LiteLLM
Open-source LLM gateway providing unified access to 100+ language models through a standardized OpenAI-compatible interface.
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Product Overview
What is LiteLLM?
LiteLLM is a comprehensive LLM gateway solution that simplifies access to over 100 language models from various providers including OpenAI, Anthropic, Azure, Bedrock, VertexAI, and more. It standardizes all interactions through an OpenAI-compatible format, eliminating the need for provider-specific code. The platform offers both an open-source Python SDK and a proxy server (LLM Gateway) that handles input translation, consistent output formatting, and advanced features like spend tracking, budgeting, and fallback mechanisms. Trusted by companies like Netflix, Lemonade, and RocketMoney, LiteLLM enables teams to rapidly integrate new models while maintaining robust monitoring and control over LLM usage.
Key Features
Universal Model Access
Standardized access to 100+ LLMs from major providers including OpenAI, Anthropic, Azure, Bedrock, and more, all through a consistent OpenAI-compatible interface.
Comprehensive Spend Management
Built-in tracking, budgeting, and rate limiting capabilities that can be configured per project, API key, or model to maintain control over LLM costs.
Robust Reliability Features
Advanced retry and fallback logic across multiple LLM deployments, ensuring application resilience even when primary models are unavailable.
Enterprise-Grade Observability
Extensive logging and monitoring capabilities with integrations to popular tools like Prometheus, Langfuse, OpenTelemetry, and cloud storage options.
Flexible Deployment Options
Available as both a Python SDK for direct integration and a proxy server for organization-wide deployment, with Docker support for containerized environments.
Use Cases
- Enterprise LLM Infrastructure : Platform teams can provide developers with controlled, day-zero access to the latest LLM models while maintaining governance over usage and costs.
- Multi-Model Applications : Developers can build applications that leverage multiple LLMs for different tasks without implementing provider-specific code for each model.
- Cost-Optimized AI Systems : Organizations can implement intelligent routing between premium and cost-effective models based on task requirements and budget constraints.
- High-Availability AI Services : Critical AI applications can maintain uptime through automatic fallbacks across different providers when primary models experience outages.
- Centralized LLM Governance : Security and compliance teams can implement consistent authentication, logging, and usage policies across all LLM interactions within an organization.
FAQs
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