# Decision Memo: Cost router that picks the cheapest capable AI model Full report: https://ideanavigatorai.com/ideas/cheapest-ai-model-router/ Recorded: Not recorded ## Decision - Team verdict: Park - Validation verdict: Research (59/100) - Confidence: 55% - Recommendation: Keep this parked until the team has evidence for the next validation step: Take three companies' last month of API logs, replay them through the router in shadow mode, and present the audited savings-versus-quality delta as the sales artifact. ## Team rationale No team rationale recorded yet. ## Reviewers - No named reviewers recorded. ## Source anchors - Buyer: Engineering lead at a company whose LLM API bill is growing faster than usage - Market: AI infrastructure / LLM ops - Problem: Teams route every request to a flagship model by default, paying 10-30x more than necessary for tasks a small model handles identically, because per-task capability testing is tedious and model prices change monthly. - Thesis: Cost router that picks the cheapest capable AI model should be tested as a narrow first-win workflow for Engineering lead at a company whose LLM API bill is growing faster than usage. - Source: https://openrouter.ai/ - Source: https://docs.claude.com/ ## Validation rubric Rubric version: INAV-VALIDATION-2026-06-04 ### Demand signal - 5.5/10 (24% weight) Demand looks thin because the report has 2 source-backed signal(s), an editorial confidence of 55/100, and a defined buyer in AI infrastructure / LLM ops. - Price-per-token differs by more than an order of magnitude between model tiers that score identically on many production task types. - Target buyer: Engineering lead at a company whose LLM API bill is growing faster than usage ### Problem severity - 6.3/10 (22% weight) Problem severity is thin when the buyer pain, customer value, and dream-outcome scores are combined. - Teams route every request to a flagship model by default, paying 10-30x more than necessary for tasks a small model handles identically, because per-task capability testing is tedious and model prices change monthly. - Price-per-token differs by more than an order of magnitude between model tiers that score identically on many production task types. ### Willingness to pay - 5.5/10 (20% weight) Willingness to pay is weak; the model has a monetization hypothesis, but it must still be proven through paid pilots or explicit pricing objections. - Percentage-of-savings pricing or flat platform fee per routed volume tier. - Take three companies' last month of API logs, replay them through the router in shadow mode, and present the audited savings-versus-quality delta as the sales artifact. ### Competitive saturation - 6/10 (18% weight) No source-backed direct match is recorded yet, so saturation risk is treated as unknown rather than proof of novelty. - Existing-product check has no named direct match. - Competitive score rewards a narrow wedge, not absence of research. ### Feasibility - 6.2/10 (16% weight) Feasibility is thin for a moderate build if the MVP is limited to the first measurable workflow. - Take three companies' last month of API logs, replay them through the router in shadow mode, and present the audited savings-versus-quality delta as the sales artifact. - OpenRouter and provider-native routing features already occupy adjacent ground. ## Market gap Underserved segments: - Engineering lead at a company whose LLM API bill is growing faster than usage who still run the workflow in spreadsheets, generic docs, email, or chat threads. - Small teams in AI infrastructure / LLM ops that feel the pain weekly but are too narrow for broad incumbents. - New adopters who need guided proof before committing to a larger platform. Feature gaps: - A narrow workflow that reaches value without configuration-heavy onboarding. - A buyer-facing proof artifact that shows time saved, risk reduced, or communication improved. - A handoff path from manual concierge service to repeatable software. Differentiation levers: - Use specificity as the wedge: one buyer, one workflow, one measurable result. - Show proof earlier than broad competitors with before-and-after examples and small pilot data. - Keep implementation lighter than incumbent suites or generic AI assistants. ## Roast and risks Promising enough to test, not strong enough to build broadly. Blind spots: - OpenRouter and provider-native routing features already occupy adjacent ground. - A broad AI assistant can flatten differentiation unless the wedge is painfully specific. - The first release can become a generic dashboard if the job is not named tightly. Hard questions: - Who wakes up already trying to solve this? - What do they stop paying for or stop doing when this works? - What proof would make a skeptical buyer trust it in one screen? - What is the smallest paid version of this idea? ## Kill criteria - Fewer than five qualified buyers agree to discuss the workflow after targeted outreach. - No buyer can name a current cost in time, money, risk, or reputation. - The first demo does not produce a clear next step, paid pilot, or specific objection. ## Offer ladder - **Lead magnet (Free)**: Cost Router That Picks The Cheapest Capable Ai Model checklist Goal: Capture qualified leads and learn the buyer's exact language. Value: Helps Engineering lead at a company whose LLM API bill is growing faster than usage audit the painful workflow before buying software. - **Frontend offer ($19-$99)**: Concierge review or paid template Goal: Validate urgency, workflow fit, and willingness to pay. Value: Delivers the first useful output manually before automation is trusted. - **Core offer ($49-$499/month)**: Cost router that picks the cheapest capable AI model focused SaaS Goal: Create the recurring revenue product after the narrow wedge survives tests. Value: Turns the recurring manual workflow into a repeatable product loop. - **Continuity ($99-$1,000/year add-on)**: Monitoring, benchmarks, and monthly reporting Goal: Increase retention and make the product part of a routine. Value: Keeps the buyer engaged with ongoing proof, saved time, or reduced risk. - **Backend offer (Custom)**: Done-with-you setup, agency, or team rollout Goal: Capture higher-value accounts once the productized wedge is proven. Value: Adds implementation help, integrations, and workflow migration.