Cut LLM eval cost & complexity with orchestrated evaluators

Executive Summary

Many development and AI teams struggle to evaluate large language models cost-effectively as new models appear weekly and evaluation pipelines become fragmented and expensive. Across an addressable market of roughly 350,000 teams (≈$10.5B at $30,000 ACV) the pain shows up as uncontrolled token bills, duplicated human labeling, and slow model selection cycles. You could build an orchestrated-evaluator platform that composes multiple automated evaluator models, applies caching and adaptive sampling to cut token and compute usage, and provides standardized metrics, experiment management, and a human-in-the-loop escalation path. Conservative targets would be a 2–5x reduction in per-eval compute costs and a ~50% faster evaluation cycle versus ad hoc scripts, with full audit trails and SDKs for CI/CD integration. The timing is attractive: model proliferation forces continuous re-evaluation, token prices keep rising, and instruction-following evaluator models are now good enough to produce reliable signals at scale. The market is large and accessible (market score 88/100, revenue potential 92/100), but competition is medium and incumbent workflows are entrenched, so you must demonstrate clear ROI and build trust; focusing on orchestration and caching, rigorous evaluator calibration, benchmark suites, and enterprise-grade privacy/compliance will be your most defensible play, though expect challenges around calibration, bias, and customer onboarding.