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/

ARBR

Control Every AI Request

Control Every AI Request