LLM Gateway is a small open-source project that puts calls to OpenAI, Anthropic, and Gemini behind one OpenAI-compatible API. It serves developers and small engineering teams that run LLM features across more than one provider and want unified keys, spend tracking, and fallbacks without adopting a large gateway platform.[1][2]
Quick verdict
Small teams routing LLM calls across OpenAI, Anthropic, and Gemini will get the most from LLM Gateway. One OpenAI-compatible endpoint with gateway-issued keys, per-request cost and latency logging, and automatic provider fallback replaces per-provider glue code in a compact Python codebase. The constraint is maturity and breadth: it is a young single-maintainer project covering three providers, so teams needing many providers, SSO, or support SLAs should choose a larger gateway instead. If you have engineers and want a small self-hosted router on your own network, start with the free MIT-licensed build; if you need 100+ providers or enterprise controls, evaluate LiteLLM instead.[1][2]
Key features
One OpenAI-compatible endpoint for three providers Chat completions and models endpoints accept existing OpenAI SDK calls and route them to OpenAI, Anthropic, or Gemini, so adoption rarely means rewriting application code.[1][2]
Gateway-issued API keys Keys are created in the admin API and sent as bearer tokens, giving each caller its own credential instead of sharing raw provider keys.[1]
Per-request cost, latency, and token logging Each call records what it spent, how long it took, and how many tokens it used, which is the raw material for spend inspection.[1]
Retries with backoff plus automatic fallback Failed calls retry with exponential backoff and can fall over to another provider or key, so traffic keeps flowing when one backend fails.[1]
Streaming responses Token streams pass through the gateway, keeping interactive chat behavior intact behind the unified endpoint.[1]
React admin dashboard A small web UI shows usage and manages keys without editing configuration files by hand.[1][2]
SQLite or PostgreSQL storage Single-file SQLite covers trials and small deploys, while PostgreSQL is documented for anything larger.[1]
Container-first deployment A Docker Compose quickstart brings up the API, dashboard, and database together from one command.[1][2]
Who will love it
Small teams juggling two or three providers: one endpoint, one key format, and per-request cost logs remove the spreadsheet-and-glue-code stage of multi-provider use without new infrastructure to learn.[1]
Cost-watchers on a budget: per-request cost, latency, and token records plus a self-hosted footprint attack unmeasured usage directly, and the MIT licence adds no software fee.[1][2]
Tinkerers who read the source: a compact FastAPI and LiteLLM codebase with 44 documented tests is easy to audit, fork, and extend for teams comfortable owning their tools.[1][2]
Who should avoid it
Teams needing broad provider coverage: only OpenAI, Anthropic, and Gemini are documented, so anyone routing across Bedrock, Vertex, Azure, or local models will hit the boundary immediately.[1]
Buyers needing SSO, audit, or support: no enterprise plan, identity integration, or SLA is documented, and operation rests entirely on the adopter.[1][2]
Anyone wanting a proven track record: two stars, two forks, and a single-maintainer history mean the project's future depends on one person, which is a real continuity risk for production dependencies.[1]
Alternatives and compatibility
LiteLLM at /projects/litellm is the closest direct substitute, routing model calls through one OpenAI-compatible API across 100+ providers with budgets and guardrails. Choose LiteLLM when provider breadth, team controls, or enterprise support matter; choose LLM Gateway when a smaller three-provider codebase you can fully audit matters more. No catalogue edge is asserted for this comparison.[1][2]
Because LLM Gateway speaks the OpenAI API, migration in either direction is usually a base-URL change plus provider credentials, which keeps the routing decision reversible. Provider token billing stays with the underlying providers in every case.[1]
Questions worth asking
What is the best LLM Gateway alternative for non-technical users?
Is LLM Gateway really no-code?
No. Providers, keys, fallbacks, and deployment are defined in configuration and containers, and the API is consumed through client code. Plan for engineering involvement from setup onward.[1]
How much does LLM Gateway cost per month?
Can you self-host LLM Gateway?
Yes. That is its only documented mode: a Docker Compose quickstart runs the API, dashboard, and database on the operator's own machine against SQLite or PostgreSQL. You still operate the deployment yourself.[1]
Sources and editorial review
Source research. Reviewed on October 11, 2026. Written by Sanjib Sah. AI assisted; reviewed by a human editor.
We have not run or tested this product.
- github.com vahid8/llm-gateway
official · Observed October 11, 2026
Repository page: product identity, MIT license, Python, 2 stars/2 forks, 22 commits, created May 2026, FastAPI plus LiteLLM plus React dashboard.
- vahid8/llm-gateway README
official · Observed October 11, 2026
README: OpenAI-compatible /v1 API, gateway-issued keys, cost/latency/token logging, retries with fallback, streaming, Docker Compose quickstart, SQLite/PostgreSQL, dashboard, MIT license.
- varunahuja70/LLM-Gateway (identity check)
official · Observed October 11, 2026
Same-named repo inspected only to disambiguate identity; not used for fact evidence.
Compared with these products
No explicit proprietary baseline is recorded.