Executive Summary

Backend engineers, SREs and platform teams at mid-to-large engineering organizations routinely struggle to distinguish application-level allocation issues from OS-level memory contention because distributed traces rarely include normalized kernel memory-pressure signals. That gap leads to misdirected debugging, unnecessary instance upsizing, and blurred SLO root causes for teams running hundreds to thousands of services. You could build an eBPF-based collector that samples low-overhead OS memory-pressure signals (PSI, page-reclaim metrics, swap activity, cgroup memory.stat, OOM scores) and normalizes those samples before attaching them as events or attributes to OpenTelemetry traces and spans. Complementing the collector with a pipeline to compress and store samples, a correlation engine that surfaces likely OS-driven incidents at the trace/span level, and SDK/agent integrations for popular runtimes and orchestrators would make the data actionable for engineers. The timing is favorable: we estimate a $12.0B addressable spend (500,000 engineering orgs × $24K ACV of enterprise observability/APM) and convergence of three trends — eBPF adoption, cloud cost scrutiny, and OpenTelemetry standardization — that lower collection cost and increase downstream consumption. Our market assessment (score 88/100) and revenue potential (78/100) suggest a real commercial opportunity, though competition is medium so execution matters. To stand out, focus on cross-kernel and cross-cloud normalization, truly low-overhead sampling, and attaching pressure samples directly to spans so developers see immediate context; an open-source collector plus paid enterprise integrations and prebuilt queries for SLO impact will ease adoption. Be honest about challenges: convincing teams to add a new telemetry source, handling multi-tenant security and privacy, managing storage costs for high-cardinality samples, and integrating with incumbent APM platforms will all require deliberate product and go-to-market work.

Executive Summary

Market Opportunity

Report OS memory pressure in traces by normalizing & attaching samples targets a $12.0B = 500,000 engineering orgs x $24K ACV (enterprise observability/APM spend) total addressable market with medium saturation and a year-over-year growth rate of 12-18% annual growth in observability/APM and systems telemetry spending.

Key trends driving demand: eBPF adoption -- easier, lower-overhead kernel-level telemetry enables richer OS signals in production.; Cloud cost focus -- teams seek signals that separate app allocation issues from OS-level memory contention to reduce waste and SLO incidents.; Tracing standardization -- OpenTelemetry and trace pipelines make adding new sample types consumable by dashboards and backends.; Edge and multi-tenant workloads -- increased need to detect and react to OS memory pressure across heterogeneous hosts..

Key competitors include Datadog (APM & Infrastructure), Honeycomb, Prometheus + node_exporter / Grafana, eBPF tools / Pixie (acquired) / bpftrace.

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Report OS memory pressure in traces by normalizing & attaching samples

Executive Summary

Report OS memory pressure in traces by normalizing & attaching samples

Executive Summary

Market Validation

Market Opportunity

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