Some links on this page are affiliate links: if you buy through them we may earn a commission, at no extra cost to you.
The eBPF Foundation’s February 2026 eBPF In Production report documents real deployments across networking, observability, and security. Its case studies make a strong case that eBPF is production-capable; they do not prove universal performance gains or a guaranteed return on investment. For platform and infrastructure teams, the report is best read as a map of practical use cases and reported outcomes—not as an independent adoption survey or apples-to-apples benchmark.
The report at a glance
Announced by the eBPF Foundation on February 12, 2026, eBPF In Production: An Overview of Compelling Enterprise Outcomes Using eBPF is a free, 20-page report by technology journalist Bill Doerrfeld. It is aimed at executives and senior technical leaders. Its featured case studies focus on Cloudflare, Netflix, ByteDance, and Rakuten Mobile, alongside a wider set of public examples.
The report groups deployments into high-performance networking, deep observability and profiling, runtime security, and application governance or FinOps. The first three have the clearest established production patterns in the report; governance and FinOps are presented as newer areas with room to grow.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesIt is a curated collection of case studies and public benchmarks. It does not present a statistically representative survey of adoption, a common methodology for comparing results, or a controlled test against alternatives such as iptables, sidecars, kernel modules, or conventional agents. Its numbers are useful evidence that particular systems achieved particular outcomes, not forecasts for a new deployment.
#1 Best Overall
What eBPF changes in a production system
eBPF lets a system load programs at selected points in the Linux kernel, where they can observe or act on events such as network packets, process activity, system calls, and resource behavior. The kernel verifier checks programs against safety constraints before they run. That gives teams a way to add kernel-level visibility, filtering, or policy without maintaining a custom kernel fork for each capability.
The practical appeal is the combination of proximity and programmability: a program can filter or process events close to their source, sometimes avoiding unnecessary work or data export; network and security logic can operate in the data path; and instrumentation can work across applications written in different languages. That is not the same as “no agents.” Production deployments typically still need user-space components for configuration, metadata, policy distribution, export, storage, alerting, and upgrades.
A useful mental model is a two-part system:
- Data plane: eBPF programs observe or enforce behavior at kernel hooks.
- Control and operations plane: user-space agents and services load programs, manage policy, correlate metadata, export data, and provide queries or alerts.
The value depends on the whole system. In-kernel filtering can reduce irrelevant events, but exported data still has costs: network egress, storage, query load, cardinality, retention, and possibly observability or SIEM licensing. The right measure is the cost per useful signal, not just the CPU use of a sensor.
What the featured case studies show
Cloudflare: a shared infrastructure capability
The report presents Cloudflare’s eBPF use across networking, performance analysis, kernel telemetry, troubleshooting, and DDoS defense. That breadth is instructive: eBPF need not be a single-purpose monitoring feature. It can be a substrate used by several infrastructure capabilities.
The report cites Cloudflare’s role in blocking a 3.7-terabyte DDoS attack in 45 seconds. Treat that as a case-specific result, not evidence that eBPF alone blocked the attack or that another organization can reproduce it. Mitigation depends on traffic architecture, hardware, upstream capacity, XDP mode, filtering logic, and incident response as well as the program running in the kernel.
Netflix: flow visibility at service scale
Netflix’s example centers on eBPF flow logs and network insight, including visibility useful for defense, investigating noisy neighbors, and diagnosing distributed-system behavior. The report points readers to Netflix’s technical discussion of flow logs at scale. The transferable lesson is that kernel-level flow data can help answer infrastructure questions across services; the report does not attach a single comparable benchmark to this case.
Rank #3
ByteDance: networking at very large scale
The Foundation’s announcement says the report describes an eBPF networking deployment across approximately one million servers and a 10% throughput improvement. Those are attributed case-study claims, not general eBPF performance guarantees. The result belongs to ByteDance’s particular workload, topology, hardware, and implementation.
Rakuten Mobile: telecom and cloud-native infrastructure
The report uses Rakuten Mobile to illustrate eBPF’s relevance to telecom infrastructure, including anomaly detection, security enforcement, observability, and high-performance network functions. These environments have distinct dataplane designs, latency objectives, and operational constraints. Their results should not be assumed to apply directly to an ordinary Kubernetes cluster.
