Stop flaky runs from skewing benchmarks: percentile-first comparisons + retry buffering

Executive Summary

Many engineering organizations that run benchmarks in CI struggle with flaky runs and intermittent tail latency that skew comparisons and waste developer time; this problem is acute for teams measuring p95/p99 and affects an addressable market of roughly 70,000 SMB, mid-market and enterprise engineering orgs (implying a $4.2B market at ~$60K ACV). The consequence is noisy release signals, avoidable rollbacks, and A/B experiments that under- or over-claim performance regressions, which frustrates both developers and SREs. You could build a CI-native benchmarking platform that performs percentile-first comparisons and maintains a retry buffer: compute percentile distributions (p50/p95/p99) as primary signals, automatically re-run borderline or noisy jobs into a short-lived buffer, and apply statistically coherent, non-parametric tests on buffered percentiles before surfacing pass/fail. The product would include developer-friendly failure explanations, programmable thresholds, lightweight SDKs for common test runners, and integrations with observability and synthetic/RUM datasets to correlate lab and production signals. This market is attractive now because of the shift-left performance trend, SRE/observability convergence, and a growing focus on tail latency; our internal market assessment scores this opportunity 88/100 with revenue potential 82/100. To stand out you must be rigorous about statistics and transparent about limits, ship excellent CI ergonomics and low operational cost, and offer an open SDK + opinionated defaults—honest challenges include educating users on percentile-first reasoning, avoiding false confidence from buffered retries, and managing additional compute/storage costs compared with simple mean-based comparisons.