About LiteLLM
LiteLLM — LiteLLM is an LLM Gateway (OpenAI Proxy) designed to manage authentication, load balancing, and spend tracking across 100+ LLMs, all while maintaining the OpenAI format. It simplifies the process of using LLM APIs from various providers like OpenAI, Azure, Cohere, Anthropic, Replicate, and Google. LiteLLM offers consistent outputs and exceptions for all LLM APIs, along with logging and error tracking for all models. It provides features like cost tracking, batches API, guardrails, model access, budgets, LLM observability, rate limiting, prompt management, S3 logging, and pass-through endpoints.
Top use cases
- Giving developers access to multiple LLMs
- Managing spend across different LLM providers
- Implementing LLM fallbacks for reliability
- Standardizing LLM API access across an organization
Built for
Key features
- LLM Gateway for 100+ LLMs
- OpenAI-compatible API
- Cost Tracking and Budget Management
- LLM Fallbacks
- Load Balancing
- Rate Limiting
- Prompt Management
- Logging and Error Tracking
Pros & cons
Pros
- Simplifies access to multiple LLMs
- Provides consistent API format (OpenAI)
- Offers cost tracking and budget management
- Enables LLM fallbacks for increased reliability
- Supports logging and error tracking
- Open Source option available
Cons
- Requires initial setup and configuration
- May introduce a slight performance overhead
- Enterprise features require a paid subscription
Pricing
Open Source
$0
Free
Enterprise
For giving LLM access to a large number of developers and projects. Includes Enterprise Support, Custom SLAs, JWT Auth, SSO, and Audit Logs.
Frequently asked questions
What is LiteLLM?
LiteLLM simplifies model access, spend tracking, and fallbacks across 100+ LLMs.
What features does LiteLLM offer?
LiteLLM offers features like cost tracking, budgets, rate limits, OpenAI-compatible API, LLM fallbacks, and more.
How does LiteLLM help with LLM management?
LiteLLM standardizes logging, the OpenAI API, and authentication for all models, significantly reducing operational complexities.
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