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Custom LLM Cost-per-Task Eval & Routing Platform
A SaaS platform that lets engineering teams define custom task-specific evaluations, run them across frontier models, and get real cost-per-task and quality analytics. It addresses the core pain of untrustworthy vendor benchmarks by giving teams their own private, task-specific leaderboard with dollar-cost tracking and optional smart routing.
Pourquoi c'est important
You lead an engineering team building AI-powered products, spending thousands on LLM APIs every month. Every time a new model drops, you are bombarded with cherry-picked benchmark charts from providers claiming superiority. You know these charts do not reflect your actual workload. When you compare models manually, you discover one provider uses 3x more tokens than another for the same task, obliterating your budget. You need a way to run your own task-specific evaluations, track true cost per successful outcome, and catch model drift over time, but existing ML platforms are too heavy and generic leaderboards are too broad.
- · Conçu pour Engineering teams and product managers at AI-heavy startups and enterprises who spend >$1K/month on LLM APIs and need to optimize model selection for cost and quality..
- · Monétisation la plus probable : SaaS subscription with usage-based tiering.
La douleur · Récit
You lead an engineering team building AI-powered products, spending thousands on LLM APIs every month. Every time a new model drops, you are bombarded with cherry-picked benchmark charts from providers claiming superiority. You know these charts do not reflect your actual workload. When you compare models manually, you discover one provider uses 3x more tokens than another for the same task, obliterating your budget. You need a way to run your own task-specific evaluations, track true cost per successful outcome, and catch model drift over time, but existing ML platforms are too heavy and generic leaderboards are too broad.
Détail du score
Signal du marché
Mise sur le marché
Technical founders and senior engineers at AI-first startups spending $1K-$50K monthly on LLM APIs who need to justify model selection to stakeholders
~100K potential users globally in AI-first startups and enterprise innovation teams
Hacker News launch targeting early-adopter dev community
$99/month for starter tier, $499/month for teams with routing
25 paying users within 30 days of HN launch
Périmètre MVP · 1–2 semaines
- Set up FastAPI backend with multi-provider API key management
- Build custom eval runner that can send a prompt to OpenAI and Anthropic and track tokens/cost
- Create 5 pre-built eval templates for common SWE tasks (code generation, bug fix, test writing)
- Design simple dashboard showing cost-per-task and quality score comparison
- Deploy MVP to staging and test with 3 internal eval suites
- Add model drift detection by re-running evals on a schedule and diffing results
- Implement smart routing API proxy that directs requests based on cost thresholds
- Build user onboarding flow with API key setup and eval template selection
- Add SOC 2-friendly data handling (no prompt logging, encrypted API keys)
- Launch on Hacker News with a free tier for individual developers
Différenciation
Pourquoi cela pourrait échouer
Auto-contre-argument — le signal de confiance le plus important
- 1Established platforms like Braintrust or Weights & Biases could add cost-per-task features quickly, leveraging existing enterprise relationships to out-distribute a new entrant.
- 2Engineering teams may treat model selection as a one-time decision rather than a recurring need, reducing retention after initial evaluation.
- 3Non-deterministic model outputs make it extremely difficult to provide consistent eval results, potentially undermining user trust in the platform's accuracy.
Résumé des preuves
Comment l'IA a synthétisé cet aperçu — pas de citations textuelles
Roughly 14 commenters expressed deep distrust in vendor-published benchmarks, noting that labs cherry-pick favorable metrics. Multiple users referenced Artificial Analysis for independent pricing data but highlighted that it lacks task-specific and cost-per-task granularity. About 5 commenters emphasized the critical need to build custom evals to catch model drift and measure real-world performance. Several users discussed extreme cost disparities, with one noting a 7x dollar difference between models for the same evaluation suite.
Plan d'Action
Validez cette opportunité avant d'écrire du code
Prochaine Étape Recommandée
Construire
Signaux de demande forts. Vraie douleur et volonté de payer détectées — commencez à construire un MVP.
Kit de Textes pour Landing Page
Textes prêts à coller, basés sur le langage réel de la communauté Reddit
Titre Principal
Custom LLM Cost-per-Task Eval & Routing Platform
Sous-titre
A SaaS platform that lets engineering teams define custom task-specific evaluations, run them across frontier models, and get real cost-per-task and quality analytics. It addresses the core pain of untrustworthy vendor benchmarks by giving teams their own private, task-specific leaderboard with dollar-cost tracking and optional smart routing.
Pour Qui
Pour Engineering teams and product managers at AI-heavy startups and enterprises who spend >$1K/month on LLM APIs and need to optimize model selection for cost and quality.
Liste des Fonctionnalités
✓ Custom eval suite builder with pre-built SWE task templates ✓ Multi-provider API runner with automatic token and cost tracking ✓ Private task-specific leaderboard showing true cost-per-successful-task ✓ Model drift detection alerts when a provider's model changes behavior ✓ Smart routing API proxy that routes requests by cost/quality thresholds
Où Valider
Partagez votre landing page sur r/HN · front_page — c'est exactement là que ces points de douleur ont été découverts.
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