LiteLLM is an open-source AI gateway and proxy that lets teams call many model providers through one OpenAI-compatible interface.

LiteLLM has an open-source core that teams can self-host. Enterprise, managed, or on-prem options may require custom pricing.

Who should use LiteLLM?

LiteLLM is best for developers and platform teams that need model routing, fallbacks, budgets, rate limits, and spend tracking.

Does LiteLLM replace model providers?

No. LiteLLM sits in front of model providers, so you still pay provider costs for usage.

Can LiteLLM be deployed on-prem?

Yes. LiteLLM promotes on-prem and enterprise gateway deployment options for teams with stricter infrastructure requirements.

What should buyers watch for?

Teams should evaluate provider billing, key management, routing rules, observability, guardrails, and enterprise support needs.

LiteLLM

Open-source AI gateway for routing, budgets, fallbacks, and model access.

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What is LiteLLM?

TL;DR

LiteLLM is an open-source gateway and Python SDK for calling many LLM providers through one interface, with routing, fallbacks, observability, budgets, and enterprise controls for production AI systems.

OpenAI-compatible gateway across many LLM providers
Routing, fallbacks, budgets, and rate-limit controls
Spend tracking, logging, guardrails, and observability

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About

LiteLLM is an open-source AI gateway and proxy that gives developers one OpenAI-compatible interface for calling many model providers. It helps platform teams manage model access, routing, fallbacks, budgets, rate limits, prompt management, pass-through endpoints, logging, and spend tracking across providers such as OpenAI, Azure, Anthropic, Gemini, Bedrock, and others. The product is especially useful when an AI product needs provider flexibility instead of being locked to one API. Teams can route requests through LiteLLM, standardize model names, attribute usage to users or teams, set spend controls, and add observability around production LLM traffic. LiteLLM has an open-source core and commercial enterprise gateway options. The public site emphasizes on-prem deployment, model access, budgets, rate limiting, guardrails, cost tracking, and enterprise requests rather than simple self-serve plan cards. For FutureStack, LiteLLM belongs in Developer Tools because it solves a real infrastructure problem for AI builders. It is best for teams that need a reliable LLM gateway, multi-provider routing, cost controls, fallbacks, and production model governance.

Use Cases

Model Gateway

Expose many LLM providers behind a consistent API so applications can switch models without rewriting integrations.

Model Routing

Route requests between models using cost, latency, quality, or fallback rules for resilient production systems.

AI Observability

Track model requests, usage, spend, and performance across providers from a centralized gateway.

Enterprise AI Controls

Apply budgets, access controls, rate limits, and governance policies to organization-wide model usage.

Key Features

OpenAI-compatible gateway across many LLM providers
Routing, fallbacks, budgets, and rate-limit controls
Spend tracking, logging, guardrails, and observability
Open-source core with enterprise and on-prem options

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