IQ Routing

Trajectory-aware LLM routing that cuts agent cost

Developer ToolsArtificial Intelligence

▲ 0 votes1 commentsLaunched Aug 27, 2026

Daily #21Weekly #95

IQ Routing is a drop-in gateway. We classify each request, serve from cache, and route to the cheapest model that clears the quality bar and completes the task. The insight others miss: an agent is a trajectory, not a stream of independent calls. We route each step based on where it sits in the run: a cheap model on boilerplate, the strongest model on what matters.

AI Analysis

📝 Summary

IQ Routing is a drop-in gateway for LLM agents that classifies requests, serves from cache, and routes to the cheapest model meeting quality standards. Its unique insight treats agents as trajectories rather than independent calls, applying cheap models to boilerplate steps and stronger models to critical ones. It solves key pain points of high inference costs and inefficient routing in complex agent workflows. The value proposition is substantial cost reduction for agent runs while preserving output quality and task success.

📈 Market Timing

In 2025-2026, AI agent adoption is accelerating rapidly alongside high LLM inference costs and demand for efficiency. Routing technology is mature, user needs for cost control are urgent, and the economic environment favors AI optimization tools. This aligns perfectly with industry trends toward scalable, cost-effective agentic AI. Excellent Timing.

✅ Feasibility

Technical difficulty is moderate: gateway and caching are standard, but trajectory-aware logic requires solid AI expertise. Development and operation costs are manageable with cloud infrastructure; caching reduces expenses. Low compliance risks for dev tool. Strong scalability via API gateway model. Overall High feasibility for a team with LLM experience. Rating: High.

🎯 Target Market

Primary users are AI developers, engineers, and teams building LLM agents at AI startups, tech companies, and enterprises. Global distribution with concentration in US/Europe. Industries: software development and AI services. Core pain points are runaway token costs and suboptimal model selection in multi-step agents. High willingness to pay for direct cost savings via subscription or usage-based pricing. Market is large and expanding rapidly within the AI infrastructure sector.

⚔️ Competition

Medium. Direct competitors: 1. LiteLLM (litellm.ai), 2. OpenRouter (openrouter.ai), 3. Portkey (portkey.ai), 4. Helicone (helicone.ai). Advantages: unique trajectory-aware routing for agents delivers better cost-quality tradeoffs than generic routers; drop-in design. Disadvantages: newer entrant may have fewer integrations and less proven track record at scale compared to established players with broader feature sets.

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