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/
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.
On the analytics side, ARBR competes within Open Source, Developer Tools and Artificial Intelligence — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how ARBR performed against the three products that launched closest to it on the same day.
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.
For a complete overview of ARBR including community comment highlights and product details, visit the product overview.
Shubham Deshmukh