Analysis updated 2026-05-18
Compare more than 50 AI gateways side by side on compliance, price, security, and stability.
Use the cost benchmark to see how much the same AI task can cost across different providers.
Follow the decision tree to pick a gateway based on self hosting needs and EU data residency.
Copy ready made code to switch an existing OpenAI client to a different gateway with one line.
| cuihuan/awesome-ai-gateway | andyuneducated/resolve-ai | carriex6/cvpr2026_similarity_as_evidence | |
|---|---|---|---|
| Stars | 18 | 18 | 18 |
| Language | Python | Python | Python |
| Setup difficulty | easy | hard | hard |
| Complexity | 1/5 | 4/5 | 4/5 |
| Audience | developer | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
This repository is a curated comparison list of AI gateways and LLM proxies, tools that sit between your application and AI providers like OpenAI, Anthropic, or Google. Instead of your code calling each provider directly, you point it at a gateway with a single URL, and the gateway handles routing the request to the right model, switching to a backup if one fails, caching repeated queries to save money, tracking costs across providers, and enforcing guardrails. The whole point is that you change one line of code (the base URL) and gain flexibility over which AI service you actually use. The list covers more than 50 gateways and scores them on four dimensions: compliance posture, price, security, and stability. It also includes a reproducible cost benchmark showing that the same AI task can cost over 100 times more depending on which model you route to. This makes it genuinely useful beyond a simple link roundup, since you can see the tradeoffs side by side rather than reading each project's own marketing. The entries are grouped by use case. Some gateways are best for teams that want the cheapest access to many models without running their own infrastructure. Others are open-source tools you self-host, which gives more control over data and compliance but requires you to run a server. A few are enterprise-grade solutions designed for Kubernetes clusters with strict audit and guardrail requirements. There is also a section covering gateways designed for the China AI ecosystem, which uses different model providers. The README includes a decision tree to help you pick the right gateway based on whether you want to self-host, which cloud you already use, and whether EU data residency matters. It also provides ready-to-paste code showing how to switch any standard OpenAI client library to use a different gateway with a one-line change. The list is updated daily via automated scripts and accepts community contributions. It is released under CC0, meaning no copyright restrictions on the content.
A curated, scored comparison of over 50 AI gateway and LLM proxy tools, including a cost benchmark and a decision tree for picking one.
Mainly Python. The stack also includes Python.
No restrictions at all, the content is dedicated to the public domain.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.