Research AI Landscape

litellm

3 min read

LiteLLM

Status: trial | Maturity: production-ready | Last reviewed: 2026-03-20

What it does

LiteLLM is an open-source LLM gateway and proxy that provides a unified OpenAI-compatible API interface to 100+ LLM providers (OpenAI, Anthropic, Google, Azure, Bedrock, Cohere, Mistral, local models via Ollama, and many more). Teams building AI-powered research tools or internal platforms use LiteLLM to avoid vendor lock-in, apply rate limiting, log usage, and manage API keys centrally.

The self-hosted LiteLLM Proxy is the core product: a lightweight server that translates requests between the OpenAI API format and any supported provider’s native format.

Key capabilities

  • Unified API for 100+ LLM providers (OpenAI-compatible interface)
  • Rate limiting, usage tracking, and cost management
  • API key management and access control
  • Model routing and fallback (e.g. fall back to cheaper model if primary is unavailable)
  • Support for local model deployment (Ollama, vLLM)
  • Logging and observability integrations
  • Self-hosted proxy — data stays within your infrastructure

Fit for Sheffield researchers

Primarily relevant to Research Computing / CiCS staff and research software engineers building AI-powered research infrastructure at Sheffield. LiteLLM enables a central AI platform where:

  • Sheffield can offer researchers access to multiple LLMs through a single governed endpoint
  • API key management is centralised (researchers don’t hold individual vendor API keys)
  • Usage and costs are tracked per team/project
  • The platform can be swapped to different LLM backends without changing application code

This is infrastructure, not an end-user tool. Priority audience: CiCS and research computing teams considering how to deliver AI services to Sheffield researchers.

Licensing and access

Open source (MIT licence) for the core proxy. Self-hosted deployment on Sheffield’s own infrastructure is straightforward. Enterprise cloud managed service available for teams wanting vendor-managed hosting.

Trust and governance notes

When self-hosted, LiteLLM routes requests to external LLM provider APIs — data governance depends on which underlying providers are configured. A Sheffield deployment should default to routing to providers with appropriate data processing agreements (e.g., enterprise OpenAI, Anthropic API, or locally-hosted open models).

Local model routing (via Ollama/vLLM) enables fully on-premise AI inference, which would address most GDPR concerns for sensitive research data. This is a significant advantage over cloud-only solutions.

Observed limitations

  • Infrastructure tool — requires DevOps/technical expertise to deploy and maintain
  • Ongoing maintenance burden: upstream API changes from providers require proxy updates
  • Not an end-user tool — researchers need a separate interface layer
  • Enterprise cloud service pricing not publicly listed
  • Community support varies by LLM provider; less well-tested providers may have issues

claude-code · ag2-autogen · coding-and-development capability area

Sources and evidence

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