Deployment & Inference ★ 24.0k

LiteLLM

One OpenAI-shaped interface for 100+ model providers, plus a proxy that tracks spend per key.

LiteLLM normalises provider APIs behind a single SDK and ships a proxy server with budgets, rate limits and automatic fallbacks. It is the cheapest way to stop hard-coding one vendor into an application you will have to migrate later.

Best for: Multi-provider routing, spend control, fallbacks

Deploy: Self-hostable

What to do about it

  • Check licence terms
  • Verify data handling
  • Test failure behavior

What it is

LiteLLM provides a single interface to access multiple model providers. It normalises the APIs of these providers behind a single SDK. This allows developers to interact with different models in a uniform way.

The tool also includes a proxy server that tracks spend per key and provides features like budgets, rate limits, and automatic fallbacks. This helps with cost control and ensures that applications do not exceed their allocated budgets.

Where it fits in a deployment stack

Tools like LiteLLM are used in the deployment stack to manage the interaction between applications and machine learning models. They sit between the application code and the model providers, handling tasks like API normalization, cost tracking, and request routing.

In a production system, this category of tool normally takes over the work of managing multiple model providers, ensuring that the application can switch between them easily if needed. This is important for avoiding vendor lock-in and ensuring that the application can adapt to changing requirements.

These tools also provide features like observability, which helps developers understand how their applications are using the models and identify potential issues. This is critical for ensuring that the application is working correctly and efficiently.

How to evaluate it

To evaluate LiteLLM, developers need to consider how it will be used in their application and what features are most important to them. This involves testing the tool with their specific use case and verifying that it meets their requirements.

  • Feed it a dataset with varying input sizes to measure performance
  • Inject failures in the model providers to test the proxy's fallback behavior
  • Inspect the output of the SDK to verify that it is correctly normalizing the provider APIs
  • Measure the latency of the retrieval step under different concurrent session loads
  • Verify that the budget and rate limit features are correctly tracking spend per key
  • Test the tool's ability to handle multiple model providers and switch between them seamlessly

What to verify before you commit

Before committing to LiteLLM, developers need to verify several key aspects of the tool. This includes checking the licence terms, data handling practices, and operational costs. They must also ensure that the tool meets their specific requirements and can adapt to their changing needs.

  • Check the licence terms to ensure they allow for the intended use case
  • Verify that the tool handles sensitive data correctly and securely
  • Calculate the operational costs of using the tool, including any potential fees or charges
  • Test the tool's failure behavior to ensure it meets the application's requirements
  • Check for any potential lock-in or vendor-specific dependencies
  • Verify that the tool provides the necessary features and functionality for the application

Where this fits in the FDE path