eBPF (extended Berkeley Packet Filter) is a technology that lets you run small, sandboxed programs directly inside the Linux kernel, attached to events like system calls, network packets, and function calls, without writing a kernel module or patching kernel source code. Before any eBPF program runs, an in-kernel verifier checks it exhaustively: no unbounded loops, no out-of-bounds memory access, no way to crash the kernel.
For eBPF Kubernetes observability specifically, this matters enormously. A standard Kubernetes cluster generates constant network traffic, process activity, and application behaviour across every pod and node, and until eBPF matured, getting visibility into all of that meant either instrumenting every application manually or injecting a sidecar proxy into every pod. eBPF changes the trade-off entirely: a single kernel-level program per node can observe everything happening on that node, across every container, with near-native performance and zero application code changes.
Where eBPF Fits in Kubernetes Architecture
To understand why eBPF Kubernetes observability is such a significant shift, it helps to place it within standard kubernetes architecture. Every Kubernetes cluster relies on a Container Network Interface (CNI) to handle pod networking, and historically, most CNIs (Flannel, Calico) relied on iptables to implement Kubernetes Services, each packet evaluated sequentially against potentially thousands of rules on a large kubernetes cluster.
eBPF-based CNIs, most notably Cilium, replace this iptables-based approach entirely. Instead of sequential rule matching, Cilium loads eBPF programs directly into the kernel’s networking path, making per-packet decisions at near-native speed. This isn’t a bolt-on observability tool sitting beside your kubernetes architecture, it becomes the networking layer itself, which is precisely why it can offer such deep, low-overhead visibility into everything flowing through the cluster.
Why eBPF Beats Sidecar-Based Observability
Before eBPF matured, deep observability in a service-mesh architecture typically meant injecting a sidecar proxy (like Envoy) into every pod, adding a second container that intercepts and inspects traffic. This works, but at a real cost.
| Dimension | Sidecar-Based Monitoring | eBPF-Based Observability |
| Deployment model | One proxy container per pod | One kernel-level program per node |
| Memory overhead | 50–150 MB per pod | Shared across all pods on a node |
| CPU overhead | On every network packet, per pod | Under 1% per node |
| Code changes | None, but requires pod restarts | None, no restarts required |
| 500-pod cluster (cited example) | >75 GB RAM for sidecars | ~12 GB for the full eBPF stack |
This overhead difference compounds significantly as a kubernetes cluster scales, the larger the deployment, the more pronounced the resource savings from moving observability into the kernel rather than duplicating a proxy container per pod.
Master Modern Kubernetes Observability with eBPF Kubernetes
Understanding the advantages of eBPF Kubernetes observability is only the first step. The real value comes from learning how to deploy kernel-level networking, security monitoring, and application tracing using production-ready tools like Cilium, Hubble, and Tetragon. Build the practical skills to design scalable, high-performance Kubernetes observability stacks for real-world cloud environments. Explore Now
The 2026 eBPF Kubernetes Observability Stack
By 2026, eBPF Kubernetes observability has converged around a fairly consistent set of tools, each covering a distinct layer of visibility:
- Cilium + Hubble – Network Layer
Cilium is an eBPF-based CNI replacing iptables-based kube-proxy with L3/L4/L7 network policy and observability. Hubble is its built-in flow observability layer, recording every network flow and exposing them via CLI, gRPC, and a web UI.
- Tetragon – Security Layer
Built by Isovalent (the company behind Cilium), Tetragon provides kernel-level security observability and runtime enforcement, visibility into process execution, file access, and network syscalls, with the ability to block malicious behaviour, not just detect it.
- Pixie / Grafana Beyla – Application Layer
Pixie auto-traces HTTP, gRPC, and database protocol traffic using eBPF uprobes with zero code instrumentation. Beyla provides similar automatic OpenTelemetry span emission for any language runtime, both eliminate the need for SDK-based manual instrumentation.
Together, these three layers, Cilium/Hubble for network, Tetragon for security, Pixie/Beyla for application tracing, cover the full observability surface of a modern kubernetes deployment, all delivered through eBPF running at the kernel level rather than through sidecars or manual code instrumentation.
Cilium and Hubble: Network Observability
Cilium is the foundation of most modern eBPF Kubernetes deployments, it has become the default CNI at most major managed Kubernetes providers in 2026, precisely because it replaces the performance bottleneck of iptables-based networking with eBPF programs running directly in the kernel’s packet path.
