LiteLLM
Open-source AI gateway to unify 100+ LLMs with cost tracking, load balancing, and logging
Visit Website ↗What is LiteLLM
LiteLLM is an open-source AI gateway that solves a practical pain point: different LLM providers have different API formats, requiring code changes when switching between them. LiteLLM allows you to use a unified OpenAI format to call over 100 providers, with minimal code changes.
It has two use cases. One is as a Python SDK, imported directly into your program; the other is as a Proxy Server (AI Gateway), deployed as a shared service for teams or organizations, with all LLM traffic routed through it. The latter provides centralized cost tracking, load balancing, and logging, eliminating the need for each project to reinvent the wheel. LiteLLM is fully open-source and developed by Y Combinator-backed BerriAI, with high acceptance in the developer community.
Key Features and Use Cases
LiteLLM's core value is "one interface to rule them all." For teams that need to switch between multiple models, perform A/B testing, or avoid being locked into a single provider, it provides an abstraction layer that makes model switching as easy as changing settings. Built-in cost tracking enables clear visibility into spending on each project and model, which is crucial in managing LLM bills.
Typical scenarios: deploying a LiteLLM Proxy as a unified entry point for multiple teams, managing keys, quotas, budgets, and logs; or using the same codebase to quickly compare different models during development. Load balancing and fault-tolerance mechanisms also make it suitable for production environments, automatically switching to backup providers when needed. Suitable for any development team that needs to manage multiple models, track costs, and maintain governance.
Key Features
- Unified OpenAI format for calling 100+ LLMs
- Deployable as Python SDK or Proxy gateway
- Built-in cost tracking, load balancing, and fault tolerance
- Supports logging and centralized governance
- Open-source, developed by YC-backed BerriAI
Pros
- Switch models without changing code, avoiding vendor lock-in
- Cost tracking provides transparent and controllable LLM bills
- Open-source with an active community, high acceptance
Cons
- Deploying Proxy and tuning require some operational expertise
- Governance features are comprehensive but have a learning curve
- Not suitable for pure front-end or non-technical users
Use Cases
- Freely switching and comparing multiple models
- Deploying a unified gateway for centralized key and budget management
- Providing a shared LLM entry point for multiple teams
- Implementing load balancing and fault tolerance in production environments
Editor's Note
In the era of multiple models, a gateway that abstracts well is almost a necessity. LiteLLM is a benchmark open-source choice, with cost tracking being particularly useful. However, enjoying governance features requires investing in deployment and maintenance. We give it 4.4 stars.
FAQ
How does LiteLLM support so many models?
It abstracts each provider's API into a unified OpenAI format, allowing you to use the same calling method, with LiteLLM translating to the corresponding provider, minimizing code changes when switching models.
What's the difference between SDK and Proxy?
SDK is imported directly into your program, while Proxy is deployed as a standalone server, serving as a shared entry point for teams, centralizing cost, quota, and log governance.