LiteLLM
Headquarters
San Francisco, California, USA
Year Founded
2023
Industries
Artificial Intelligence, Generative AI, Large Language Models, AI Infrastructure, LLMOps, AI Gateway Technology, Model Routing, AI Observability, AI Agents, Machine Learning Operations, Enterprise AI, AI Governance, Developer Tools, Cloud Computing, Open-Source Software
What Is LiteLLM?
LiteLLM is an artificial intelligence infrastructure company developing open-source software for accessing and managing large language models. Its technology provides a unified interface for connecting applications to models from different AI providers, including commercial APIs and self-hosted deployments. LiteLLM offers a Python software development kit and an AI proxy gateway that support model routing, request monitoring, access controls, and usage management. The platform is used by developers and organizations building generative AI applications, AI agents, and systems that rely on multiple language models.
What Does LiteLLM Do?
LiteLLM provides a common API interface for interacting with different AI models. Developers can connect applications to language models through its Python SDK or route requests through the LiteLLM Proxy Gateway. This reduces the need to maintain separate API integrations for every model provider.
The gateway manages model requests through configurable routing, retries, fallbacks, and load balancing. It also supports virtual API keys, usage budgets, rate limits, and cost tracking, allowing organizations to control access to AI models across applications, users, and teams.
LiteLLM additionally provides logging, monitoring integrations, and model usage analytics. Its infrastructure can be deployed within an organization’s own environment and connected to existing observability systems. These capabilities support production AI applications that require centralized model access, operational reliability, and consistent management across different providers.
LiteLLM Technology
LiteLLM’s technology is built around a unified API layer for large language models. Its Python SDK translates requests between application code and the interfaces used by different AI providers. The SDK supports common operations such as text generation, embeddings, image generation, speech processing, and other model capabilities, depending on provider compatibility. Developers can change supported models without rewriting the complete application integration.
The LiteLLM Proxy Gateway operates as a centralized service between AI applications and model providers. It exposes OpenAI-compatible API endpoints and supports request routing across multiple model deployments. Its routing mechanisms include automatic retries, fallback models, load balancing, and configurable rate limits. These features help applications respond to provider outages, capacity constraints, and changing model availability.
LiteLLM also provides virtual API keys, spend tracking, budget controls, and access management. Organizations can assign keys to users or teams, configure usage limits, and monitor estimated costs across model providers. The platform supports integration with external authentication, logging, and monitoring services, including systems based on OpenTelemetry.
Its observability capabilities record request metadata, latency, token consumption, and model usage. The platform also supports configurable caching, request policies, and integrations with external guardrail systems. Because LiteLLM is available as open-source software, organizations can deploy the gateway within their own infrastructure and customize how model traffic is managed.
Why LiteLLM Matters
AI applications increasingly use models from multiple providers, each with different APIs, pricing structures, capabilities, and operational limits. Managing these integrations independently can create additional development work and make it difficult to monitor costs and maintain consistent access policies.
LiteLLM provides a common infrastructure layer that separates application development from individual model providers. Its unified API and routing capabilities allow developers to change model configurations, distribute requests, and introduce fallback options without rebuilding each integration.
Its open-source gateway and enterprise management capabilities also address the operational requirements of production AI systems. Centralized monitoring, access controls, and usage budgets help organizations manage generative AI applications as their model consumption and number of users increase.
LiteLLM Products & Platforms
LiteLLM AI Gateway
LiteLLM AI Gateway is a self-hosted proxy server that provides a unified, OpenAI-compatible API for accessing models from multiple AI providers. It supports request routing, load balancing, retries, fallback models, virtual API keys, rate limits, and budget management. Organizations use the gateway to centralize model access, monitor request costs, and manage authentication and permissions across applications, users, and teams.
LiteLLM Python SDK
LiteLLM Python SDK is an open-source library that standardizes how Python applications interact with different large language models. Developers can use a common interface for text generation, embeddings, image generation, and other supported model operations. The SDK provides consistent response formatting, streaming, error handling, retries, and fallbacks, reducing the need for provider-specific application code.
LiteLLM MCP Gateway
LiteLLM MCP Gateway provides centralized access to tools and services using the Model Context Protocol. Organizations can register MCP servers and control which users, teams, or applications are authorized to access them. The gateway uses virtual keys and access policies to manage connections between AI agents and external tools, while recording activity through the platform’s logging infrastructure.
LiteLLM Agent Gateway
LiteLLM Agent Gateway extends the platform’s access management capabilities to agent-to-agent communications. It supports registering and invoking agents through compatible protocols, including Agent2Agent (A2A). Organizations can apply virtual keys, access permissions, and request logging to agent interactions, providing a common management layer for distributed AI workflows.
LiteLLM Enterprise
LiteLLM Enterprise provides additional administration, security, and support capabilities for organizations operating AI infrastructure at scale. Its features include enterprise authentication, single sign-on, audit logging, and advanced governance controls. The commercial offering builds on LiteLLM’s open-source gateway and supports organizations that require centralized model management within their own infrastructure.
LiteLLM Applications
LiteLLM supports generative AI applications, enterprise model infrastructure, and AI agent systems. Developers use its Python SDK to integrate language models into software applications, while platform teams use the AI Gateway to manage model access across multiple services. Applications include AI assistants, document processing, customer support automation, software development tools, and retrieval-augmented generation systems.
The platform also supports AI cost management, model reliability, and organizational governance. Teams can monitor token consumption, enforce usage budgets, route requests between providers, and apply access controls to models and external tools. Its MCP and Agent Gateway capabilities extend these controls to agent-based workflows that connect language models with business applications and other automated systems.
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Frequently Asked Questions
Is LiteLLM free and open source?
Yes. LiteLLM provides an open-source Python SDK and AI Gateway under the MIT license. Organizations can deploy the software within their own infrastructure. LiteLLM also offers a paid Enterprise edition with additional security, administration, and support features.
How many AI model providers does LiteLLM support?
LiteLLM supports more than 100 AI model providers through its unified API. Supported services include commercial cloud models and compatible self-hosted deployments. The available models and supported capabilities vary by provider.
Can LiteLLM be deployed on private infrastructure?
Yes. LiteLLM’s AI Gateway can be self-hosted on an organization’s servers or cloud infrastructure. This allows teams to manage provider credentials, access controls, logging, and model traffic within their own deployment environment.
Does LiteLLM support AI agents and MCP tools?
Yes. LiteLLM includes gateway capabilities for Model Context Protocol tools and Agent2Agent-compatible systems. These features allow organizations to manage access permissions, virtual keys, and logging for agent interactions and external tools.
Is LiteLLM the same company as OpenRouter?
No. LiteLLM and OpenRouter are separate AI infrastructure companies. LiteLLM primarily provides a self-hosted gateway and Python SDK for managing model connections, while OpenRouter operates a hosted API service for accessing models from different providers.
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