ARBR launches open control plane for AI requests

ARBR’s strongest idea is making model optimization an evidence-backed, human-approved workflow instead of an opaque routing rule. It targets the operational gap between basic proxies and production-grade AI governance.

• –Drop-in OpenAI compatibility minimizes migration work for existing SDKs and agent frameworks
• –Request-level cost, latency, quality, and failure data supports measurable model-switching recommendations
• –Shadow tests, guarded canaries, regression gates, and rollback make provider changes safer
• –Self-hosting and MIT licensing reduce vendor lock-in, while LiteLLM support allows teams to layer ARBR onto existing infrastructure
• –The platform’s value will depend on evaluation quality and production maturity; traffic-to-training-data capture remains roadmap-only