litellm
One API in front of every model provider, with per-key spend limits, logging and automatic failover.
Mature enough to put in production this quarter.
Any team calling more than one model provider, or anyone who has been surprised by an AI bill.
A homegrown provider-switch layer, and finding out about overspend from the invoice.
The open-issue count is very large and the surface area is wide; pin your version and read release notes before upgrading.
Put it in front of your existing calls and set a hard monthly cap per key. That alone pays for the migration.
Custom / check the LICENSE file. GitHub could not identify a standard licence for this repository, which usually means a custom or source-available one. Read the LICENSE file before you build on it.
This is a reading of the licence label, not legal advice.
The numbers
Maintainers describe it as: “The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM]”
litellm, in short
- Should a small team use litellm?
- Adopt now. Mature enough to put in production this quarter. Any team calling more than one model provider, or anyone who has been surprised by an AI bill.
- What does litellm actually do?
- One API in front of every model provider, with per-key spend limits, logging and automatic failover.
- What does litellm replace?
- A homegrown provider-switch layer, and finding out about overspend from the invoice.
- What is the downside of litellm?
- The open-issue count is very large and the surface area is wide; pin your version and read release notes before upgrading.
- Can litellm be used in a commercial product?
- Its licence is Custom / check the LICENSE file, which carries obligations or restrictions for commercial use. Read the LICENSE file, and get a lawyer's read before you ship it inside a product you sell.