AI stacks are getting more complex with more models, providers, costs, and decisions. ARBR gives your applications one control layer across your AI stack. Connect once through an OpenAI-compatible endpoint to route, govern, observe, evaluate, and deploy across AI models. Open source, MIT-licensed, provider-neutral, and self-hosted.
Hey Product Hunt 👋
We built ARBR because teams can see how much their LLMs cost, but their logs rarely answer the harder production question:
Which workloads can safely move to a different model, what evidence supports the change, and did the result hold after rollout?
ARBR closes that loop.
It observes workloads, surfaces model-switching opportunities, builds evaluation datasets from representative traffic, and compares candidate models across quality, cost, latency, format adherence, and critical failures.
The final decision remains human-controlled. Teams can approve a recommendation, introduce it through shadow testing or a guarded canary, measure the realized savings, and roll back if quality drops.
ARBR is:
▪️ Self-hosted and provider-neutral
▪️ OpenAI-compatible
▪️ Open source under the MIT License
▪️ Usable as a standalone gateway or above LiteLLM
▪️ Built around explicit, auditable, and reversible routing decisions
Explicitly pinned models stay pinned. When an application uses model: "auto", ARBR follows only the rules and policies that the team has enabled.
You can explore the complete workflow in demo mode without adding a provider key, then connect your own traffic when you are ready.
We would genuinely value feedback from teams running LLM workloads in production:
▪️ Is the evidence sufficient for you to approve a model change?
▪️ Which governance or deployment controls are missing?
▪️ Which provider integrations should we prioritize next?
Deploy it, break it, open an issue, or tell us where the workflow falls short.
GitHub: https://github.com/project-arbr/...
Docs: https://projectarbr.org/docs/
There is something great about pulling a scattered pile of AI tools back under one roof where you can genuinely keep an eye on the whole thing.
shadow test, guarded canary, then rollback if quality drops. thats a lot of gates for one model swap. what actually trips the rollback, an eval score or a person
Congrats! I like that ARBR focuses on the workload rather than pushing you toward a particular model. Different tasks obviously need different things.
I like the idea of having one layer to handle model routing instead of building all of this logic into every application.
Cost + performance is going to be a big challenge as AI usage grows for us. Routing different
tasks to different models seems like a pretty sensible approach.
About ARBR on Product Hunt
“Control Every AI Request”
ARBR launched on Product Hunt on September 3rd, 2026 and earned 94 upvotes and 10 comments, placing #14 on the daily leaderboard. AI stacks are getting more complex with more models, providers, costs, and decisions. ARBR gives your applications one control layer across your AI stack. Connect once through an OpenAI-compatible endpoint to route, govern, observe, evaluate, and deploy across AI models. Open source, MIT-licensed, provider-neutral, and self-hosted.
ARBR was featured in Open Source (68.8k followers), Developer Tools (518.8k followers) and Artificial Intelligence (477.8k followers) on Product Hunt. Together, these topics include over 216.5k products, making this a competitive space to launch in.
Who hunted ARBR?
ARBR was hunted by Shubham Deshmukh. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how ARBR stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Shubham Deshmukh