What is LiteLLM?
LiteLLM is a powerful LLM Gateway, also known as an OpenAI Proxy, designed to simplify model access, spend tracking, and fallbacks across over 100 large language models (LLMs). Built with platform teams in mind, it provides a unified interface that is fully compatible with the OpenAI format, making it easy for developers to integrate and manage multiple LLM providers without changing their code.
Key features include robust spend tracking, budgets, and rate limiting. LiteLLM allows you to accurately charge teams for their usage by attributing costs to keys, users, teams, or organizations. It supports automatic spend tracking across providers like OpenAI, Azure, Bedrock, and GCP, and offers tag-based spend tracking and the ability to log spend to S3, GCS, or other storage solutions. This ensures complete visibility and control over LLM costs.
LiteLLM also provides intelligent fallback mechanisms to ensure high availability. If one provider experiences an outage, LiteLLM can automatically fallback to another provider, with configurable cooldowns and retries. It tracks remaining tokens per minute (tpm) and requests per minute (rpm) limits across providers, and can retry across multiple deployments under the same model name. This minimizes downtime and ensures a seamless experience for end users.
Use cases for LiteLLM are diverse. Platform teams can use it to give developers secure access to a variety of LLMs while enforcing budgets and rate limits. It is ideal for applications that require high reliability, such as customer-facing chatbots, content generation tools, and AI assistants. Technical details include support for prompt formatting for Hugging Face models, pass-through endpoints, and integration with observability tools. LiteLLM is backed by Y Combinator and has a strong community with over 52K GitHub stars, making it a trusted solution for managing LLM infrastructure at scale.
Use Cases
Key Features
unified LLM interface OpenAI format compatibility spend tracking budget management rate limiting intelligent fallbacks multi provider support tag based cost attribution high availability automatic retriesWho is it for?
platform teams, developers, DevOps engineers, IT managers, finance teams, AI/ML engineers, product managers
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