eBPF Monitoring: How It Works and Where It Fits in 2026

eBPF Monitoring: How It Works and Where It Fits in 2026

eBPF monitoring uses small, verified programs that run inside the Linux kernel to capture network, system and application signals without changing application code. It gives SRE and platform teams deep visibility into hosts and Kubernetes nodes, but it produces raw signals, not answers. The value comes from what you do with those signals once they reach your telemetry stack.

TL;DR

• The problem: Tool sprawl and uninstrumented services leave blind spots, especially in network behavior and in services nobody has had time to instrument.
• The takeaway: eBPF collects kernel-level telemetry with no application code changes, but it complements language-level instrumentation instead of replacing it, and it is Linux-only.
• The requirements: Loading programs needs elevated privileges (privileged mode or CAP_BPF), so treat the collector like any other privileged workload.
• The next step: Pilot on one non-critical Kubernetes node pool, export to the metrics backend you already run, and decide what correlates those signals during an incident.

Table of Contents

• What eBPF Is, in Plain Terms
• How eBPF Monitoring Collects Telemetry
• What eBPF Observability Can and Cannot Show
• eBPF Monitoring vs. Traditional Agents and SDKs
• Practical Uses: Network, Performance and Security
• Requirements Before You Deploy
• Costs, Risks and Safe Rollout
• From Signals to Answers
• Opsphere for Teams Adding eBPF Telemetry
• FAQ
• Sources

What eBPF Is, in Plain Terms

eBPF is a Linux technology for running sandboxed, event-driven programs in a privileged context. You do not have to change kernel source or load kernel modules, according to the eBPF Foundation’s introduction. Think of it as a safe way to ask the kernel a question and get an answer every time a specific event happens.

Those events are called hooks. A program attaches to a hook, such as a system call, a network packet arriving or a function entry. Nothing runs continuously. The program executes only when the event fires, which is one reason the approach can be efficient.

The Linux kernel documentation notes that eBPF is designed for JIT compilation with one-to-one instruction mapping. That lets the code run almost as fast as natively compiled code.

The Life of an eBPF Program

The normal path, as IBM’s explainer describes it, has these steps:

1. Source code is compiled to eBPF bytecode.
2. A user-space loader hands the bytecode to the kernel through the BPF system call.
3. The verifier checks the program for safety.
4. The kernel interprets or JIT-compiles it.
5. The program runs when its attached event triggers, reading and writing maps.
6. The program is eventually unloaded.

Maps are the shared state. A kernel-side program can count events or store data in a map, and a user-space process can read the same map. Ring buffers serve the other direction of the flow, pushing events from kernel space up to user space.

Why the Verifier Matters

Running custom code in the kernel would be reckless without a gatekeeper. The eBPF documentation on the verifier explains the risk: unchecked programs executing as native code in kernel mode could corrupt memory, leak sensitive information, crash the kernel or hang it.

The eBPF Foundation says the verifier checks termination, bounded loops, initialized variables, memory bounds, program size, finite complexity and required privileges before a program attaches. Programs that pass run without expensive runtime checks. The rules do vary by program type, and verifier complexity limits apply, which is why some ambitious programs fail to load on older kernels.

How eBPF Monitoring Collects Telemetry

An eBPF program by itself only observes and aggregates. A monitoring pipeline needs a few more pieces: a kernel-side program, a transport to user space, a processor and an exporter.

Riptides published a clear walkthrough of this path. In their example, a kernel tracepoint event passes through an eBPF ring buffer to a Go user-space process. That process uses OpenTelemetry and exports a Prometheus metric (From Tracepoints to Prometheus). The same post explains that ring buffers give high-throughput, low-latency communication between kernel and user space, while OpenTelemetry converts raw events into standard telemetry such as Prometheus counters.

The pattern generalizes:

The in-kernel aggregation step is what keeps overhead manageable. Instead of shipping every packet to user space, a program can maintain counters and send only summaries.

Zero-Code Instrumentation with OpenTelemetry

The most practical entry point for many teams is OpenTelemetry’s eBPF Instrumentation, known as OBI. Its documentation says it captures trace spans, RED metrics, runtime metrics and application and network relationships from supported Linux workloads, without application-code or configuration changes.

The supported signals and protocols include HTTP/S, HTTP/2, gRPC, Kafka, NATS, MQTT, database protocols and DNS. On the network side, OBI lists TCP round-trip time, retransmissions, connection metrics, socket I/O and service-to-service relationships.

