EvalBench
Side-by-side LLM testing with regression detection across providers.
● The Problem
AI product teams switch between OpenAI, Anthropic, and Google models but have no standardized way to compare quality, cost, and latency across providers. A model upgrade that improves one use case often silently degrades another. There is no "unit test" equivalent for LLM outputs.
● The Solution
A testing sandbox where teams define evaluation criteria, run prompts against multiple models side-by-side, and get scored comparisons. Detects regressions when switching models or updating prompts. Tracks cost per evaluation to optimize model selection.
Key Signals
MRR Potential
$20K-100K
Competition
Medium
Build Time
1-3 Months
Search Trend
rising
Market Timing
Every company shipping LLM features is discovering that prompt engineering without systematic testing is unsustainable. Model provider lock-in anxiety is driving multi-model strategies.
Similar Ideas
Related Market Trends
$12.8B market growing at 40% CAGR. AI development platforms projected at $68B by 2030.
EU AI Act enforcement starts Aug 2026. Penalties up to 7% of global revenue. Every AI company needs compliance.
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