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AI Tools›Infrastructure & MLOps›Litellm
Infrastructure & MLOps

Litellm

LiteLLM is an open-source gateway and Python library that provides unified access to over 100 large language models (LLMs) through a standardized OpenAI-compatible API format. It abstracts the differences between various LLM providers such as OpenAI, Azure, Anthropic, and Google Gemini, enabling developers to interact with multiple models using consistent input and output formats without rewriting code for each provider. LiteLLM operates both as a proxy server managing authentication, load balancing, and cost tracking across teams, and as a Python SDK for direct integration into applications. This dual functionality supports both platform teams overseeing LLM access for multiple developers and individual developers building LLM projects.

Updated Jan 13, 2026open-source
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Overview

LiteLLM offers a unified OpenAI-compatible API gateway and Python SDK to access and manage over 100 large language models from multiple providers.

Pricing
open-source
Category
Infrastructure & MLOps
Company
Visual Guide
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Key Features
01
Enables calling over 100 LLMs using a consistent OpenAI input/output format with automatic translation to each provider's specific endpoints.
02
Monitors costs per project or user and allows setting budget limits across different LLM deployments.
03
Supports retry and fallback mechanisms across multiple deployments through a Router feature to ensure reliability.
04
Provides authentication, authorization, and multi-tenant cost tracking with virtual keys for secure access control.
05
Ensures text responses are always available at the same location in the response structure regardless of the LLM provider used.
06
Allows control over request rates and implementation of safety guardrails on a per-project basis.
07
Offers a user interface to monitor and manage LLM usage and costs across projects.
Real-World Use Cases

Unified LLM Access for Development Teams

Platform teams managing multiple developers and projects can use LiteLLM to provide consistent access to various LLM providers with centralized cost and usage tracking.

Individual Developer Integration

Developers building applications that require multiple LLMs can integrate LiteLLM's Python SDK to simplify API calls without rewriting code for each provider.

Quick Start
1
Install LiteLLM
Run pip install litellm to install the Python package.
2
Create Configuration File
Set up a litellm_config.yaml file defining your model list with provider credentials including API keys and endpoints.
3
Deploy Proxy Server (Optional)
Clone the repository and deploy the proxy server using Docker, Helm, or Terraform depending on your infrastructure.
4
Configure Model Parameters
Define models in the config file with their provider routes and API credentials.
5
Test Setup
Send a test request to the proxy endpoint or use the Python SDK directly with the configured model name.
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Pricing
Model: open-source

LiteLLM offers an open-source version and an enterprise edition with additional features such as custom SLAs, JWT authentication, single sign-on, and audit logs. Specific pricing details are not publicly available.

Assessment
Strengths
  • Provides a unified OpenAI-compatible API format for over 100 LLMs, reducing integration complexity.
  • Supports cost tracking, load balancing, and fallback mechanisms across multiple LLM providers.
  • Includes both a proxy server and Python SDK to serve different user needs.
  • Offers centralized authentication and multi-tenant management with virtual keys.
Limitations
  • No publicly available pricing information for enterprise features.
  • Documentation and GitHub repository lack detailed statistics such as stars or contributors count.