For a team with dozens of services in different languages, some never instrumented, that is a fast way to get request rate, error and duration data for everything on a node.

What eBPF Observability Can and Cannot Show

Because eBPF sits at the kernel boundary, it sees what crosses that boundary. That makes it strong in some areas and silent in others.

Where It Is Strong

• Service-to-service relationships. Connection data lets you draw who talks to whom without anyone annotating code.
• Network health. TCP RTT, retransmits and connection data expose latency and packet loss that application logs rarely mention.
• Uninstrumented workloads. Legacy services, third-party binaries and sidecar-free pods still produce RED metrics.
• Consistency. One collection mechanism behaves the same across languages on the same node.

Where It Does Not Reach

OpenTelemetry is direct about this: OBI does not replace language-level instrumentation for custom spans, application-specific attributes, business events or other in-process telemetry it cannot derive. If you need to know which customer tier a slow checkout belongs to, that context lives in your code, and your code has to emit it.

The practical split looks like this:

• Use eBPF for baseline coverage: RED metrics, network behavior and dependencies.
• Use language-level instrumentation for business logic, custom attributes and in-process detail.
• Use both, and make sure they land in a place where you can correlate them.

eBPF Monitoring vs. Traditional Agents and SDKs

Traditional approaches add a library to each service or run an agent that scrapes or tails data. eBPF changes where collection happens.

OpenTelemetry describes OBI as lightweight because it needs no code changes, libraries or restarts. It also states the limit plainly: support is Linux-oriented and depends on kernel capabilities and supported attachment mechanisms. If part of your fleet runs on other operating systems, plan a second collection method for it.

Practical Uses: Network, Performance and Security

Kubernetes Network Troubleshooting

Microsoft’s June 2023 explanation of its AKS network observability add-on is a good concrete example. It describes eBPF probes attached to Linux netfilter locations to identify dropped packets, collect packet metadata and drop reasons, and convert events into Prometheus metrics in user space.

Microsoft says this approach helps diagnose network-policy drops, receive-buffer saturation, packet loss and latency spikes. These are the problems that make an on-call engineer lose an hour: the application looks healthy, the service is timing out, and the cause sits one layer down.

Performance Troubleshooting

With zero-code RED metrics per service and TCP-level signals per connection, a slow request can be split into two questions. Is the service itself slow, or is the path to it degraded? Retransmissions and round-trip time point to the path. Duration histograms with healthy network metrics point to the service.

Security Visibility

Because eBPF programs can observe system calls and file and network activity at the kernel, the same technique is used for runtime security monitoring. If you go this route, keep the security collector and the performance collector as separate, clearly owned deployments, with their own review of what they can see.

Requirements Before You Deploy

EBPF monitoring is not a drop-in on every host. Check these first.

Kernel and Platform

Support is tied to Linux and to kernel capabilities. Inventory the kernel versions across your node pools, including managed Kubernetes images, before choosing a collector. Different program types and attachment mechanisms need different kernel features, and the verifier’s complexity limits can reject programs that load fine on newer kernels.

Permissions

The eBPF Foundation states that unless unprivileged eBPF is enabled, loading programs requires privileged mode or CAP_BPF. If unprivileged eBPF is enabled, unprivileged processes get reduced functionality and limited kernel access.

In Kubernetes, that usually means the collector runs as a privileged DaemonSet or with specific added capabilities. Security reviewers will ask about this, so have the answers ready:

• Which image runs the collector, and who maintains it?
• Which capabilities does it request, and is that the minimum?
• Which namespaces and nodes does it run on?
• Where does the data go, and does it include payloads or only metadata?

Costs, Risks and Safe Rollout

The verifier exists because kernel-mode code is powerful. Verified programs are designed not to crash or hang the kernel, and the checks happen before attachment. That is a strong safety property, but it does not remove operational responsibility.

What to Watch

• Collector overhead. Kernel-side work is small per event, but user-space processing, enrichment and export still use CPU and memory. Measure them on your own workloads.
• Cardinality. Per-connection or per-endpoint labels can multiply metric series in your backend. Aggregate before export.
• Data sensitivity. Header and protocol-level visibility can include sensitive fields. Decide up front what is retained.
• Version drift. Kernel upgrades can change which programs load. Include collector health in your node upgrade checklist.

