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LiteLLM Review

LiteLLM is an open-source library and proxy that simplifies calling various large language models with a unified API.

shipped Aug 20, 2026freemium
Domain rating79Monthly visits16K/mo
LiteLLM — product screenshot

Why it matters

1Supports over 100 LLM providers and 1,800 models via a single OpenAI-compatible API.
2Achieved SOC 2 Type I Certification, with Type II expected by April 15th, 2025.
3Offers a free tier and does not add per-token markup on LLM usage.
4Features automatic fallbacks, load balancing, and cost tracking across providers.

About LiteLLM

Business Model
Subscription SaaS
Usage Pricing
$0.03 - $5 per request
Free Credits
$10 free credits
Platforms
Web, API
Target Audience
Platform teams managing AI integrations

Pricing Plans

Free Tier
$0 / monthly
  • • 140+ LLM provider integrations
  • • Langfuse, Arize Phoenix, LangSmith
  • • Virtual keys, budgets, and teams
  • • Load balancing
Enterprise
Custom Pricing / annual
  • • Enterprise support
  • • Custom SLAs
  • • JWT auth, SSO
  • • Audit logs

Cost Examples

  • • gpt-5: $1.25 per request
  • • claude-opus-4-8: $5 per request
  • • llama-4-maverick: $0.35 per request
API DocsGitHubOpen Source

Specs

API Docs

View Documentation →

GitHub

View Repository →

API Available

Yes, public API

overview

What is LiteLLM?

LiteLLM is an AI gateway tool developed by BerriAI that enables individual developers, small teams, and large companies to simplify interactions with various Large Language Models (LLMs) by providing a unified interface. It functions as both a Python SDK and a self-hosted AI gateway (proxy server), allowing developers to call over 100 LLM APIs using a single, OpenAI-compatible interface. LiteLLM normalizes APIs across providers like OpenAI, Anthropic, Google Gemini, Mistral, Cohere, AWS Bedrock, and local models via Ollama, accelerating development and reducing vendor lock-in. The tool also offers features for cost optimization, centralized management, enhanced reliability through fallbacks, and security governance with virtual key management and audit logs. As of mid-2026, LiteLLM has garnered over 40,000 GitHub stars and 240 million Docker pulls.

features

Key Features of LiteLLM

LiteLLM provides a comprehensive suite of features designed to streamline LLM integration and management for developers and organizations. These features address challenges related to multi-provider access, cost control, operational reliability, and security.

  • Unified OpenAI-compatible API for over 100 LLM providers and 1,800 models.
  • Centralized LLM gateway/proxy for managing API traffic, keys, and monitoring usage.
  • Cost optimization features including budget controls, lowest-cost routing, prompt compression, and caching.
  • Automatic fallbacks to alternative providers or models for enhanced reliability.
  • Standardized error handling, retries, and timeouts across all integrated LLMs.
  • Virtual key management, rate limiting, access control, and audit logs for security and governance.
  • Detailed usage tracking and budgeting capabilities across all LLM expenditures.
  • Air-gapped deployment options for data privacy and compliance requirements.
  • Support for tool-result guardrails and auto-router deployment affinity (v1.97.0).

use cases

Who Should Use LiteLLM?

LiteLLM is designed for a broad range of users, from individual developers to large enterprises, who require efficient and flexible interaction with multiple Large Language Models. Its capabilities are particularly beneficial for teams focused on AI development, platform management, and cost-effective LLM deployment.

  • Individual developers and small teams seeking to experiment with or integrate various LLMs without extensive code changes.
  • Generative AI teams requiring a unified API to switch between different LLM providers and models for application development.
  • Platform teams managing AI integrations across an organization, needing centralized control over API traffic, keys, and monitoring.
  • Large companies prioritizing data privacy and compliance, utilizing self-hosted deployments for sensitive data or offline use cases.
  • Organizations focused on cost optimization, leveraging LiteLLM's features for budget controls, lowest-cost routing, and usage tracking across multiple LLM providers.

how to use

How to Use LiteLLM

LiteLLM can be utilized as a Python SDK or deployed as a self-hosted proxy server to manage LLM interactions. Getting started typically involves installation and configuration of API keys for desired LLM providers.

