An eBPF sensor's overhead depends on the events it captures, the workload it observes, and the pipeline that handles its output. This guide describes a measurement approach for your environment; it does not report benchmark results or promise a universal overhead percentage.
01
Build a Comparable Baseline
Start in a representative test environment. Record node sizing, kernel versions, application images, sensor version, enabled detections, and the load profile. Keep those inputs consistent when comparing runs with the sensor disabled and enabled.
Repeat both conditions rather than comparing a quiet baseline with a busy instrumented window. Record autoscaling, deployments, and other changes that could explain a difference. Include API services, workers, and stateful workloads if they are part of the intended deployment.
02
Measure Application Impact and Telemetry Delivery
Capture CPU and memory for the node, application, and sensor separately. Compare application throughput and latency under the same offered load; a sensor CPU measurement alone does not describe the application's experience.
Record event rate, capture loss, queue pressure, and export failures wherever the sensor and receiver expose them. Low resource use with missing events is a different outcome from low resource use with complete delivery. Keep the observation window and sampling interval with each measurement.
kubectl top nodes
kubectl top pods -A --containers- Compare CPU and memory at equivalent application load.
- Include application latency percentiles and throughput, not just averages.
- Review capture and delivery limitations alongside resource measurements.
03
Tune the Event Scope and Repeat the Test
Define the detections and investigation evidence you need before tuning collection. Change one setting at a time, then repeat the same load profile. Record any reduction in coverage alongside the resource change so a faster configuration is not mistaken for equivalent evidence.
Event scope is also decided by the sensor you pick. Falco evaluates events against rules in userspace, while Tetragon filters in the kernel before an event leaves it; our Falco vs Tetragon comparison covers what each one's overhead scales with.
Before rollout, agree on acceptable resource use, application impact, and capture or delivery limitations. Keep the measurements and configuration with the evaluation so another engineer can repeat it. Primod's deployment workflow describes the coverage and delivery questions to review for its sensor.
Methodology notes
- Profile with-and-without tracing under identical deployment windows to isolate telemetry overhead from workload drift.
- Capture CPU, memory, and event-queue behavior per workload class (API, worker, and stateful services).
- Repeat each measurement across clusters and clouds before drawing conclusions, to avoid one-off cluster bias.
Sources and references
- What Is eBPF?
eBPF Foundation
- Linux Kernel BPF Documentation
Linux Kernel Documentation
- Kubernetes System Metrics
Kubernetes Documentation
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