10 Best eBPF Monitoring Tools in 2026: Full-Stack Observability Without Manual Instrumentation

eBPF monitoring has shifted from kernel hacker territory to production observability infrastructure. The reason: eBPF programs run in the Linux kernel, capturing system calls, network packets, and application behavior without requiring code changes or heavyweight agents. This makes eBPF particularly valuable for Kubernetes environments, where instrumenting every service manually is impractical and sampling at the edge loses the exact traces you need during an incident.

According to the CNCF Annual Survey 2024, 47% of organizations now use eBPF-based tools in production, up from 31% in 2023. That adoption is driven by teams replacing traditional APM agents with eBPF instrumentation that captures complete telemetry at the kernel level, then exports it to backends via OpenTelemetry or Prometheus.

This guide compares 10 eBPF monitoring tools across full-stack observability platforms, auto-instrumentation engines, and continuous profiling tools. Each is assessed on deployment model, signal depth, backend requirements, and total cost of ownership. If you are evaluating whether eBPF fits your stack, the decision framework at the end maps tool selection to team size, Kubernetes maturity, and whether you need a complete backend or just the instrumentation layer.

Quick Comparison: 10 eBPF Monitoring Tools at a Glance

Tool	Best For	Pricing Model	Includes Backend?	Kubernetes-Native?
CubeAPM	Teams that want full-stack observability with eBPF auto-instrumentation inside their own cloud	$0.15/GB ingestion, unlimited retention	✓ Yes, self-hosted	✓ Native
Metoro	Kubernetes teams that need logs, traces, metrics, profiling, and AI-driven root cause analysis in one platform	From $20/node/month with 100GB included	✓ Yes, SaaS or BYOC	✓ Native
Coroot	Open-source teams that want self-hosted eBPF observability with service maps, profiling, and SLOs	From $1/CPU core/month	✓ Yes, self-hosted	✓ Native
Pixie	Fast in-cluster debugging with live telemetry stored locally for short-term investigation	Open source, hosted or self-hosted	✓ Yes, in-cluster storage	✓ Native
Anteon	Performance testing combined with eBPF observability for load-test-driven debugging	Pro from $99/month plus usage	✓ Yes, cloud or self-hosted	✓ Native
Grafana Beyla	OpenTelemetry-native teams that want vendor-neutral eBPF instrumentation feeding existing backends	Free, open source	✗ Exports to OTLP/Prometheus	✓ Kubernetes metadata
Odigos	Auto-instrumentation control plane for OpenTelemetry with eBPF fallback for uninstrumented services	Open source, Enterprise custom pricing	✗ Exports to any OTLP backend	✓ Native
Grafana Pyroscope	Always-on CPU profiling with eBPF integration for flame graphs and performance bottleneck identification	Free OSS, Grafana Cloud from $50/month	✓ Yes, self-hosted or cloud	Partial
Parca	Continuous profiling focused on resource efficiency and low overhead eBPF data collection	Open source, Cloud pricing TBD	✓ Yes, self-hosted or cloud	Partial
Groundcover	APM and infrastructure monitoring with eBPF instrumentation and cost analytics	Custom enterprise pricing	✓ Yes, SaaS	✓ Native

Why Teams Are Adopting eBPF for Production Observability

Traditional observability stacks require three things that create friction at scale: manual instrumentation of every service, language-specific agents that add latency and memory overhead, and sampling decisions made at the edge before telemetry reaches your backend. eBPF removes all three constraints.

eBPF Captures Telemetry at the Kernel Layer

Instead of instrumenting application code, eBPF programs attach to kernel hooks like system calls, socket operations, and process events. This means an eBPF agent can capture HTTP requests, database queries, and service-to-service calls for any process running on that node, regardless of whether the application has OpenTelemetry instrumentation or proprietary agents installed.

A production example: On a 50-node Kubernetes cluster running 200 microservices, manually instrumenting every service with OpenTelemetry SDKs requires coordination across multiple teams, language-specific libraries, and deployment cycles. An eBPF-based tool like CubeAPM or Metoro instruments all 200 services automatically by deploying a single DaemonSet that captures kernel-level network and process telemetry across every node.

