From Kubernetes to bare metal, eBPF unlocks high-fidelity, low-overhead performance data—reshaping how APM captures system, network, and runtime insights.

Why eBPF is changing APM now

Application Performance Monitoring (APM) traditionally relies on language agents, SDKs, and sidecars to collect metrics, traces, and logs. Those methods work, but they can add latency, miss kernel-level hotspots, and require per-runtime maintenance. Extended Berkeley Packet Filter (eBPF) changes the model. By running verified, sandboxed programs inside the Linux kernel, eBPF lets teams observe network paths, syscalls, and process behavior with extremely low overhead—often without touching application code. For high-throughput microservices and latency-sensitive user journeys, the difference is practical: more accurate timing, fewer blind spots, and reduced performance tax during incidents and load tests.

How kernel-level visibility improves data quality

Because eBPF runs where events actually happen, it can capture timing and context with fewer context switches and less buffering. That reduces sample loss and makes it easier to see intermittent “tail” latency that traditional polling misses. eBPF hooks allow you to trace TCP state changes, DNS lookups, file I/O, and even packet drops; in aggregate, you get a ground-truth view that aligns with what users experience. Importantly, the verifier ensures eBPF programs are safe before they execute, lowering operational risk relative to historical kernel modules.

Kubernetes: Cilium, Hubble, and flow-aware troubleshooting

In containerized estates, Cilium uses eBPF to implement identity-aware networking. Its Hubble component adds real-time, L3–L7 observability: service-to-service flows, DNS queries, HTTP status codes, and label-rich metadata (namespace, pod, workload). For APM teams, Hubble’s view closes a classic gap: when a trace shows a slow span between services, Hubble can reveal whether that time vanished into retransmits, policy denials, or congestion on a specific node. Pairing Hubble with application tracing yields a shared narrative for SREs and developers: not just “it’s slow,” but where the time actually went across the network and kernel.

Agentless (or agent-lite) capture without code changes

eBPF can observe common protocols and runtime behaviors without injecting language agents into each service. That reduces toil in polyglot stacks and eases upgrades across frameworks and OS versions. It also helps in restricted environments like third-party binaries, legacy apps, or performance-critical services where adding an agent isn’t feasible. Where deep function-level tracing is still desired, teams can blend eBPF signals with OpenTelemetry (OTel) spans—using the eBPF layer to enrich, corroborate, or trigger targeted sampling.

The cost and performance angle

Observability costs rise with cardinality and volume. eBPF isn’t a silver bullet for data costs, but it helps you choose better data:

• Precision first: capture only the flows, syscalls, or socket states associated with slow user transactions.

• Tail-biased sampling: sample more aggressively when you detect rare, high-latency conditions at the kernel or network layer.

• Smarter enrichment: attach container, pod, and namespace labels directly at collection time, so downstream systems don’t have to join multiple datasets.
  The net effect is fewer blind traces, less noisy logging, and higher diagnostic value per byte stored.

Practical integration patterns (that work)

1. Start at the node: Enable an eBPF-based collector on a non-critical Kubernetes node and compare system-level latency to your existing APM timings. Validate overhead during peak traffic.

2. Wire APM with Hubble: Export Hubble flow events and align them with trace IDs or connection tuples from your application telemetry. Highlight “APM span ↔ network flow” correlations in one dashboard.

3. Focus on golden transactions: For checkout, search, or login, define kernel-level symptoms you care about—packet loss, SYN retries, DNS latency—and alert only when they degrade alongside user SLOs.

4. Use the OpenTelemetry Collector: Treat the OTel Collector as the routing brain. Ingest eBPF-derived metrics and traces, redact sensitive attributes, and fan-out to multiple backends (analysis vs. archive) without changing instrumentation.

5. Pin versions, guard experiments: Keep experimental eBPF programs in a separate pipeline. Track kernel versions and eBPF feature gates across clusters to avoid surprises during node autoscaling.

Security and governance considerations

eBPF needs privileged access. Production rollouts should adopt defense-in-depth:

• Program signing and policy: Only allow vetted eBPF programs; record their hashes and maintain change control.

• Least privilege runners: Restrict who can load, attach, and update programs; audit these actions.

• PII discipline at the edge: Redact sensitive payloads at collection time and avoid high-cardinality identifiers (e.g., user IDs) in labels.

• Kernel and distro hygiene: Standardize node images and patch cadence; pre-validate eBPF features and map compatibility during upgrades.

Buyer’s guide: questions to ask vendors

• Do you support eBPF-native capture for network and syscall hotspots, and can it run alongside existing agents?

• How do you correlate eBPF signals with traces and logs (e.g., socket ↔ span, pod ↔ service)?

• What’s the overhead profile at p95 throughput? Do you provide benchmarks on our kernel versions?

• Is Kubernetes traffic visible by service, namespace, and policy? (Hubble-like flow metadata is a strong indicator.)

• Can we stay portable? Validate first-class OTel ingest so you’re not locked into a single backend.

• What guardrails exist for cost? Tail-based sampling, attribute allowlists, and on-ingest aggregation should be standard.

What’s next: eBPF + OTel + AI

The next wave isn’t “eBPF versus APM”—it’s eBPF inside APM. Expect platforms to fuse kernel-level observability with OTel traces and to apply AI on top: summarizing causal chains (“HTTP 500s spike in payment service due to TCP retransmits on node A after policy update”), quantifying user impact, and proposing rollbacks or autoscaling. As eBPF expands beyond Linux into managed edges and data planes, these signals will feel as routine to APM teams as CPU and heap graphs are today.

Closing Thoughts

eBPF brings the operating system’s truth into APM with minimal tax on your workloads. Adopt it where it helps first—Kubernetes networking and high-value user flows—then fold the new context into your existing SLOs, runbooks, and release checks. The goal isn’t collecting more data; it’s collecting decisive data that shortens incidents, prevents regressions, and keeps costs predictable.

Reference sites (5)

Publication: ebpf.io
Topic: What is eBPF?
URL: https://ebpf.io/what-is-ebpf/

Publication: Cilium Docs
Topic: Network Observability with Hubble
URL: https://docs.cilium.io/en/stable/observability/hubble/index.html

Publication: New Relic Blog
Topic: What is eBPF, and why does it matter for observability?
URL: https://newrelic.com/blog/best-practices/what-is-ebpf

Publication: Eunomia Blog
Topic: The eBPF Evolution and Future: From Linux Origins to Cross-Domain Adoption
URL: https://eunomia.dev/en/blogs/ebpf-2024/

Publication: F5 Company Blog
Topic: F5 acquires MantisNet to enhance cloud-native observability in the F5 Application Delivery and Security Platform
URL: https://www.f5.com/company/blog/f5-acquires-mantisnet-to-enhance-cloud-native-observability-in-the-f5-application-delivery-and-security-platform

Author: Serge Boudreaux — AI Hardware Technologies, Montreal, Quebec
Co-Editor: Peter Jonathan Wilcheck — Miami, Florida