Problem: traces show only in-process allocator usage, hiding OS-level memory pressure. Solution: attach a normalized 0..=100 OS memory-pressure value to every memory sample and propagate through trace-server for dashboards and eviction logic.
Target Audience
Infrastructure and platform engineering teams running memory-sensitive services (databases, caches, real-time systems) in SMBs and mid-market; SRE orgs at larger enterprises looking for eviction signals and better observability.
Market Size
$35B = 100,000 orgs x $350K AC...
Competition
medium
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Expose OS memory-pressure in traces to inform eviction and dashboards targets a $35B = 100,000 orgs x $350K ACV (global observability/APM market for enterprises and cloud providers) total addressable market with medium saturation and a year-over-year growth rate of 12-20% annual growth in observability, tracing, and profiling adoption driven by cloud-native migration.
Key trends driving demand: Cloud-native operations -- increased adoption of Kubernetes and dynamic workloads raises need for runtime memory signals that inform scheduling and eviction.; eBPF and kernel telemetry -- production-safe, low-overhead kernel observability makes it practical to surface OS-level metrics per trace sample.; Cost and reliability pressure -- rising cloud egress and instance costs push teams to optimize memory usage and avoid costly OOMs and node evictions.; Converged observability -- customers prefer unified traces + infra signals in a single UX rather than stitching dashboards across tools..
Key competitors include Datadog, Honeycomb, Parca (and other continuous profilers like Pyroscope), New Relic, Prometheus + Grafana (workaround).
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