LiteLLM: A Unified Gateway for 100+ AI Language Models

If you’re building applications with Large Language Models (LLMs), you’ve probably experienced the pain of switching between different providers. Each has their own SDK, authentication method, error handling, and API quirks. Want to test Claude from Anthropic alongside GPT-4 from OpenAI? Get ready to manage multiple libraries, different response formats, and incompatible code patterns.

Enter LiteLLM—an open-source library that solves this problem elegantly by providing a unified OpenAI-compatible interface for calling over 100 different LLM providers.

What is LiteLLM?

LiteLLM is both a Python SDK and an AI gateway (proxy server) that enables you to interact with models from OpenAI, Anthropic, AWS Bedrock, Azure, Google, and many others through a single, consistent API.¹ The key insight: most developers are already familiar with OpenAI’s API format, so why not use that everywhere?

The library comes in two main components:

1. Python SDK

The Python SDK allows direct integration into your applications:

from litellm import completion
import os

# Call OpenAI
response = completion(
  model="openai/gpt-4o",
  messages=[{"content": "Hello!", "role": "user"}]
)

# Switch to Anthropic with the same code
response = completion(
  model="anthropic/claude-3-opus",
  messages=[{"content": "Hello!", "role": "user"}]
)

Install it with: pip install litellm

2. Proxy Server (AI Gateway)

The proxy server provides a centralized gateway for enterprise use cases:

pip install 'litellm[proxy]'
litellm --model gpt-3.5-turbo

This launches an OpenAI-compatible API endpoint that routes requests to any configured provider. Think of it as a reverse proxy specifically designed for LLM APIs.²

Why Multi-Provider Flexibility Matters

The AI landscape is evolving rapidly. Models that are state-of-the-art today might be surpassed tomorrow. Providers change their pricing, rate limits, and feature sets. Here’s why keeping your options open matters:

Avoid Vendor Lock-In: By abstracting away provider-specific code, you can switch between OpenAI, Anthropic, or AWS Bedrock with a single line configuration change rather than a major refactor.

Cost Optimization: Different providers have different pricing structures. LiteLLM even supports cost-based routing to automatically select the cheapest available provider for your use case.³

Reliability Through Diversity: Configure multiple deployments and use load balancing strategies like “least-busy” or “latency-based-routing” to ensure your application stays responsive even if one provider has issues.

Experiment Freely: Testing multiple models to find the best fit for your use case becomes trivial when you don’t need to rewrite integration code each time.

Getting Started in Minutes

Let’s walk through a quick setup to demonstrate how straightforward LiteLLM is:

Basic Python SDK Usage

from litellm import completion
import os

# Set your API keys
os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"

# Call any model with the same interface
models = ["gpt-4", "claude-3-opus", "gemini-pro"]

for model in models:
    response = completion(
        model=model,
        messages=[{"role": "user", "content": "Explain LLMs in one sentence."}]
    )
    print(f"{model}: {response.choices[0].message.content}")

That’s it. Same code, three different providers.

Setting Up the Proxy Server

For team environments or production deployments, the proxy server approach offers centralized management:

1. Create a configuration file (config.yaml):

model_list:
  - model_name: gpt-4
    litellm_params:
      model: openai/gpt-4
      api_key: os.environ/OPENAI_API_KEY

  - model_name: claude-3
    litellm_params:
      model: anthropic/claude-3-opus
      api_key: os.environ/ANTHROPIC_API_KEY

router_settings:
  routing_strategy: simple-shuffle

2. Launch the proxy:

docker run \
  -v $(pwd)/config.yaml:/app/config.yaml \
  -e OPENAI_API_KEY \
  -e ANTHROPIC_API_KEY \
  -p 4000:4000 \
  docker.litellm.ai/berriai/litellm:main-stable

3. Use it like the OpenAI API:

import openai

client = openai.OpenAI(
    api_key="your-litellm-key",
    base_url="http://localhost:4000"
)

response = client.chat.completions.create(
    model="gpt-4",  # Or claude-3, configured in your YAML
    messages=[{"role": "user", "content": "Hello!"}]
)

Your application now talks to LiteLLM’s proxy, which routes to the actual provider. Swap providers by editing the YAML file—no code changes needed.

