LiteLLM

LiteLLM

LiteLLM is an open-source AI gateway that provides a standardized interface to access and manage 100+ large language models. It helps developers and teams simplify integration, control costs, and streamline operations.
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AI gatewayLLM unified APImanage multiple LLMsLLM cost managementopen-source model routerenterprise AI ops platform

Features of LiteLLM

Unified, OpenAI-compatible API that supports calls to 100+ major and local large language models.
Built-in intelligent routing and failover that automatically selects models based on policies to ensure availability.
Centralized tracking of token usage and costs across models, projects, and teams, with budget controls and alerts.
Deployable as an independent proxy server with unified authentication, rate limiting, and audit logging.
Flexible deployment via Docker, Helm, Terraform or other tools for cloud or on-premises environments.

Use Cases of LiteLLM

Platform teams centralize access and cost control for multiple LLM vendors used by internal developers.
Run multi-model A/B tests or balance cost vs. performance using intelligent routing and model switching.
Build enterprise-grade AI services that require high availability, autoscaling and centralized monitoring.
Developers building applications that use multiple LLMs simplify code and avoid vendor lock-in.
Meet data residency or compliance needs by self-hosting the gateway and controlling model calls.

FAQ about LiteLLM

QWhat is LiteLLM and what is it used for?

LiteLLM is an open-source tool for unified access and integration of large language models. Acting as an AI gateway, it standardizes calls to 100+ LLMs to simplify integration, management and operations, reducing the complexity of multi-model setups.

QWhich large language models does LiteLLM support?

LiteLLM supports over 100 LLM providers, including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Cohere, Mistral, Ollama, and models hosted on Hugging Face, among others.

QHow does LiteLLM help control AI development costs?

LiteLLM offers centralized cost tracking to monitor token usage and expenses by model, project and team. It supports budget alerts and quotas, and helps optimize costs through request caching and intelligent routing.

QWhat deployment options does LiteLLM offer?

LiteLLM can be integrated directly via a Python SDK or deployed as a standalone proxy server. It supports deployment on cloud or on-premises Kubernetes using Docker, Helm or Terraform.

QIs LiteLLM suitable for small projects that use a single model?

If your application always uses a single provider, introducing LiteLLM may add unnecessary architectural complexity. It’s best suited for teams and organizations that need multi-model flexibility, centralized governance or cost controls.

QHow does LiteLLM handle high availability and failures?

LiteLLM includes intelligent routing and failover mechanisms. If a primary model becomes unavailable, hits rate limits, or times out, it can automatically switch to preconfigured fallback models to maintain service continuity and resilience.

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