eBPF Observability for SRE: The End of Sidecars in 2026

Introduction

The SRE toolbox is going through its biggest transformation in a decade. For years, if you wanted deep observability into your Kubernetes workloads, you deployed sidecars, DaemonSets, and host-level agents — each one consuming CPU, memory, and your team's patience. In 2026, that model is fading fast.

eBPF (extended Berkeley Packet Filter) has matured from a niche kernel technology into the backbone of modern observability. It lets you inspect every system call, every network packet, and every application request — without touching a single line of application code and without deploying a single sidecar container.

This article is a practical guide for SREs and platform engineers who want to understand what eBPF means for their observability stack, which tools to use, how to deploy them, and where the traditional sidecar model still holds ground.

What Is eBPF and Why It Matters for SRE

eBPF is a Linux kernel technology that allows programs to run in a sandboxed environment inside the kernel itself. These programs hook into kernel events — system calls, network events, tracepoints, and function calls — and can safely inspect, filter, and transform data without ever leaving kernel space.

For SREs, the implications are enormous: zero-instrumentation observability. You no longer need to add OpenTelemetry SDKs to your Go services, inject Envoy sidecars into every pod, or run a DaemonSet of node exporters. eBPF programs attach to the kernel at runtime and observe everything that happens on a node — network flows, filesystem access, process execution, and HTTP/gRPC request details — all without the application knowing anything is different.

The eBPF verifier guarantees that programs cannot crash the kernel or cause infinite loops, which means it is safe to run in production. And because eBPF programs run in kernel context, they are dramatically faster than userspace alternatives.

The key shift for SREs is philosophical: observability moves from opt-in to always-on. You do not instrument individual services — you instrument the kernel once and get visibility into everything running on that node.

eBPF vs Traditional Monitoring: Agents, Sidecars, and DaemonSets

To appreciate why eBPF is such a leap forward, it helps to understand the operational overhead of the traditional model.

The Traditional Observability Stack

A typical Kubernetes cluster running the classic observability triad might look like this:

• Metrics: Prometheus node_exporter as a DaemonSet on every node, plus application-level metrics exported via sidecars or /metrics endpoints.
• Logging: Fluentd or Filebeat DaemonSet scraping container logs from /var/log/containers on every node.
• Tracing: OpenTelemetry Collector as a sidecar injected into every pod, forwarding spans to Jaeger or Tempo.
• Service Mesh: Istio or Linkerd sidecars for mutual TLS, request metrics, and traffic routing.

Each of these components consumes resources. A typical Istio sidecar consumes 50-100 MiB of memory per pod. Multiply that by hundreds of pods, and you are looking at gigabytes of memory spent on infrastructure, not business logic. Then add the CPU overhead, the configuration complexity, and the operational burden of upgrading sidecar versions across every workload.

The eBPF Model

With eBPF-based observability, you run a single agent per node — no sidecars, no application modifications, no DaemonSets for logging/metrics/tracing separately. One eBPF program running in the kernel can:

• Capture all network flows with process-level attribution
• Trace HTTP and gRPC request latency, status codes, and payload sizes
• Monitor filesystem and process events for security
• Generate Prometheus-compatible metrics without scraping endpoints

The operational model shifts from "manage N observability components across M pods" to "manage one eBPF agent per node."

Here is a comparison table to make the differences concrete:

The trade-off is clear: eBPF gives you better observability at a fraction of the operational cost — provided your kernel is modern enough.

The eBPF Tooling Landscape in 2026

The eBPF ecosystem has consolidated around a handful of production-ready tools. Here is what SRE teams are adopting:

Cilium + Hubble

Cilium is the leading CNI (Container Network Interface) built on eBPF. It replaces kube-proxy with eBPF-based load balancing, network policy enforcement, and service mesh capabilities. Hubble is Cilium's observability layer — it provides real-time network flow visibility with service-level, pod-level, and even process-level granularity.

Hubble gives you a live service dependency map, latency histograms per service-to-service communication, and the ability to trace individual network packets through your cluster. For SREs debugging "why is service A slow talking to service B," Hubble is a game-changer.

