Teams building production agentic pipelines don't know which model gives the best performance per dollar for their specific use case, and switching costs are high.
A routing/orchestration layer that profiles your agentic workload, runs micro-benchmarks across models, and automatically routes tasks to the optimal model based on cost-performance tradeoffs. Includes dashboards showing cost-efficiency curves per task type.
Usage-based SaaS pricing tied to API calls routed, plus enterprise tier for custom benchmarking
Agentic pipeline costs are spiraling. A single agent run can cost $0.50-$5.00 with frontier models, and teams run thousands daily. The Reddit data showing 11x cost differences between comparable models proves teams are bleeding money. Every AI engineering team lead is being asked 'why is our LLM bill so high?' Cost is the #1 blocker to scaling agent deployments to production.
TAM is significant but still emerging. ~50K companies actively building agentic systems in production as of 2026, growing fast. At $500-5000/mo average contract value, that's $300M-3B TAM. The constraint: the market is growing INTO existence — many teams are still in experimentation. But the trajectory is steep and the adjacent LLMOps market (observability, gateways) is already $1B+.
Teams spending $10K-100K+/mo on LLM APIs will eagerly pay 5-10% of that for a tool that cuts costs 30-50%. The ROI is immediate and measurable — this sells itself with a cost savings dashboard. Portkey and similar tools already prove teams pay for LLM middleware. The usage-based model aligned with API spend makes adoption frictionless.
This is genuinely hard to build well. The micro-benchmarking engine needs to be statistically rigorous, fast, and cheap to run. Pipeline-aware routing requires understanding agentic frameworks (LangGraph, CrewAI, AutoGen, custom). Quality evaluation at scale is an unsolved problem — who judges if a cheaper model's output is 'good enough'? An MVP proxy with basic A/B testing and cost dashboards is doable in 8 weeks, but the intelligent routing that actually delivers value requires significant ML/eval infrastructure. Solo dev risk is high for the full vision.
Existing tools route individual requests. NOBODY is optimizing at the pipeline/agent level — understanding that step 3 of your agent chain is cost-insensitive (use cheap model) while step 7 requires frontier quality. The micro-benchmarking on YOUR actual data is also a clear gap. Current solutions require manual model selection or use generic benchmarks. The 'agentic-native' positioning is wide open.
Usage-based SaaS tied to API calls is inherently recurring and grows with the customer. As teams scale their agent deployments, routing volume increases automatically. Model landscape changes monthly (new releases, price cuts), so continuous re-optimization is needed — customers can't churn because the optimization problem never stops. Very strong natural retention dynamics.
- +Massive and quantifiable ROI — 'we saved you $X this month' is the easiest product to sell
- +Clear gap in market — pipeline-level optimization is unaddressed by all current competitors
- +Usage-based revenue model scales automatically with customer growth
- +Strong tailwinds: model proliferation, cost pressure, and agentic adoption all accelerate demand
- +Pain signals are loud and public — Reddit, Twitter, Hacker News full of LLM cost complaints
- !LLM providers may build this themselves — OpenAI/Anthropic/Google could add cost-optimization routing natively
- !Technical complexity of quality evaluation is the hardest unsolved problem — bad routing recommendations destroy trust instantly
- !Agentic framework fragmentation (LangGraph vs CrewAI vs AutoGen vs custom) means broad integration burden
- !Race to the bottom on model pricing could shrink the optimization delta over time
- !Chicken-and-egg: need production traffic to optimize, but teams won't route production through unproven tool
AI model router that automatically selects the best LLM for each request based on quality requirements. Uses a learned routing model to predict which LLM will perform best per query.
AI gateway and observability platform. Provides a unified API to 200+ LLMs with fallbacks, load balancing, caching, and cost tracking. Acts as middleware between your app and LLM providers.
Unified API for 100+ LLMs with a single endpoint. Offers optional auto-routing and provides transparent per-token pricing across providers.
ML-powered model router that predicts which LLM will give the best response for each query. Trains routing models on evaluation data to maximize quality while reducing costs.
Open-source unified LLM API proxy supporting 100+ providers. Handles fallbacks, load balancing, spend tracking, and budget management.
A lightweight proxy that sits between agentic pipelines and LLM APIs. Week 1-2: Build proxy with multi-provider support (OpenAI, Anthropic, Google, open-source). Week 3-4: Add cost tracking dashboard per pipeline step with real-time spend visualization. Week 5-6: Implement A/B testing framework — for any pipeline step, split traffic between 2 models and compare output quality using automated evals (LLM-as-judge + task-specific metrics). Week 7-8: Build recommendation engine that suggests cheaper model substitutions with estimated quality impact and cost savings. Ship as a Python SDK + web dashboard. Target LangGraph/LangChain users first for framework integration.
Free tier: proxy + cost dashboard for up to 10K requests/mo (land) → Pro $99-499/mo: A/B testing, benchmarking, optimization recommendations (expand) → Usage-based: $1-2 per 1000 routed requests above free tier (scale with customer) → Enterprise $2K+/mo: custom benchmarking, SLA guarantees, SSO, dedicated support, on-prem deployment (upsell)
8-12 weeks to first paying customer. The cost dashboard alone (weeks 1-4) is enough to get design partners. First revenue from teams spending $5K+/mo on LLM APIs who can see immediate savings. Enterprise contracts at 6+ months.
- “GLM-5 nearly matched Claude Opus 4.6 at 11× lower cost”
- “the cost-efficiency curve here is real”
- “Kimi-K2.5 actually tops the revenue-per-API-dollar chart at 2.5× better than the next model”
- “There's no frontier model moat. The only real moats left are infrastructure, compliance, and unit economics”