eBPF went from kernel nerd toy to mainstream SRE tool in record time. The CNCF landscape exploded with projects—Cilium, Pixie, Parca, Falco, BCC—while case studies from Netflix, Cloudflare, and Shopify showed what eBPF can do for security and observability. The question we kept hearing from clients: “How do we use eBPF without becoming kernel engineers?”
Here is how Cloudythings rolls out eBPF superpowers responsibly.
Choose the right use cases
We focus on three high-impact scenarios:
- Incident triage: On-demand profiling, network tracing, and syscall auditing.
- Capacity planning: High-cardinality visibility into resource usage without instrumentation overhead.
- Zero-trust enforcement: Runtime policy enforcement with minimal latency.
If the use case does not tie directly to reliability or security outcomes, we park it. Purpose drives adoption.
Assemble the tooling stack
Our reference stack blends:
- Cilium for Kubernetes networking, network policy, and Hubble observability.
- Pixie (now a New Relic project) for auto-instrumented application tracing.
- Parca (or Pyroscope) for continuous profiling.
- Falco or Tetragon for runtime security alerts.
- bcc / bpftrace CLI scripts for ad-hoc deep dives.
We deploy everything via GitOps with customized Helm charts. RBAC is strict—only SREs and security engineers access dangerous tooling.
Integrate with incident response
During incidents, speed matters:
- Runbooks include eBPF probes (e.g.,
pixie run px/http_data) as standard steps. Output snapshots attach to the incident timeline. - ChatOps commands (e.g.,
/px capture checkout) allow responders to trigger captures without SSH. We log usage for audit trails. - Automated triggers run bpftrace scripts when burn-rate alerts fire, capturing context even if engineers miss the moment.
This mirrors Shopify’s narrative about using eBPF to debug Ruby app latency—they codified probes as reusable incident tools.
Feed continuous intelligence
We store eBPF telemetry in data lakes:
- Continuous profiling data (Parca) reveals hot functions and container-level CPU usage. We correlate with deployment markers.
- Network flow logs (Hubble) feed Grafana Loki, powering security dashboards and SLO monitoring for service-to-service latency.
- Syscall events (Falco/Tetragon) integrate with SIEM platforms to detect privilege escalation or crypto-mining attempts.
We treat this data like product metrics—versioned schemas, retention policies, and access controls.
Manage overhead and safety
eBPF runs in-kernel; mistakes hurt. We enforce:
- Resource budgets via Kubernetes
PodSecurityPolicies(or PSA) and cgroup limits on agents. - Gradual rollout: Enable new probes in staging, then canary a subset of nodes before fleet-wide adoption.
- Observability of the observability: Dashboards track agent CPU, memory, and dropped event rates.
Following Cloudflare’s “prove zero overhead” mantra, we refuse to ship probes that add measurable latency.
Train the humans
eBPF jargon intimidates. We run:
- Hands-on labs using Katacoda-style scenarios (inspired by Liz Rice’s excellent workshops).
- Office hours with kernel experts who demystify BPF maps, programs, and verifier errors.
- Documentation in Backstage with copy-paste probe recipes and interpretation guidance.
Engineers learn what questions eBPF answers—and which ones still need code instrumentation.
Govern like any platform feature
We treat eBPF as part of our platform roadmap:
- Stakeholders review new probes for privacy/compliance (GDPR, HIPAA).
- Change management records justify enabling deep packet inspection or TLS decryption.
- Incident reviews evaluate whether probes provided value and adjust coverage.
The payoff? Faster incident resolution, confident capacity planning, and stronger runtime security—all without turning every SRE into a kernel developer. eBPF is no longer exotic; it is another reliable tool when deployed with intention.