  • 1Install LiteLLM via pip: pip install litellm.
  • 2Configure API keys for your chosen LLM providers (e.g., OpenAI, Anthropic) as environment variables or within a configuration file.
  • 3Import LiteLLM in your Python application and use the completion() function with a unified syntax, specifying the desired model (e.g., model="gpt-4", model="claude-3-opus-20240229").
  • 4For proxy deployment, use Docker to run the LiteLLM proxy server, exposing it on a specified port.
  • 5Direct API requests to the LiteLLM proxy endpoint, which then routes them to the appropriate LLM provider based on configuration.
  • 6Monitor usage, costs, and performance through the LiteLLM dashboard or integrated logging.

pricing

LiteLLM Pricing & Plans

LiteLLM operates on a freemium model, offering a robust open-source core for self-hosting and an Enterprise tier for advanced organizational needs. The open-source version does not impose per-token markups, allowing users to pay LLM providers directly at their published rates. The Enterprise tier provides custom pricing tailored to specific organizational requirements.

  • Free Tier: $0 (monthly) - Includes the open-source library and proxy for self-hosting.
  • Enterprise: Custom Pricing (annual) - Offers advanced features, support, and compliance for large organizations.

Pros

  • +Unified API for over 100 LLM providers, simplifying integration and reducing vendor lock-in.
  • +Open-source core with an MIT license, offering flexibility and no per-token markup.
  • +Comprehensive cost optimization features, including budget controls, lowest-cost routing, and usage tracking.
  • +Enhanced reliability through automatic fallbacks, retries, and standardized error handling.
  • +Strong compliance posture with SOC 2 Type I and ISO 27001 certifications, and HIPAA policy templates.
  • +Self-hosting capability for data privacy, compliance, and air-gapped deployments.

Cons

  • −Operational complexity and hidden costs associated with self-hosting in production environments.
  • −Reported bugs, particularly with enterprise UI features like budget updates and API issues.
  • −Performance limitations due to its Python architecture, especially at high requests per second (RPS).
  • −Does not offer a Business Associate Agreement (BAA) as a vendor, requiring users to manage their own HIPAA infrastructure.
  • −Supply chain attack in March/August 2026 compromised PyPI packages, highlighting security risks in the open-source ecosystem.

Policies

Pricing Page

View Pricing→

Similar Tools

LiteLLM vs Competitors

LiteLLM holds a prominent position in the LLM gateway market, particularly due to its extensive provider support and open-source nature. However, it competes with several alternatives that offer different strengths, such as performance, managed services, or specialized observability.

1
Bifrost↗

A high-performance, open-source AI gateway built in Go, focusing on production-grade reliability, unified provider access, and enterprise-grade governance.

Offers potentially faster P99 latency and is written in Go, which might be preferred for some deployments over LiteLLM's Python, but could require different expertise for deep customization.

2
AISIX↗

A Rust-native, open-source AI gateway that provides a fully open data plane with advanced routing strategies, including cost-, latency-, and load-aware routing, semantic routing, and ensemble.

Offers a fully open-source data plane in Rust for performance and control, potentially with a steeper learning curve for Python-centric teams compared to LiteLLM.

3
HeliconeOn Stork

An open-source AI gateway and observability platform built in Rust, primarily focused on detailed monitoring, tracing, caching, and cost tracking for LLM requests.

While LiteLLM includes some observability, Helicone's core strength is deep analytics and debugging for LLM usage, making it a strong choice if detailed insights are paramount, potentially offering a more specialized feature set than LiteLLM's broader abstraction.

4
Free LLM Gateway↗

A unified OpenAI-compatible API server that specifically aggregates over 24 free LLM providers, offering automatic fallback routing and rate limit tracking.

This tool is uniquely focused on providing access to *free* LLM providers, which is a specific use case LiteLLM doesn't exclusively target, making it ideal for zero-cost inference but potentially less comprehensive for paid or enterprise LLM integrations.

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