widely adopted tools in the AI infrastructure ecosystem. It serves dual roles: as a lightweight Python SDK for programmatic access, and as a proxy server (AI Gateway) that can be deployed as a central routing layer for teams and organizations.

The project’s growth has been remarkable, driven by the practical reality that most production AI systems need to interact with multiple LLM providers – whether for redundancy, cost optimization, or accessing model-specific capabilities. LiteLLM reduces this multi-provider complexity to a single, consistent API call.

How Does LiteLLM’s Unified API Model Work?

LiteLLM’s core abstraction is deceptively simple: it provides a single completion() function that accepts a standardized set of parameters and returns a standardized response object, regardless of which underlying provider serves the request.

graph LR
    A[Your Application] --> B[LiteLLM SDK / Proxy]
    B --> C[OpenAI]
    B --> D[Anthropic]
    B --> E[Google Gemini]
    B --> F[Mistral / Together]
    B --> G[Open-Source via\nOllama/vLLM/TGI]
    B --> H[70+ more providers\nvia OpenRouter / Bedrock]
    C --> I[Standardized Response]
    D --> I
    E --> I
    F --> I
    G --> I
    H --> I

from litellm import completion

# Same function, different models -- just change the string
response = completion(
    model="claude-3-5-sonnet-20241022",
    messages=[{"role": "user", "content": "Hello!"}]
)
# or: model="gpt-4o"
# or: model="gemini/gemini-1.5-pro"
# or: model="mistral/mistral-large-latest"
# or: model="ollama/llama3"

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

Under the hood, LiteLLM handles provider-specific authentication, request formatting, streaming (SSE), error handling and retries, token counting, and response normalization. The response object follows the OpenAI format consistently, making it trivial to switch providers without changing your application code.

What Providers and Models Does LiteLLM Support?

LiteLLM’s provider support is among the most extensive of any integration library, spanning cloud APIs, managed services, and self-hosted model servers.

The list grows continuously as new providers enter the market. LiteLLM’s community actively contributes provider implementations, and the BerriAI team maintains compatibility with each provider’s evolving API.

What Features Does the LiteLLM Proxy (AI Gateway) Provide?

Beyond the SDK, LiteLLM’s proxy mode is a full-featured AI Gateway that can be deployed as a central routing and management layer for all LLM usage in an organization.

The proxy can be deployed as a Docker container or Kubernetes service, making it easy to integrate into existing infrastructure. Many organizations deploy it as the sole entry point for all LLM calls, enabling centralized governance without modifying application code.

# Start the LiteLLM proxy
docker run -p 4000:4000 \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
  ghcr.io/berriai/litellm:main-latest \
  --config ./proxy_config.yaml

# Now use it like OpenAI
curl http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-5-sonnet-20241022",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

How Do Companies Use LiteLLM in Production?

Production usage patterns vary widely, but common deployment architectures reveal how LiteLLM fits into the AI infrastructure stack.

A typical enterprise deployment pattern involves the LiteLLM proxy sitting between the application layer and upstream LLM providers. Developers interact with the proxy using the standard OpenAI SDK, experiencing a consistent API regardless of which provider ultimately handles the request. The operations team manages provider keys, cost limits, and rate limits through the proxy configuration without requiring application changes.

FAQ

What is LiteLLM? LiteLLM is an open-source Python SDK and AI Gateway developed by BerriAI that provides a unified interface for calling over 100 large language model providers. It standardizes inputs and outputs across different providers, supports cost tracking and budgeting, offers load balancing across models and keys, and can be deployed as a proxy server with an OpenAI-compatible API.

How does LiteLLM’s unified API work? LiteLLM translates between a standardized input format and each provider’s native API. You specify the model name (e.g., claude-3-5-sonnet-20241022), and LiteLLM handles authentication, request formatting, streaming, error handling, and response parsing. This means you can switch between providers by changing a single string parameter.

Does LiteLLM support cost tracking and budget management? Yes, LiteLLM has built-in cost tracking that logs token usage and associated costs for every request. It supports per-user and per-key spending limits, budget alerts, and automatic request blocking when budgets are exceeded. Cost data can be exported to monitoring tools like Datadog, Prometheus, or custom webhooks.

Can LiteLLM be deployed as a proxy server? Yes, LiteLLM can be deployed as a proxy server that exposes an OpenAI-compatible API endpoint. This proxy handles authentication, rate limiting, load balancing, caching, and logging for all upstream providers. It can be deployed via Docker, Kubernetes, or directly on a VM, making it suitable for both team use and production environments.

Is LiteLLM suitable for production use? Yes, LiteLLM is used in production by numerous companies. It supports high availability through load balancing across multiple API keys and providers, caching to reduce costs and latency, rate limiting to prevent abuse, detailed logging for audit trails, and can handle thousands of requests per minute in proxy mode.

Further Reading

• LiteLLM GitHub Repository – Source code, documentation, and community
• LiteLLM Proxy Documentation – Deployment guides and proxy configuration
• OpenAI API Reference – Reference for LiteLLM’s standardized output format
• Docker Hub: litellm – Official Docker images for proxy deployment