LiteLLM is an open-source Python library and proxy server that provides a unified API to interact with over 100 large language model (LLM) providers. It abstracts different providers' APIs into a consistent OpenAI API-like format, allowing developers to switch models or providers with minimal code changes and manage multiple deployments with retries, fallbacks, and cost tracking.

What are the core features of LiteLLM?

The core features of LiteLLM include a unified API to call over 100 LLMs using OpenAI-compatible input/output formats, support for text completion, embeddings, and image generation endpoints, and retry and fallback logic across multiple AI providers such as Azure and OpenAI. It also offers spend tracking, budgeting per project, and rate limiting, along with a central proxy server (LLM Gateway) for load balancing and centralized access management. Furthermore, LiteLLM integrates with observability and logging tools like Lunary, MLFlow, LangFuse, and Helicone, and supports streaming responses and batch requests.

How do I start using LiteLLM?

To start using LiteLLM, you need to install the Python SDK from the GitHub repository. Afterward, use the completion function to make chat or text completion requests through various models by specifying the model name with the provider prefix, for example, openai/gpt-3.5-turbo or vercel_ai_gateway/openai/gpt-4o. You can also run LiteLLM as a local proxy server or integrate it with cloud platforms to enable centralized management and enterprise features.

Which providers does LiteLLM support?

LiteLLM integrates with a wide range of providers including OpenAI, Azure OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Ollama, HuggingFace, and others, all normalized to OpenAI API formats for chat completions, embeddings, and other endpoint types.

Can LiteLLM be used with AI frameworks like LangChain or LlamaIndex?

Yes, LiteLLM can serve as the LLM provider for frameworks like LangChain and LlamaIndex to leverage its unified API and routing capabilities.

How does LiteLLM handle security and API keys?

LiteLLM handles security and API keys by securely storing provider API keys (master keys) encrypted with a user-provided salt key, LITELLM_SALT_KEY. Data in transit is encrypted using TLS/SSL in both the open-source and enterprise versions. The proxy architecture is also designed with security best practices to safely manage multiple provider keys.

How can I deploy LiteLLM?

LiteLLM can be deployed as a self-hosted proxy server using Docker, Helm, or Terraform, integrating with PostgreSQL as the backend database for cost tracking and configuration. It supports multi-cloud and multi-model routing scenarios for enterprise-scale usage.

The LiteLLM open-source project is free to use. It is designed as an abstraction layer, so you pay for the underlying API usage with individual LLM providers. Enterprise versions may offer additional features and support.

Can I perform streaming responses with LiteLLM?

Yes, LiteLLM supports streaming output from models to handle token-by-token generation efficiently.

Are there any usage limits or cropping on request lengths?

This depends on the underlying LLM provider's constraints rather than LiteLLM itself, as it acts as a proxy routing requests.

LiteLLM Review 2025 | AI Infrastructure & MLOps Tool

About LiteLLM

Who It's For

This tool is for developers and teams who want to easily work with many different AI models. It's perfect if you need to switch between various AI providers, track costs across projects, or ensure your AI applications stay reliable by automatically switching models if one has issues. It suits those using Python or managing AI access centrally.

What You Get

You get a simple way to call over 100 AI models using a familiar setup. LiteLLM gives you consistent results, even from different providers, and helps you keep an eye on costs and set budgets. It also includes features like trying another model if the first one doesn't work and tools for logging what your AI models are doing.

How It Works

LiteLLM works in two main ways. You can use its Python code library directly in your projects to talk to different AIs. Alternatively, you can set up a central LiteLLM Proxy Server. This server acts as a gateway, letting multiple users or projects access many AIs, balancing the workload, and tracking all usage from one spot.

Features & Capabilities

⚙️ Universal LLM Gateway

Unified API for 100+ LLMs

Call over 100 Large Language Models using a single, consistent OpenAI input/output format.

Consistent Output Formatting

Ensures text responses are always available at a standard path, simplifying integration across providers.

Input/Output Translation

Automatically translates requests and responses to match the specific requirements of various LLM providers.

Retry and Fallback Logic

Implements automatic retries and fallbacks across multiple LLM deployments for enhanced reliability.

💰 Cost & Access Control

Spend Tracking and Budgeting

Monitor LLM expenditures and set budget limits per project to manage costs effectively.

Rate Limiting

Enforce usage limits per user or project via the proxy to prevent abuse and manage resource allocation.

Virtual Keys with Spend Tracking

Manage access and track spend for individual users or projects using customizable virtual keys within the proxy.

📊 Observability & Debugging

Integrated Logging Callbacks

Easily log LLM inputs and outputs to popular observability tools like Lunary, MLflow, Langfuse, and Helicone.

Standardized Exception Handling

Maps exceptions from all supported providers to OpenAI's exception types for consistent error management.

Request Transformation Visualization

Provides an endpoint to see how LiteLLM normalizes and transforms LLM API requests internally for debugging.

Cost, Usage, and Latency Tracking for Streaming

Monitor real-time metrics for streaming responses via custom callback functions.

🚀 Deployment & Integration Options

LiteLLM Proxy Server (LLM Gateway)

Deploy a central LLM Gateway to unify access, load balance, and track costs across multiple LLMs for platform teams.

LiteLLM Python SDK

Integrate directly into Python applications for calling 100+ LLMs with built-in load balancing and cost tracking.

Use Cases

Centralized LLM Gateway for Enterprise AI Management

Companies leveraging multiple LLMs face challenges with API inconsistencies, reliability, and cost control. LiteLLM Proxy Server acts as a unified LLM gateway, centralizing access to over 100 models with consistent APIs, built-in retry/fallback mechanisms, and robust tools for tracking spend, setting budgets, and applying rate limits. This streamlines enterprise AI operations and provides critical financial oversight.

B2B SaaSFor: Gen AI Enablement Teams

Accelerating LLM Application Development and Iteration

Developers building AI applications struggle with the fragmentation of LLM APIs, which complicates experimentation and switching between models. LiteLLM's Python SDK provides a consistent OpenAI-like interface for over 100 LLMs, enabling rapid prototyping, seamless model swapping, and simplified integration into existing Python codebases and AI frameworks like LangChain or LlamaIndex. This significantly accelerates the development cycle.

Software DevelopmentFor: Developers

Building Resilient and Observable LLM-Powered Applications

Deploying LLM applications to production requires high reliability, robust error handling, and comprehensive observability to monitor performance and debug issues. LiteLLM provides automatic retry and fallback mechanisms to handle provider outages, standardizes error handling across all LLMs, and integrates with leading observability platforms. This allows teams to track LLM inputs/outputs, costs, usage, and latency, enabling robust and monitorable AI solutions.

Enterprise SoftwareFor: ML Platform Teams

Secure and Governed LLM Access for Enterprise Policies

Organizations need to control access to various LLMs, ensure API key security, and enforce usage policies to prevent misuse and manage costs. LiteLLM Proxy Server offers hooks for authentication, rate limiting, and secure storage of API keys. It allows ML Platform teams to centralize LLM access, implement virtual keys, track spend per project/user, and apply guardrails, ensuring secure and governed interaction with all connected LLMs.

Financial ServicesFor: Security Teams

About LiteLLM