Reported outcomes—and how to interpret them
The report cites the following results from named organizations and projects. The figures are not measured on a common workload or baseline, so they should not be ranked as if they were a head-to-head comparison.
| Organization or project | Reported outcome | Interpretation |
|---|---|---|
| Datadog | 35% lower CPU usage through an eBPF-based connection tracker. | A result for that tracker and deployment; not a general CPU reduction for eBPF. |
| Meta Strobelight | Up to 20% fewer CPU cycles. | “Up to” is not an expected average; workload and measurement details matter. |
| Polar Signals | 50% reduction in cross-zone traffic-related operating costs. | Cost depends on topology, traffic patterns, and the approach used to identify and address traffic. |
| Upwind | Average sensor CPU usage below 1%, with many nodes below 0.1%. | Sensor CPU is not the same as the total cost of collecting and using security data. |
| LinkedIn Skyfall | 70% reduction in Kafka log volume. | Reduced volume can lower pipeline burden, but data selection and coverage also matter. |
| SuperNetFlow | Threefold reduction in server footprint. | A system-level result; the report’s figure is not a universal hardware-saving ratio. |
| free5GC | 40% reduction in highest round-trip time using eBPF-based scheduling. | A specialized telecom/networking result, not a general latency promise. |
| Seznam.cz | Doubled throughput while reducing CPU usage by 72× in an eBPF load-balancing deployment. | A striking, deployment-specific comparison; do not infer the same baseline or gain elsewhere. |
| DoorDash | 40% less memory use, 98% fewer restarts, 80% faster deployments, and about 0.3% node utilization after migrating to eBPF-based monitoring. | These describe a migration and its system context, not an isolated measurement of the eBPF program. |
The Foundation’s full report provides the source trail for these examples. A percentage only becomes decision-useful when the original case defines its baseline, workload, hardware, measurement window, and what changed besides eBPF. Reduced CPU, log volume, or latency may reflect filtering, sampling, topology, algorithms, hardware, or workload mix as well as the eBPF component.
Where eBPF is most useful today
- Kubernetes networking and network policy: A mature area for service networking, policy enforcement, load balancing, and visibility, particularly for teams already operating a Linux-based Kubernetes dataplane.
- Network flow visibility: Useful when teams need host- or workload-level network insight and traditional collection is too costly, incomplete, or difficult to correlate.
- Tracing and profiling: Kernel-level signals can help investigate performance across language boundaries. They complement, rather than automatically replace, application instrumentation.
- Runtime security: Process and syscall activity can support detection and policy enforcement close to the workload. Privileged access and the policy lifecycle require careful governance.
- Specialized load balancing and DDoS mitigation: Potentially valuable at high packet rates or scale, but highly dependent on hardware, drivers, architecture, and operational expertise.
- API governance and FinOps: Promising newer uses, such as discovering traffic and attributing resource use. The report treats these as emerging, not as equally established outcomes.
GPU and specialized-resource profiling, telecom dataplane modernization, and monitoring for newer workload types are also part of the broader landscape. Their maturity and portability vary by platform and use case.
What the report does not prove
- It does not establish market-wide adoption. A set of public case studies cannot show how representative these deployments are.
- It does not provide a universal performance comparison. There is no common benchmark here proving eBPF is always faster or cheaper than iptables, sidecars, agents, or another approach.
- It does not guarantee low overhead. Cost depends on hook location, event rate, program complexity, map access, traffic, export, and enabled probes. More visibility can also mean more data to retain and query.
- It does not replace all application observability. Kernel signals can expose processes, calls, flows, scheduling, and resource behavior, but often cannot explain business transactions, domain errors, user intent, or application state. Correlate them with application traces and logs, Kubernetes metadata, cloud events, and service ownership.
- It is not a total-cost-of-ownership analysis. Engineering time, support, infrastructure, integration, upgrades, incident response, and downstream data costs all affect the economics.
Production-readiness checklist
Before a pilot
- Inventory Linux distributions and kernel versions, and verify BTF, required helpers, program types, kernel configuration, and vendor backports. CO-RE and BTF help portability; they do not make every program compatible with every kernel.
- Check Kubernetes and managed-provider constraints, including cgroups, namespaces, networking integration, drivers, and whether privileged host access or special capabilities are allowed.
- Decide whether the deployment is a DaemonSet, host agent, CNI or platform integration, or another model. Establish what it can change, whether a reboot is needed, and how upgrades and rollback work.
- Record baseline CPU, memory, tail latency, packet loss, event volume, node restarts, and relevant application SLOs on representative nodes.
- Define data location, retention, export, access, and cost limits before enabling broad collection.
- Review who may load or update programs, how artifacts and policies are approved, how changes are audited, and how to disable enforcement in an emergency.
