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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.
Why this matters
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.
- · Built for 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..
- · Most likely monetization: SaaS subscription with usage-based tiering.
The Pain · Narrative
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.
Score Breakdown
Market Signal
Go-to-Market
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
MVP Scope · 1–2 weeks
- 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
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 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.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
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.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Build
Strong demand signals detected. Real pain, real willingness to pay — start building an MVP.
Landing Page Copy Kit
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
Custom LLM Cost-per-Task Eval & Routing Platform
Sub-headline
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.
Who It's For
For 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.
Feature List
✓ 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
Where to Validate
Share your landing page in r/HN · front_page — that's exactly where these pain points were discovered.
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