Surface memory sampling in build trace outputs to locate regressions

Modern engineering organizations—about 800,000 teams worldwide—run unified build servers for monorepos and frequently struggle to locate memory regressions that appear only during CI; build traces today capture timing and I/O but rarely surface transient heap growth, peak RSS, or allocation hotspots that cause flaky tests and OOMs. Build engineers, platform teams, and SREs spend significant time bisecting builds and chasing tail-memory issues that are expensive to reproduce locally. You could build a system that continuously samples memory during build tasks and annotates build trace outputs with footprint timelines, sampled allocation stacks, and mappings to source files, dependencies, and task graphs. By leveraging low-overhead techniques such as eBPF-based sampling and statistical profilers, the product could keep added wall-time under ~5%, emit compact artifacts that integrate with CI dashboards and tools like Bazel, Gradle, Nx, and provide actionable regression diffs instead of raw traces. This market is attractive now: the addressable market is approximately $4.8B (800k teams × $6K ACV), we rate market strength 90/100 and revenue potential 78/100, and trends—monorepo consolidation, shift-left performance debugging, and practical sampling tech—make adoption feasible. To stand out, prioritize deterministic mapping from samples to build tasks, concise developer-facing conclusions (for example, “dependency X in task Y increased peak RSS by 40%”), and turnkey CI integrations and SDKs so platform teams can adopt without major toolchain changes. Honest challenges include keeping overhead and noise low across platforms, accurate symbolization and reproducibility for optimized builds, and convincing cost-sensitive teams of ROI, but the low competition and clear willingness to pay at roughly $6K/year per team make this a focused, defensible niche if you deliver accuracy, ergonomics, and easy integration.