No Sampling at the Edge Means Complete Trace Fidelity

Most APM tools sample traces at the application or agent level, typically retaining 1-5% of traffic. That works until you need the one trace that was dropped, a slow query that occurred during a brief spike, a memory leak that only surfaces under specific load, or an intermittent error that happens once every 10,000 requests.

eBPF tools capture everything at the kernel and can apply smart sampling after collection, using context like latency, error codes, or transaction type. This approach is what CubeAPM calls AI-driven smart sampling, where the sampling decision uses full trace context instead of random chance. The result: 95% storage reduction without losing the diagnostic value of the data.

Lower Overhead Than Traditional Agents

eBPF programs are verified by the kernel before execution, ensuring they cannot crash the system or access memory unsafely. Because they run in kernel space, they avoid the context switching and memory allocation overhead of user-space agents. Performance studies show eBPF instrumentation typically adds less than 2% CPU overhead, compared to 5-10% for traditional APM agents.

A Reddit user on r/devops documented a case where replacing Datadog agents with an eBPF-based observability tool reduced node CPU consumption from 8% to under 2%, which allowed them to run more application replicas per node and reduce their infrastructure bill by 18% without changing workload density.

1. CubeAPM

Full-stack eBPF observability platform with self-hosted deployment and unlimited retention

CubeAPM is a self-hosted observability platform that combines eBPF-based auto-instrumentation with APM, logs, infrastructure monitoring, RUM, synthetics, and error tracking. It runs inside your cloud or on premises, so telemetry never leaves your infrastructure. The platform uses AI-driven smart sampling to reduce storage while retaining traces that matter, traces with high latency, error codes, or anomalous behavior.

Key Features:

• eBPF auto-instrumentation captures HTTP, gRPC, database queries, and Kafka messages without code changes
• Full-stack observability: traces, logs, metrics, RUM, synthetics, and Kubernetes monitoring in one platform
• Self-hosted deployment with vendor-managed updates, keeping telemetry inside your VPC or data center
• AI-driven smart sampling reduces storage by up to 95% while retaining diagnostic traces
• Compatible with OpenTelemetry, Prometheus, Datadog, and New Relic agents for incremental migration

Pricing: $0.15/GB ingestion with unlimited retention and no per-user fees

Pros:

• Unified platform reduces tool sprawl, no separate bills for logs, APM, and infrastructure monitoring
• Predictable ingestion-based pricing scales linearly without surprise overages
• Self-hosted deployment ensures compliance with data residency and HIPAA requirements
• Works alongside existing agents, allowing gradual migration without ripping out your current stack

Cons:

• Requires managing infrastructure, even with vendor-managed updates
• Kubernetes-focused, less suited for serverless-heavy or non-containerized workloads
• No autonomous anomaly detection, alerting requires manual threshold configuration

Best for: Teams that want full-stack observability with eBPF auto-instrumentation inside their own cloud, avoiding SaaS data egress and unpredictable pricing.

2. Metoro

Kubernetes-native eBPF observability with AI-driven root cause analysis

Metoro is a Kubernetes observability platform that uses eBPF to capture logs, metrics, traces, profiling data, and service relationships without manual instrumentation. Its differentiator is the AI SRE layer, which uses the eBPF telemetry to perform root cause analysis, investigate alerts, verify deployments, and suggest fixes during incidents.

Key Features:

• eBPF-based auto-instrumentation for HTTP, gRPC, database queries, and message queues
• Unified observability: logs, metrics, traces, continuous profiling, service maps, and Kubernetes state
• AI SRE workflows for root cause analysis, alert investigation, and deployment verification
• SaaS, BYOC, and on-premises deployment options for data sovereignty
• OpenTelemetry support for custom instrumentation beyond automatic eBPF data

Pricing: Free tier available, Scale plan from $20/node/month with 100GB included per node

Pros:

• One Helm install provides full observability stack, no need to stitch together multiple tools
• AI SRE layer accelerates incident response by correlating telemetry with deployment and code context
• Strong fit for teams with Kubernetes-heavy workloads and limited SRE headcount
• Multiple deployment models including air-gapped environments for regulated industries

Cons:

• Kubernetes-centric, not designed for non-containerized or serverless-first architectures
• Requires eBPF and DaemonSet permissions, which may conflict with restrictive security policies
• Proprietary platform, not open source

Best for: Kubernetes teams that need logs, traces, metrics, profiling, and AI-driven root cause analysis in one platform.

