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LiteLLM

Freemium
llmllmopsopen sourcepythondeveloper toolsapi gatewayopenai proxyai

An open-source library that simplifies LLM integration by providing a unified, standardized API call format for over 100+ models, enabling seamless switching, fallbacks, and robust error handling for developers.

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LiteLLM is a powerful open-source library designed to standardize interactions with a vast array of Large Language Models. It serves as a universal adapter, allowing developers to call over 100 different LLMs from providers like OpenAI, HuggingFace, and Anthropic using a single, consistent format. This abstraction layer significantly simplifies development, enabling easy model switching for cost optimization, performance testing, or provider fallback. Its key value lies in creating resilient, production-ready AI applications by managing API inconsistencies, standardizing exception handling, and providing features like automatic retries and logging. For teams building on generative AI, LiteLLM reduces vendor lock-in and accelerates the development lifecycle.

Pros

  • Supports over 100 LLMs from various providers via a single, consistent API format, reducing vendor lock-in.
  • Built-in production-ready features like automatic retries, model fallbacks, and timeouts.
  • Provides detailed logging and callbacks for tracking cost, usage, and performance per API key or model.
  • Functions as a self-hostable OpenAI-compatible proxy server for streamlined infrastructure and key management.
  • Open-source (MIT license) and highly extensible, allowing for complete control and customization.

Cons

  • As a rapidly evolving project, it can have breaking changes between versions, requiring careful dependency management.
  • The learning curve can be steep for advanced configurations like custom routing logic or complex authentication.
  • While it unifies the API call, developers still need to understand the unique capabilities and pricing of each underlying model.
  • The hosted version can introduce a central point of failure if not deployed in a highly available configuration.

Key features

  • Unified `completion()` function to call any supported LLM.
  • Support for streaming responses across all models.
  • Self-hostable OpenAI Proxy Server.
  • Automatic cost and usage tracking.
  • Built-in caching to reduce latency and API costs.
  • Consistent error handling and exception mapping.
  • Support for local LLMs via Ollama and other OpenAI-compatible endpoints.

Integrations

OpenAI / Azure OpenAIAnthropic (Claude Models)Google (Gemini, Vertex AI)CohereAmazon BedrockHugging FaceOllamaTogether AIGroqAnyscale Endpoints

Target audience

AI/ML engineers, software developers, and engineering teams building applications that leverage multiple Large Language Models for tasks like A/B testing, cost optimization, and provider redundancy.

Ratings & Reviews

0.0

Based on 0 reviews

Key Metrics

Active Users

3M+ monthly downloads

Founded

2023

Headquarters

San Francisco, USA

Pricing Tiers

Open Source Library

The core Python library, self-hostable. Unlimited usage, requires your own infrastructure and maintenance.

Free

Hosted - Free

Managed LiteLLM Proxy. Includes 10,000 requests/month, 1 project, 25 virtual keys, and community support.

$0/mo

Hosted - Pro

Managed LiteLLM Proxy for growing teams. Includes 100,000 requests/month, 5 projects, unlimited virtual keys, and email support.

$250/mo

Self-Hosted - Enterprise

For large-scale deployments. Includes unlimited requests, SSO, SLA, a dedicated support channel, and advanced security features.

Custom

Frequently Asked Questions

Top Alternatives to LiteLLM

Portkey

A commercial LLM gateway focused heavily on observability, prompt management, and an enterprise UI, while LiteLLM is open-source first and more developer-centric.

Native LLM SDKs

Using a provider's official SDK (e.g., OpenAI Python SDK) offers the most direct integration but locks you into their ecosystem and lacks LiteLLM's model-agnostic flexibility.

LangChain

A broader AI orchestration framework for building complex agentic workflows, whereas LiteLLM specializes in being a lightweight, universal API translation layer for LLM calls.

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