Pixie (New Relic)

Pixie uses eBPF to automatically capture application-level metrics, traces, and events without any code changes. It provides pre-built dashboards for HTTP throughput, error rates, and latency — all captured by kernel probes that intercept read() and write() system calls.

Pixie's PxL scripting language lets you write custom queries against live eBPF data. For example, you can write a script that shows all MySQL queries with latency above 100ms across your cluster in real time.

Tetragon (Isovalent/Cilium)

Tetragon is a security-focused eBPF tool that monitors process execution, filesystem access, network activity, and privilege escalation in real time. For SREs concerned with runtime security, Tetragon can detect when a container unexpectedly executes a shell, reads /etc/shadow, or binds a privileged port.

Falco (Sysdig)

Falco was one of the first eBPF-based security tools and remains widely deployed. It uses a rules engine to detect anomalous behavior — for example, a container spawning a shell inside a production workload, or unexpected outbound network connections.

Grafana Beyla

Grafana Beyla is a newer entrant specifically designed for application-level observability. It auto-instruments HTTP and gRPC services running on a node and exports RED metrics (Rate, Errors, Duration) directly to Prometheus or Grafana Cloud. Beyla is particularly useful for brownfield applications where you cannot or do not want to add OpenTelemetry instrumentation.

Hands-On: Deploy Cilium + Hubble on Kubernetes

Let us walk through deploying Cilium with Hubble on a Kubernetes cluster. This setup replaces your existing CNI and kube-proxy with eBPF-based equivalents and gives you immediate network observability.

Prerequisites

• Kubernetes 1.24+ cluster (managed or self-hosted)
• Linux kernel 5.10+ on all nodes
• Nodes must have eBPF support enabled (most cloud provider AMIs do)
• cilium CLI installed locally

Step 1: Install the Cilium CLI

CILIUM_CLI_VERSION=$(curl -s https://raw.githubusercontent.com/cilium/cilium-cli/main/stable.txt)
CLI_ARCH=amd64
if [ "$(uname -m)" = "aarch64" ]; then CLI_ARCH=arm64; fi
curl -L --fail --remote-name-all https://github.com/cilium/cilium-cli/releases/download/${CILIUM_CLI_VERSION}/cilium-linux-${CLI_ARCH}.tar.gz{,.sha256sum}
sha256sum --check cilium-linux-${CLI_ARCH}.tar.gz.sha256sum
sudo tar xzvfC cilium-linux-${CLI_ARCH}.tar.gz /usr/local/bin
rm cilium-linux-${CLI_ARCH}.tar.gz{,.sha256sum}

Step 2: Install Cilium with Hubble Enabled

cilium install \
  --version 1.16.0 \
  --set hubble.relay.enabled=true \
  --set hubble.ui.enabled=true \
  --set kubeProxyReplacement=true

The kubeProxyReplacement=true flag tells Cilium to replace kube-proxy entirely with eBPF — this eliminates iptables-based routing and dramatically reduces latency.

Step 3: Validate the Installation

cilium status --wait

You should see output confirming that Cilium is running with Hubble enabled and that kube-proxy replacement is active.

Step 4: Access the Hubble UI

cilium hubble ui

This opens the Hubble UI in your browser. You will see a service map showing all communication flows between pods, services, and external endpoints. Click on any edge to see latency, throughput, and HTTP status codes for that specific service-to-service path.

Step 5: Inspect Flows via CLI

# Watch all flows in real time
cilium hubble observe

# Filter for HTTP traffic to a specific pod
cilium hubble observe --from-pod default/backend-7d4f8c9b-xyz --protocol http

# Show flows with verdict (allowed/denied by network policy)
cilium hubble observe --verdict DROPPED

Step 6: Export Hubble Metrics to Prometheus

Hubble can export Prometheus-compatible metrics including HTTP request rates, TCP handshake latency, and dropped packets. Enable them in your Cilium Helm values:

hubble:
  metrics:
    enabled:
      - dns
      - drop
      - tcp
      - flow
      - http
      - icmp
      - port-distribution

Once enabled, point your Prometheus scrape config at the Hubble metrics endpoint on port 9965 and you will get real-time service-level metrics from every pod on every node — without a single sidecar.

eBPF for Application Tracing: HTTP and gRPC Latency Without Code Changes

One of the most powerful eBPF use cases for SREs is application-level request tracing without touching application code. Traditional distributed tracing requires you to add OpenTelemetry SDKs to your code, configure context propagation, and sample traces — a multi-sprint effort across every service team.