- Cilium is fully compatible with the standard Kubernetes NetworkPolicy API, existing policies keep working, with CiliumNetworkPolicy available as an additive extension for L7 features
- Hubble, Cilium’s observability layer, records every network flow processed by eBPF and exposes service map visualisation, flow logs, and real-time traffic inspection
- For many use cases, Cilium’s built-in mTLS and L7 policy capabilities reduce or eliminate the need for a separate service mesh like Istio, though Istio still offers richer traffic management features (retries, circuit breaking) that Cilium doesn’t fully replicate
For anyone managing kubernetes architecture at scale, Cilium plus Hubble is typically the first eBPF component adopted, it addresses both a performance problem (iptables scaling) and an observability gap (network flow visibility) simultaneously.
Tetragon: Kernel-Level Security Observability
Tetragon extends eBPF Kubernetes observability into security. Because it runs inside the kernel, it has a more privileged vantage point than traditional user-space security tools (antivirus, host-based intrusion detection), it observes activity before an attacker who has compromised the OS could evade detection at the user-space level.
- Tetragon provides real-time visibility into process execution, sensitive file access, and abnormal network connections across a kubernetes cluster
- Unlike detection-only tools, Tetragon supports enforcement, it can actively block malicious behaviour, not just alert on it
- Tetragon policies are defined as Kubernetes custom resources, meaning teams manage them through the same GitOps tooling (like Argo CD) already used for standard kubernetes deployment workflows
- Production teams typically start policies in observe-only mode before promoting to blocking, to validate false-positive rates against real cluster traffic first
Tetragon reached production maturity in 2024 and by 2026 is deployed at multi-thousand-node scale by large organisations, a meaningful shift from being viewed as an interesting demo just a couple of years earlier.
Pixie and Beyla: Zero-Instrumentation Application Tracing
The final layer of the eBPF Kubernetes observability stack addresses application-level tracing, historically the domain of SDK-based APM tools requiring manual code instrumentation.
- Pixie uses eBPF uprobes to automatically trace HTTP/1.1, HTTP/2, and gRPC request/response data, along with database protocol traffic (PostgreSQL, MySQL, Redis, Kafka), all without any application code changes
- Pixie captures a rolling buffer of full request data per node for a short window, then aggregates statistics for longer-term storage, giving engineers full request-level debugging visibility without the storage cost of retaining everything indefinitely
- Grafana Beyla offers a similar zero-instrumentation approach, emitting standard OpenTelemetry spans for any language runtime, and has since been donated to the OpenTelemetry project, signalling a move toward vendor-neutral standardisation
This layer is often the most immediately compelling for teams currently relying on manual instrumentation, it removes the ongoing maintenance burden of keeping tracing SDKs updated across every service in a kubernetes cluster.
Getting Started: eBPF in a Real Kubernetes Cluster
For engineers looking to learn kubernetes observability practices with eBPF hands-on, a practical rollout typically follows this sequence:
- Verify kernel version: run uname -r on cluster nodes and confirm 6.1+ for full 2026 production feature support and CO-RE compatibility
- Install Cilium and Hubble on a staging cluster, and evaluate migration from an existing CNI (Flannel, Calico) if applicable
- Deploy Tetragon with basic TracingPolicies in observe-only mode, start with common patterns like sensitive file access or privilege escalation detection
- Add Pixie or Beyla as a DaemonSet, and run it in parallel with any existing SDK-based instrumentation before fully cutting over
- Route all collected telemetry through an OpenTelemetry Collector into existing backends (Grafana, Prometheus, Tempo) for unified visualisation
This staged approach, network, then security, then application tracing, lets a team validate each eBPF layer independently before depending on it for production kubernetes deployment visibility, rather than attempting a full stack cutover all at once.
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FAQs
Q1. What is eBPF Kubernetes observability?
Ebpf kubernetes observability uses kernel-level eBPF programs to monitor network, security, and application behaviour across a cluster without code changes or sidecar containers.
Q2. What is eBPF, in simple terms?
eBPF Kubernetes is a technology that runs small, sandboxed programs directly inside the Linux kernel, attached to events like network packets and system calls, without writing a kernel module.
Q3. How does eBPF fit into kubernetes architecture?
In modern kubernetes architecture, eBPF-based CNIs like Cilium replace iptables-based networking, becoming the networking layer itself while providing built-in observability.
Q4. Is eBPF better than sidecar-based monitoring for a kubernetes cluster?
For most use cases, yes, eBPF runs one program per node with under 1% CPU overhead, versus sidecar-based monitoring adding 50–150 MB memory overhead per pod across the kubernetes cluster.
Q5. What tools make up a typical eBPF observability stack for kubernetes deployment?
A typical eBPF stack for kubernetes deployment combines Cilium and Hubble for network visibility, Tetragon for security observability, and Pixie or Beyla for zero-instrumentation application tracing.
Q6. Where should I start if I want to learn kubernetes observability with eBPF?
To learn kubernetes observability with eBPF, start by installing Cilium and Hubble on a staging cluster, then add Tetragon in observe-only mode before introducing application-level tracing.
