OpenAI-style access across providers
Call more than 100 LLMs through an OpenAI-compatible interface, then translate those calls into provider-specific endpoints such as chat completions, responses, embeddings, images, audio, and batches.
LiteLLM
LiteLLM provides an OpenAI-compatible way to call and manage 100+ LLMs through a Python SDK or proxy server. It helps teams route requests, track spend, and work across multiple providers from one interface.
What LiteLLM does
LiteLLM is a developer platform for calling and managing large language models through either a Python SDK or a proxy server. Its core purpose is to present an OpenAI-compatible interface while translating requests to many provider-specific endpoints behind the scenes.
The docs describe LiteLLM as supporting more than 100 models and a broad set of endpoint types, including chat completions, responses, embeddings, images, audio, batches, routing, and proxy-based gateway workflows. That makes it useful for teams that want a single access layer for multi-provider LLM usage, cost tracking, and request management.
Core capabilities
Centralized proxy and access control
Use the proxy as a centralized LLM gateway with authentication and authorization, virtual keys, and an admin dashboard for monitoring and management.
Multi-tenant cost management
Track spend by project and user, set budgets, and apply per-project customization such as logging, guardrails, and caching.
Routing, fallback, and load balancing
Route requests across deployments with retry and fallback logic, including cooldowns, timeouts, queueing, and support for load balancing across Azure, OpenAI, and other providers.
Broad endpoint coverage
Expose multiple supported surfaces through the proxy, including chat completions, embeddings, image generation, RAG endpoints, guardrails, memory, and other provider-specific endpoints.
Observability and SDK ergonomics
Integrate observability callbacks such as Lunary, MLflow, and Langfuse, and use OpenAI-compatible errors for application-level handling.
Common ways teams use LiteLLM
Centralized model gateway
Use the proxy as a central LLM gateway when multiple applications need controlled access to shared model providers. The docs highlight authentication, authorization, virtual keys, admin monitoring, and per-project policy controls for this setup.
Direct application integration
Use the Python SDK when you want LiteLLM embedded directly in application code. The docs position this path for developers building LLM projects who need a unified interface without operating a separate proxy.
Cross-deployment routing and failover
Use Router when traffic must be distributed across multiple deployments of the same model alias. The routing docs describe load balancing, retry, fallback, cooldowns, queueing, and latency- or cost-aware strategy options.
Budget and spend oversight
Use the platform to track spend and manage budgets across teams or projects. The home page calls out spend tracking and budgets per project, while the proxy docs add multi-tenant cost management and user/project-level controls.
Multi-endpoint provider access
Use LiteLLM when you need to reach many provider-specific endpoints through one interface. The supported endpoints page shows coverage beyond chat, including embeddings, images, audio, RAG, memory, guardrails, and other specialized APIs.
Pros and Cons
Pros
- Provides an OpenAI-compatible interface for many providers, which reduces provider-specific code changes.
- Supports both a proxy server and a Python SDK, so teams can choose a centralized gateway or direct library integration.
- Includes routing features such as retry, fallback, cooldowns, timeouts, and load balancing across deployments.
- Offers cost and budget controls with per-project spend tracking and support for user/project-level management.
- Documents a wide range of supported endpoints, from chat completions and embeddings to image generation, RAG, guardrails, and memory.
Cons
- The public pricing page in the collected sources is not available, so pricing cannot be confirmed from these docs.
- The strongest evidence in the provided sources centers on proxy, routing, and endpoint support; some areas such as pricing and broader integrations remain only partially documented here.
FAQ
How do I use LiteLLM?
LiteLLM can be used either through the Proxy Server or directly from the Python SDK. The docs show both approaches as part of the same product, with the proxy positioned as a central LLM gateway and the SDK as the option for direct use in Python code.
What kinds of endpoints does LiteLLM support?
The docs emphasize that LiteLLM translates requests into provider-specific endpoints while keeping an OpenAI-style input and output format. It supports chat completions, responses, embeddings, images, audio, batches, and more.
Does LiteLLM handle routing and failover?
LiteLLM Router can load-balance across multiple deployments and supports retry, fallback, cooldowns, timeouts, and queueing. The proxy docs also mention Redis-based tracking for cooldown and usage when managing token-per-minute and requests-per-minute limits in production.
Is pricing listed in the docs?
The collected sources do not show public pricing details. The pricing URL returns a page not found message, so pricing should be treated as unavailable from the provided docs.
Who is LiteLLM for?
The proxy is described for GenAI enablement and ML platform teams, while the Python SDK is described for developers building LLM projects. That suggests the product can serve both centralized platform workflows and direct application integration.
Quick Facts
- Category
- Developer Tool
- Primary workflow
- OpenAI-compatible access to multi-provider LLMs via proxy or SDK
- Primary users
- Gen AI enablement teams, ML platform teams, and developers
- Source domain
- docs.litellm.ai
- Supported providers
- 100+ LLMs and endpoints across providers such as OpenAI, Anthropic, Azure, Vertex AI, NVIDIA, Hugging Face, Ollama, OpenRouter, Novita AI, and Vercel AI Gateway
- Pricing
- Not available in the collected docs
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