A Rollout Plan That Holds Up

1. Pick one node pool. Choose a non-critical pool with a mix of services and a kernel you have inventoried.
2. Define success first. For example: RED metrics available for every service on the pool, and a measured collector footprint within a budget your team agrees on.
3. Export to your existing backend. Keep the standard formats so dashboards and alerts do not need rebuilding.
4. Set cardinality limits. Drop or aggregate high-cardinality labels at the collector.
5. Run it through one real incident or game day. Check whether the signals shortened the path to a cause.
6. Expand by pool. Add the next pool only after the first has been stable through a kernel or collector upgrade.

From Signals to Answers

Collecting eBPF telemetry solves one problem and exposes the next. You now have kernel-level network metrics, zero-code RED metrics, application traces, Kubernetes events, cloud resource changes, deploy history and alerts, all in different tools. During an incident, the slow part is rarely missing data. It is joining it.

A useful working model is to treat eBPF data as one evidence source among several. A latency spike on a service becomes meaningful when you can line it up with a deploy, a node event, a config change and a downstream dependency, in one place, in minutes. Teams already running Prometheus will recognize this: the metrics are there, and the hard part is context. Our guide on Prometheus monitoring and where teams hit limits covers that gap in detail.

That joining step is what Opsphere is built for. It is an AI-powered operational intelligence platform that unifies context across AWS, Kubernetes, observability, CI/CD, security and engineering tools. It connects to the tools you already run instead of asking you to replace them, and it works through read-only operational tools. You can see how the Opsphere platform brings these sources into a single investigation interface, and how its MCP operational gateway exposes that context to AI agents and IDE-based workflows. The strict read-only posture is documented on the security page.

Opsphere for Teams Adding eBPF Telemetry

If your eBPF pilot is producing signals faster than your team can interpret them, the next logical step is a layer that correlates them with everything else. Opsphere is designed for exactly that: structured, evidence-backed investigation across your existing stack, with parallel hypothesis testing and context memory, while each tool stays the source of truth.

Plans start with a Community tier at €0 per month and a Developer plan at €19 per month, with Team and Enterprise tiers for larger organizations. Compare them on the pricing page.

START WITH THE COMMUNITY PLAN and connect your first data source, or review the plans to find the right tier for your team.

FAQ

What is eBPF monitoring?

eBPF monitoring collects telemetry using small programs that run in the Linux kernel when specific events occur, such as a network packet or system call. The programs are verified for safety before they attach, and the results are sent to user space and exported as metrics, traces or logs. It requires no changes to application code.

Does eBPF replace application instrumentation?

No. OpenTelemetry states that its eBPF instrumentation does not replace language-level instrumentation for custom spans, application-specific attributes or business events. The practical approach is to use eBPF for broad baseline coverage and in-code instrumentation where you need business context.

Does eBPF monitoring need root access?

It needs elevated privileges. Per the eBPF Foundation, unless unprivileged eBPF is enabled, loading programs requires privileged mode or CAP_BPF. In Kubernetes, that typically means running the collector as a privileged workload, so review its capabilities with your security team before rollout.

Does eBPF work outside Linux?

The OpenTelemetry eBPF Instrumentation is Linux-oriented, and what it can capture depends on kernel capabilities and supported attachment mechanisms. If you run other operating systems, plan a different collection method for those hosts.

Is eBPF observability the same as eBPF monitoring?

The terms overlap heavily. Monitoring usually means collecting known signals and alerting on them. Observability is the broader goal of asking new questions about system behavior from the data. eBPF supplies kernel-level data for both.

Sources

• What is eBPF? An Introduction and Deep Dive (eBPF Foundation)
• Classic BPF vs eBPF (Linux kernel documentation)
• Verifier (eBPF Docs)
• What Is eBPF? (IBM)
• OpenTelemetry eBPF Instrumentation
• From Tracepoints to Prometheus: The journey of a kernel event to observability (Riptides)
• Enhance Kubernetes Troubleshooting with Network Observability add-on in AKS (Microsoft Azure)

Recommended

• Prometheus Monitoring: How It Works and Where Teams Hit Limits
• MCP Operational Gateway for DevOps & SRE

This article is provided for general informational purposes only and does not constitute professional, legal, security, or compliance advice. Please evaluate recommendations against your organization’s specific environment and requirements.