3. Coroot

Open-source eBPF observability with service maps, profiling, and SLO tracking

Coroot is an open-source observability platform built around eBPF-based monitoring. It provides service maps, logs, metrics, traces, continuous profiling, SLO tracking, cloud cost monitoring, and AI-powered root cause analysis. The platform is self-hosted, making it a strong fit for teams that want full control over their observability infrastructure without committing to a proprietary SaaS backend.

Key Features:

• eBPF-based monitoring captures network, process, and application behavior automatically
• Service maps, metrics, logs, traces, and continuous profiling in one unified view
• SLO tracking and cloud cost monitoring for Kubernetes workloads
• AI-powered root cause analysis to accelerate troubleshooting
• Relies on Prometheus and ClickHouse for backend storage and querying

Pricing: Community Edition available, Standard from $1 per monitored CPU core/month, Premium contact sales

Pros:

• Open-source Community Edition with clear upgrade path to paid support
• Transparent per-core pricing instead of opaque enterprise-only models
• Strong Kubernetes observability coverage with cost and SLO visibility
• Self-hosted deployment keeps telemetry under your control

Cons:

• Operational ownership is higher than managed SaaS platforms
• Requires scaling and maintaining Prometheus and ClickHouse at larger data volumes
• UI and workflow polish may lag behind larger commercial observability vendors

Best for: Open-source teams that want self-hosted eBPF observability with service maps, profiling, and SLOs.

4. Pixie

In-cluster eBPF Kubernetes observability for live debugging

Pixie is an open-source observability tool that uses eBPF to capture telemetry such as full-body HTTP/gRPC requests, resource metrics, network behavior, application profiles, and service maps without manual instrumentation. Its primary differentiator is that telemetry is stored locally inside the cluster, making it ideal for fast debugging without shipping every byte of raw telemetry to a central backend first.

Key Features:

• eBPF-based automatic capture of full HTTP/gRPC request bodies, SQL queries, and Kafka messages
• Telemetry stored in-cluster for fast, low-latency querying during live debugging
• Scriptable PxL query language for custom analysis and filtering
• Service maps and application profiling without code changes
• Open-source CNCF sandbox project with hosted and self-hosted deployment models

Pricing: Open source, hosted Pixie and self-hosted Pixie available

Pros:

• Very strong developer debugging workflow with live cluster views
• Minimal external data movement for short-term troubleshooting
• Detailed request-level telemetry without requiring application instrumentation
• Active CNCF community with regular feature updates

Cons:

• Designed for live debugging, not long-term retention or historical analysis
• Kubernetes-only, no support for VMs, serverless, or non-containerized workloads
• Teams usually still need another system for long-term alerting, dashboards, and reporting

Best for: Fast in-cluster debugging with live telemetry stored locally for short-term investigation.

5. Anteon

eBPF Kubernetes observability paired with no-code performance testing

Anteon is a Kubernetes monitoring platform built around its open-source eBPF agent, Alaz. It captures service interactions, service maps, metrics, traces, SQL queries, HTTP status codes, RPS, latency, and infrastructure metrics automatically. Anteon’s main differentiator is pairing eBPF observability with no-code performance testing, allowing teams to run load tests and inspect the resulting service-to-service behavior in the same UI.

Key Features:

• eBPF-based Alaz agent captures service maps, metrics, and traces without code changes
• No-code performance testing integrated with observability data
• HTTP, gRPC, SQL, and infrastructure metric collection via eBPF
• Cloud and self-hosted deployment options
• OpenTelemetry compatibility for exporting telemetry to external backends

Pricing: Pro starts at $99/month plus usage dimensions for traces, logs, metrics, and user simulation, Enterprise contact sales

Pros:

• Fast Kubernetes setup with single DaemonSet deployment
• Service map and metrics dashboard with no sidecars or manual instrumentation
• Useful for load-test-driven debugging because performance testing and monitoring live together
• Multiple deployment models including self-hosted

Cons:

• Narrower product focus than broader observability platforms like Metoro or CubeAPM
• Pricing includes usage dimensions for traces, logs, metrics, and user simulation, so bill predictability depends on usage
• Not primarily a continuous profiling tool

Best for: Performance testing combined with eBPF observability for load-test-driven debugging.