With eBPF, kernel probes intercept the read() and write() system calls that carry HTTP and gRPC traffic. By parsing the application-layer protocol inside the kernel, eBPF tools can extract:

• Request URL and method (GET, POST, etc.)
• Response status code
• Request and response payload sizes
• Latency from first byte sent to last byte received
• Source and destination process IDs

Pixie and Grafana Beyla both do this automatically. Here is what a Pixie PxL script looks like for finding slow HTTP responses:

# px/slow_requests.pxl
import px

df = px.DataFrame(table='http_events', start_time='-5m')
df = df[df['resp_latency_ns'] > 100_000_000]  # filter to >100ms
df = df[['time_', 'remote_addr', 'req_path', 'resp_status', 'resp_latency_ns']]
px.display(df)

This script runs entirely against eBPF-captured data — no application instrumentation, no sidecars, no code changes. In a production incident, you can write and run a PxL script in under a minute to pinpoint which endpoint is slow and for which clients.

For gRPC services, eBPF probes on HTTP/2 frames give you the same visibility: service name, method name, status codes, and streaming durations. The fact that gRPC runs over HTTP/2 means the same eBPF HTTP parser works without modification.

eBPF for Security Observability: Runtime Threat Detection

SREs increasingly own security observability — detecting threats at runtime in production environments. eBPF is uniquely suited for this because it can observe every kernel-level event that a malicious actor would need to perform.

What eBPF Security Tools Can Detect

Tetragon, Falco, and similar tools hook into kernel tracepoints to detect:

• Unexpected process execution: A container running a shell or a binary that is not part of the image
• Privilege escalation: A process calling setuid(0) or opening capabilities it should not have
• Sensitive file access: A container reading /etc/shadow, /proc/1/environ, or mounted secrets
• Network anomalies: A web server making outbound SSH connections
• Kernel module loading: Any attempt to load a kernel module from a container

Here is a Falco rule that detects a shell spawned inside a container:

# falco_shell_in_container.yaml
- rule: Shell in Container
  desc: Detect a shell running inside a container
  condition: >
    spawned_process
    and container
    and proc.name in (bash, sh, zsh, dash)
    and not proc.args contains "kubectl exec"
  output: >
    Shell spawned in container (user=%user.name
    container_id=%container.id image=%container.image.repository
    shell=%proc.name parent=%proc.pname cmdline=%proc.cmdline)
  priority: WARNING
  tags: [container, shell, runtime]

What makes eBPF security observability different from traditional host-based intrusion detection is that it operates at the kernel level and cannot be bypassed by userspace tricks. If a process opens a file, the kernel sees it — and the eBPF probe on the openat syscall captures it.

For SREs, these tools provide a safety net: even if your containers have no security agents installed, the node-level eBPF agent catches malicious activity across every workload on that node.

Performance Overhead Myths

A common objection to eBPF is fear of performance overhead. The concern is understandable — running code inside the kernel sounds expensive. But the reality, verified by production benchmarks from Cilium, Meta, and cloud providers, tells a different story.

eBPF programs are constrained by the kernel verifier: they must complete within a bounded number of instructions, they cannot loop indefinitely, and they cannot allocate memory dynamically. The result is that well-written eBPF programs add negligible overhead — typically under 1% CPU on modern kernels.

Key performance characteristics:

• eBPF network processing (Cilium): Cilium's eBPF-based service load balancing is faster than kube-proxy's iptables implementation, especially at scale. Benchmarks show 3-5x lower latency at the 99th percentile for large clusters.
• eBPF observability agents: Pixie reports less than 5% CPU overhead per node under normal workloads. Falco's overhead is typically under 2%.
• eBPF tracing: Linux kernel ftrace and perf-based tracing have higher overhead than eBPF-based alternatives because userspace tracers pay the cost of context-switching every event. eBPF traces stay in kernel space.

