liteLLM

Product Introduction
LiteLLM is a versatile LLM Gateway that acts as an OpenAI Proxy, streamlining interactions with over 100 large language models (LLMs) while adhering to the familiar OpenAI API format. It simplifies the complexity of managing authentication, cost tracking, and performance optimization for developers leveraging AI models from diverse providers such as OpenAI, Azure, Cohere, Anthropic, Replicate, and Google. By abstracting the intricacies of multiple LLM APIs, LiteLLM ensures consistent outputs and exceptions, making it easier to integrate AI into applications. The platform also supports advanced functionalities like logging, error tracking, and budget control, enabling businesses to maintain efficiency and oversight in their AI workflows. Whether open-source or enterprise-tier, LiteLLM empowers users to focus on innovation rather than infrastructure.

Core Features
LiteLLM offers a centralized solution for LLM management with its robust feature set:

Unified API Interface: Maintain OpenAI compatibility while accessing 100+ LLMs, ensuring seamless integration without code changes.

Cost & Budget Control: Track expenses across providers in real time and set spending limits to prevent unexpected costs.

LLM Fallback Mechanism: Automatically reroute requests to alternative models if one fails, enhancing reliability and uptime.

Load Balancing & Rate Limiting: Distribute traffic efficiently among models and enforce API rate limits to optimize performance.

Prompt Management: Store and reuse prompts, with guardrails to filter inappropriate content and ensure responsible AI usage.

Comprehensive Logging: Capture all model activity in S3-compatible storage for auditability and troubleshooting.

Error Tracking & Observability: Monitor exceptions and model behavior to identify issues and improve system stability.

Customizable Pass-Through Endpoints: Retain full flexibility by allowing direct API calls to specific providers when needed.

Use Cases
LiteLLM excels in scenarios where managing multiple LLMs is critical:

Developer Workflows: Provide teams access to a broad range of models without rewriting code for each provider.

Cost Optimization: Compare and control expenses across LLM services to maximize ROI in business applications.

High Availability Systems: Implement fallbacks and load balancing to ensure critical apps remain functional during model outages.

Enterprise AI Governance: Enforce usage policies, track model performance, and standardize API access for large organizations.

Prompt Libraries: Build and manage reusable prompts for consistent responses in customer support, content creation, and automation.

FAQ 1. How does LiteLLM simplify using multiple LLMs?
LiteLLM unifies diverse LLM APIs under a single interface (e.g., completion(model, messages)), eliminating the need to handle provider-specific authentication, endpoints, or formats. Developers can switch between models effortlessly while maintaining consistent outputs.