/ ACCELERATOR· RouteAIQ

RouteAIQ — Intelligent model routing. Every query to the right model, at the right cost.

RouteAIQ scores how relevant each query is to your project and routes it to the right model tier — cheap, mid, or premium — with per-team budgets, burn-rate downgrades, and full cost logging.

A project-policy router across Amazon Bedrock (Claude, Nova) and vLLM-hosted open-weight models (Qwen, Llama). Hybrid relevance scoring, three-tier routing, budget enforcement, and outcome logging — governed, model-agnostic, and built for the enterprise.

Sending every query to a frontier model is how AI budgets evaporate. RouteAIQ measures how closely each query aligns with the project's documentation, then routes it to the cheapest tier that will do the job — premium only where it earns its cost, with a hard budget ceiling the system enforces on its own.

Three model tiers

Each project defines its own model pool, organised by cost tier. The router only ever picks within the tier the relevance score and budget allow.

Premium

High-relevance, complex, project-critical work.

  • Typical models: Opus 4.8, GPT-4o.
  • Approx. cost: $0.015–$0.060 per query.

Mid

Medium-relevance or ambiguous queries.

  • Typical models: Sonnet 4.6, GPT-4o-mini.
  • Approx. cost: $0.003–$0.010 per query.

Cheap

Off-topic queries and simple in-scope work.

  • Typical models: Qwen-7B, Haiku 4.5, Nova Lite.
  • Approx. cost: $0.0005–$0.002 per query.

Selection strategy

Within a tier, route by the admin's chosen lever.

  • Cheapest — lowest cost per input token.
  • Lowest latency — best p50.
  • Round-robin & availability-aware failover.

The stack

RouteAIQ is model-agnostic by design, spanning managed and self-hosted inference so you inherit better models without re-architecting.

Phased rollout

Phase 1 delivers project-policy-based routing — the project registry, identity mapping, hybrid relevance scoring with percentile-calibrated thresholds, three-tier routing, budget tracking with auto-downgrade, per-user caps, retry escalation, and comprehensive logging. Phase 2 adds complexity-aware model selection (query complexity plus benchmark scores) and learned, per-model capability profiles, plus calibrated confidence scoring.

Where it fits

RouteAIQ sits in front of your agents and LLM apps as the routing and cost-governance layer. It is governed by Responsible AI controls, its routing quality is measured by AI Eval Service, and it pairs naturally with the GenAI Delivery Factory for production rollout.

Related resources

RouteAIQ is one of six production-ready accelerators we run in the open. See it routing live — and explore the full accelerator suite →

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