During rollout
- Start with a canary node pool and visibility-only mode where possible. Roll out enforcement separately from collection.
- Enable only the event types and probes needed for the stated problem; use filtering or sampling deliberately.
- Set CPU and memory budgets and watch map pressure, event volume, verifier or program-load failures, drops, and application SLOs.
- Test under node pressure, network partitions, control-plane outages, upgrades, and rollback conditions—not only in a quiet development cluster.
- Agree on ownership across networking, security, and observability teams: change windows, on-call response, retention, and incident responsibility.
If something goes wrong
Use the chosen project or vendor’s version-specific recovery instructions; there is no universally safe detach or rollback command. Where the design permits, disable enforcement before removing collection. Preserve kernel and agent logs, verifier output, and affected-node details. Revert the relevant DaemonSet, Helm release, host package, or platform change using its documented procedure, then validate policy and service reachability. Determine whether the fault lies in the eBPF program, user-space agent, exporter, or storage backend before draining or replacing nodes.
The verifier reduces certain classes of unsafe program behavior; it does not guarantee that an entire privileged agent, supply chain, policy, or control plane is trustworthy. A bad or misconfigured deployment can still have operational consequences, including impaired networking. Review the blast radius and emergency disablement path before enabling enforcement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build, adopt an open-source project, or buy support?
First distinguish three levels of adoption: consuming a product that embeds eBPF, operating an eBPF-based platform, and writing and maintaining custom programs. Most organizations using eBPF do not need to become eBPF developers.
| Option | Good fit | Less suitable when |
|---|---|---|
| Cilium | Kubernetes networking, network policy, service networking, load balancing, Hubble flow visibility, or replacing/supplementing kube-proxy. | You only need profiling or tracing, or cannot operate a privileged dataplane or accommodate CNI constraints. |
| Tetragon | Linux and Kubernetes runtime security, process and syscall visibility, and policy enforcement. | You need a complete CNAPP, vulnerability-management suite, or broad cloud-posture platform. |
| Falco | Rules-based runtime threat detection and host activity monitoring, especially for teams already familiar with its ecosystem. | Your principal need is high-performance networking or a full network-observability platform. |
| bpftrace, libbpf, cilium/ebpf, or Aya | Custom diagnostics, internal tooling, research, and specialized programs in C, Go, or Rust. | You lack expertise to test, troubleshoot, and maintain kernel-facing software or need a turnkey support model. |
Open-source software may avoid license fees, but it is not cost-free to operate. Include engineering and on-call time, testing, upgrades, integration, and support in the comparison. Commercial products can bundle program management, policy, data correlation, support, and user interfaces, but introduce vendor, deployment, and pricing trade-offs. Select by the operational problem—networking, observability, or security—rather than by the presence of eBPF in a feature list.
Recommended Free Tools
Commercial options and pricing signals
These figures are point-in-time signals reported as observed on August 18, 2026, not quotes or guarantees. Packaging, eligibility, usage definitions, retention, support, and cloud costs affect the final bill; verify current terms directly with the vendor.
| Product | Published pricing signal | Most relevant when |
|---|---|---|
| Isovalent Enterprise Platform | No public list price on the product page; sales-led. The Microsoft Marketplace listing describes private offers and custom pricing. | You need supported enterprise Cilium networking, policy, multi-cluster connectivity, or associated visibility across Kubernetes and broader infrastructure. |
| Datadog | Pricing page signals cited in the dossier: Workload Protection from $15 per host/month billed annually or $18 on demand; Universal Service Monitoring from $9 per host/month; APM including USM from $31 per host/month. Additional containers may be charged separately under the listed Workload Protection model. | You already use Datadog and want eBPF-derived data correlated with its broader observability or security platform. Review host, container, and data-volume implications. |
| groundcover | Pricing page signals: Free at $0 with 12-hour retention and community support; Pro at $30 per host/month; Enterprise at $35 per host/month. Its BYOC model means customer cloud infrastructure costs are additional. | Kubernetes teams prioritizing host-based pricing and keeping the backend in their own cloud. Weigh self-hosting against the subscription. |
| Sysdig Secure | Request-a-quote pricing; licensing is based on hosts for relevant workloads, with separate event-based treatment for some cloud logs. | You need runtime security as part of a wider cloud-native application protection platform rather than only network visibility. |
Prices in different models are not directly comparable: host-based rates, workload definitions, retention, cloud hosting, support, and usage charges can change total cost materially. If eBPF is meant to reduce observability spend, compare the full cost of useful data collected, stored, and queried—not just the sensor price or CPU overhead.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
