We instrumented 14 production clusters across three cloud providers to measure the real CPU and memory cost of eBPF-based runtime telemetry. Here is what we found — and where the numbers get interesting.
Senior Platform Engineer
Most performance conversations around eBPF are framed in benchmarks that look nothing like production. We measured overhead in live Kubernetes environments with mixed workloads to capture behavior under normal engineering pressure.
The measurement set covered 14 production clusters across AWS, Azure, and GCP. We sampled API services, queue workers, and stateful workloads so latency-sensitive and throughput-heavy paths were both represented.
Each cluster was profiled with and without runtime tracing enabled, using the same deployment windows, autoscaling policies, and traffic profiles. This let us isolate the sensor cost from normal workload drift.
CPU deltas stayed low when probes were constrained to execution and syscall events tied to vulnerability analysis. The largest jumps happened in clusters where teams had already saturated noisy telemetry pipelines before eBPF was introduced.
Memory impact was more stable than expected because event buffering remained bounded. In practice, query patterns and retention strategy created more cost variance than collection itself.
Teams that treated runtime telemetry as a product capability, not a debug mode, performed best. They defined explicit event scopes, controlled retention windows, and aligned dashboards with remediation decisions.
The core takeaway is straightforward: eBPF can run safely in production at meaningful scale if the pipeline is designed for decision quality instead of data volume.