The real performance win, however, is the elimination of sidecars. A 200-pod cluster running Istio sidecars at 100 MiB each consumes 20 GiB of RAM just for the mesh. Replacing those sidecars with a single eBPF-based service mesh (Cilium Service Mesh) frees up that memory for actual workloads. The net performance impact is overwhelmingly positive.

That said, you should benchmark your specific workload. Run your application under load, enable the eBPF agent, and compare metrics. In virtually all cases, the overhead will be too small to measure without specialized tooling.

Migration Path: Sidecar to eBPF with Cilium Service Mesh

If you are running an Istio or Linkerd service mesh today, the migration to eBPF is not an overnight cutover. It is a phased transition that can proceed incrementally.

Phase 1: Deploy Cilium as CNI (Replace kube-proxy)

Install Cilium as your CNI while keeping your existing service mesh. Cilium can coexist with Istio — you get immediate benefits from eBPF-based networking and Hubble observability without touching your sidecar configuration.

Phase 2: Enable Cilium Service Mesh Features

Cilium supports mutual TLS via eBPF (no Envoy process needed), L7 traffic management with HTTP/GRPC-aware routing, and canary deployments. Enable these features incrementally, service by service:

# Enable L7 policy enforcement on a namespace
kubectl annotate namespace my-app cilium.io/l7-proxy=enabled

Phase 3: Remove Sidecars Service by Service

For each service, test the Cilium-managed mTLS and traffic routing. If your service only needs mTLS + basic traffic splitting, you can remove the Istio sidecar entirely. The service mesh logic runs in eBPF inside the kernel.

Phase 4: Sunset the Old Mesh

Once all services are migrated, uninstall Istio/Linkerd. Your cluster now runs a fully eBPF-based networking and observability stack.

The key enabler for this migration is that Cilium Service Mesh implements the Gateway API, the same Kubernetes standard that Istio is adopting. Your routing rules and traffic policies are portable.

When NOT to Use eBPF

eBPF is not a silver bullet. There are scenarios where traditional sidecars or agents are still the right choice:

Old Kernels

If your production nodes run Linux kernels older than 4.18, eBPF is not available. Even kernels between 4.18 and 5.10 lack many eBPF features (BTF, CO-RE, ring buffers) that modern tools depend on. If you are stuck on an older distribution like CentOS 7, eBPF is off the table.

Complex L7 Routing

Cilium's L7 capabilities cover HTTP/1.1, HTTP/2, gRPC, and Kafka. If you need Envoy's full feature set — custom WASM filters, complex rate limiting, external authorization with OPA — the sidecar model still offers more flexibility.

Windows Nodes

eBPF is a Linux kernel feature. If you run a mixed Windows/Linux Kubernetes cluster (common in enterprise environments), Windows nodes will still need traditional monitoring agents and sidecars.

Multi-Tenant SaaS with Extreme Isolation Requirements

Some regulated environments require that observability data from different tenants never touches the same kernel context. In these cases, per-pod sidecars provide stronger isolation guarantees than node-level eBPF agents.

Immature Kernel on Cloud Provider VMs

Some cloud providers lag behind on kernel versions. Always check the kernel version of your node images before committing to an eBPF stack. As of 2026, AWS EKS optimized AMIs and GKE Container-Optimized OS both ship 5.15+ kernels, which work well.

Conclusion

eBPF is not just a technology — it is a paradigm shift in how SREs approach observability. The ability to observe every system call, network flow, and application request without modifying code or deploying sidecars changes the economics of running reliable systems.

The sidecar model served us well for a decade, but it comes with mounting operational costs: memory overhead, upgrade complexity, and startup latency. In 2026, eBPF-based tools — Cilium Hubble for network observability, Pixie and Beyla for application tracing, Tetragon and Falco for security — offer a cleaner, faster, and cheaper path to the same observability goals.

The practical next step is simple: deploy Cilium with Hubble on one of your non-production clusters. Spend an afternoon exploring the service map and flow logs. You will wonder why you ever tolerated the complexity of sidecars.

For deeper reading on related topics, check out our guide on Kubernetes security best practices in 2026, our walkthrough on AI-powered observability for SRE monitoring, and our comprehensive OpenTelemetry tutorial and setup guide.