UniqRouter

Teams wire one model — usually the most expensive — into every tool and feature. When that provider has an outage, is rate-limited, or ships a breaking change, the AI simply stops. There's no visibility into how much each team spends, simple tasks run on premium models, and every new model means changing code and tools. Cost climbs while reliability suffers.

• ✓Your AI never stops — if a provider is down, slow, or rate-limited, requests fail over to a backup model or vendor automatically, with no code change.
• ✓Cuts AI spend 20–40% — cheap models handle simple work, caching and context compression reduce tokens, and premium models are used only where they matter.
• ✓One managed endpoint for every model and team — Claude, Codex, Z.ai/GLM, Gemini, or local models, governed with budgets and policies in one place.
• ✓Full visibility — measure exactly how much AI each team, project, or feature uses, and forecast the capacity you actually need.
• ✓No vendor lock-in and no re-plumbing — swap or add models behind the same API your tools already call.

• Multi-provider failover — keep coding agents like Claude Code, Codex, and ZCode running even when a provider goes down.
• Model aliases — ask for “opus” and the router serves Opus 4.6 with automatic backups; your tools never change when models do.
• Cost-aware routing — set a budget or quality target and the router picks the model to hit it.
• Semantic caching — reuse answers for repeated or similar prompts instead of paying for them twice.
• Context compression — trim and compress prompts before they're sent, cutting tokens with no quality loss.
• Prompt optimization — automatically refine prompts for cheaper, better responses.
• Per-team budgets, caps & alerts — stop runaway spend before it happens.
• Usage analytics & chargeback — spend broken down by model, team, and feature.
• Managed MCP tools — give teams curated MCP servers through the router, with per-tool cost tracking and limits.
• One key, many model types — chat, coding, embeddings, and vision governed together without overspending.

Any team running AI in production — especially those using multiple models or AI coding agents — that wants reliability and predictable cost without re-plumbing their tools. We run it on our own internal work.