Configuration Testing & Validation Platform for AI Gateways
Teams operating AI gateways (Bifrost, LiteLLM, Portkey) struggle to test configuration changes before deployment. A misconfigured failover rule or broken routing policy can direct all traffic to a single expensive model or cause complete AI service outage.
Problem Statement
AI gateway configurations are modified via YAML or JSON files with no way to simulate the impact before applying. Teams discover routing errors only in production when requests fail or costs spike. There's no equivalent of infrastructure-as-code testing for AI routing configurations.
The Idea
A configuration testing environment for AI gateways that validates routing rules, failover logic, and budget constraints against synthetic traffic before production deployment, similar to what Terraform plan does for infrastructure.
Why Now
AI gateways became production-critical infrastructure in 2025-2026 with teams routing millions of requests through them. Configuration complexity grew as teams added fallback chains, budget limits, model-specific routing, and A/B testing rules simultaneously.
Target User
Platform engineers and SREs operating AI gateway infrastructure for production LLM applications
Target Market
Companies with production AI gateways routing 100K+ daily LLM requests across multiple providers
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