Supported Providers

LiteLLM supports an impressive array of providers⁴:

Major Cloud Providers:

• OpenAI
• Azure OpenAI
• AWS Bedrock
• Google Vertex AI & AI Studio
• Anthropic

Specialized Providers:

• Groq (ultra-fast inference)
• Mistral AI
• Perplexity AI
• Together AI
• HuggingFace
• Ollama (local models)
• OpenRouter
• Replicate

This extensive coverage means you can experiment with cutting-edge models from startups while maintaining the option to fall back to established providers for production workloads.

Enterprise-Grade Features

While the simple use cases are compelling, LiteLLM really shines when you need enterprise features:

Cost Tracking and Budget Management

Track spending across all your LLM usage with granular detail:

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Analyze this..."}],
    user="customer-123",
    extra_body={
        "metadata": {
            "tags": ["project:analytics", "team:data-science"]
        }
    }
)

LiteLLM automatically calculates costs and provides APIs to query spending by user, team, project, or custom tags.⁵

Virtual Keys and Authentication

Generate API keys with fine-grained controls:

• Specify which models each key can access
• Set spending limits per key
• Configure expiration dates
• Enable/disable keys dynamically

This is perfect for multi-tenant applications or giving team members controlled access to LLM APIs.

Load Balancing Strategies

Configure how requests are distributed across multiple deployments:

• simple-shuffle: Random distribution
• least-busy: Route to the deployment with fewest active requests
• latency-based-routing: Send requests to the fastest responding endpoint
• cost-based-routing: Automatically choose the cheapest provider

Error Handling

LiteLLM maps provider-specific exceptions to OpenAI-style errors, giving you consistent error handling:

import litellm

try:
    response = litellm.completion(
        model="gpt-4",
        messages=[{"role": "user", "content": "hello"}],
        timeout=0.01
    )
except openai.APITimeoutError as e:
    print(f"Timeout: {e}")
except openai.RateLimitError as e:
    print(f"Rate limited: {e}")

Same exception types work across all 100+ providers.⁶

Deployment Options

LiteLLM is designed to run anywhere:

Docker: Official images available at docker.litellm.ai/berriai/litellm

Kubernetes: Helm charts provided for cluster deployments

Cloud Platforms: Deploy on AWS ECS/EKS, Google Cloud Run, Railway, or Render

Production Requirements: Minimum 4 CPU cores and 8 GB RAM recommended. PostgreSQL for virtual keys and cost tracking, Redis for rate limiting and multi-instance coordination.⁷

Real-World Use Cases

Here are some practical scenarios where LiteLLM excels:

1. Development and Testing: Quickly prototype against multiple models to find the best fit for your use case without rewriting integration code.

2. Cost Optimization: Route traffic to the most cost-effective provider for each request type. Use expensive flagship models only when necessary.

3. Reliability: Implement automatic failover between providers. If OpenAI has an outage, seamlessly route to Anthropic or Azure.

4. Access Control: Centralize LLM access for your organization. Manage who can use which models, track spending, and enforce budgets.

5. Compliance: Keep data in specific regions by routing to appropriate cloud providers based on user location or data classification.

Considerations and Trade-offs

While LiteLLM is powerful, it’s worth considering:

Additional Layer: You’re adding another component to your stack. For simple single-provider use cases, this might be overkill.

Provider-Specific Features: Some providers offer unique features (like OpenAI’s function calling or Anthropic’s extended context windows) that might not map perfectly across all providers through a unified interface.

Latency: Proxying requests adds a small amount of latency. LiteLLM claims 8ms P95 latency at 1000 RPS, which is negligible for most applications.⁸

Learning Curve: While the basics are simple, mastering enterprise features like virtual keys, cost tracking, and advanced routing takes time.

Getting Help and Contributing

LiteLLM is actively developed and open-source:

• Documentation: docs.litellm.ai
• GitHub: github.com/BerriAI/litellm
• Issues and Discussions: Active community on GitHub

The project welcomes contributions and has responsive maintainers.

Conclusion

LiteLLM solves a real problem: the complexity of managing multiple LLM providers. By providing a unified OpenAI-compatible interface, it lets you focus on building features rather than wrestling with integration code.

Whether you’re a solo developer experimenting with different models or an enterprise team managing LLM access for hundreds of users, LiteLLM offers a compelling solution. The ability to switch providers with a configuration change—rather than a code rewrite—provides flexibility that’s increasingly valuable in the fast-moving AI landscape.

Start simple with the Python SDK for local development, then graduate to the proxy server when you need enterprise features. Either way, you’re one pip install away from unified access to 100+ LLM providers.

References