6. Grafana Beyla / OpenTelemetry eBPF Instrumentation

eBPF auto-instrumentation exporter for OpenTelemetry backends

Grafana Beyla is an eBPF-based application auto-instrumentation tool that inspects application executables and the OS networking layer, then exports traces and RED metrics for Linux HTTP/S and gRPC services without code changes. In 2025, Grafana Labs donated Beyla’s core instrumentation engine to OpenTelemetry as OpenTelemetry eBPF Instrumentation, commonly called OBI. Beyla continues as Grafana’s distribution of the upstream project.

Key Features:

• eBPF-based instrumentation at the executable and protocol layer for broad language/runtime coverage
• Exports OpenTelemetry traces and Prometheus metrics to any OTLP or Prometheus backend
• Kubernetes metadata decoration for Pod, Service, and namespace context
• Supports HTTP, HTTPS, gRPC, and generic TCP/UDP protocols
• Vendor-neutral telemetry generation, no lock-in to Grafana Cloud

Pricing: Free, open source

Pros:

• Vendor-neutral telemetry path through OpenTelemetry and Prometheus exports
• Strong fit for teams standardizing on Grafana Cloud, Grafana Alloy, Prometheus, Tempo, or another OTLP backend
• Broad language/runtime coverage because it instruments at the executable and protocol layer
• Can decorate Kubernetes telemetry with Pod and Service metadata

Cons:

• Not a complete observability platform by itself, you still need storage, querying, dashboards, and alerting
• Requires eBPF and kernel version 5.8+, which may not be available in older Linux distributions
• Limited to network-layer protocols, does not capture application-specific business logic or custom spans

Best for: OpenTelemetry-native teams that want vendor-neutral eBPF instrumentation feeding existing backends.

7. Odigos

OpenTelemetry control plane with eBPF fallback for uninstrumented services

Odigos is an OpenTelemetry control plane that automates instrumentation across Kubernetes workloads. It uses OpenTelemetry SDKs for application-level instrumentation when available and falls back to eBPF-based instrumentation for services that lack manual instrumentation. Odigos acts as a centralized control plane for managing OpenTelemetry pipelines, collectors, and instrumentation across your entire cluster.

Key Features:

• Automatic OpenTelemetry instrumentation across multiple languages with eBPF fallback
• Centralized control plane for managing collectors, pipelines, and destination backends
• Supports exporting telemetry to any OTLP-compatible backend including Grafana, Jaeger, and commercial vendors
• Kubernetes-native deployment with CRD-based configuration
• Open-source core with Enterprise features for larger teams

Pricing: Open source, Enterprise custom pricing

Pros:

• Simplifies OpenTelemetry adoption by automating SDK injection and collector configuration
• eBPF fallback ensures uninstrumented services still produce telemetry
• Vendor-neutral, works with any OTLP backend
• Strong fit for teams standardizing on OpenTelemetry across the organization

Cons:

• Does not include a backend for storage, querying, or visualization
• Requires Kubernetes, not suitable for non-containerized workloads
• eBPF fallback is less feature-rich than application-level OpenTelemetry SDKs

Best for: Auto-instrumentation control plane for OpenTelemetry with eBPF fallback for uninstrumented services.

8. Grafana Pyroscope

Continuous profiling with eBPF integration for performance bottleneck identification

Grafana Pyroscope is a continuous profiling platform that captures CPU profiles from running applications to identify performance bottlenecks, memory leaks, and inefficient code paths. It integrates eBPF profiling for languages like Go, Rust, C, and C++, capturing stack traces without requiring application changes or recompilation.

Key Features:

• eBPF-based continuous profiling for CPU and memory usage
• Flame graphs and differential flame graphs for visualizing performance changes
• Integration with Grafana Cloud and self-hosted Grafana stacks
• OpenTelemetry compatibility for correlating profiles with traces
• Multi-language support including Go, Rust, C, C++, Python, Java, and Node.js

Pricing: Free OSS, Grafana Cloud from $50/month

Pros:

• Always-on profiling with minimal overhead using eBPF
• Strong integration with Grafana ecosystem for unified observability
• Open-source project with active development and community support
• Differential profiling helps identify performance regressions between deployments

Cons:

• Profiling-focused, not a full observability platform
• Requires Grafana for visualization and querying
• eBPF profiling limited to specific languages and kernel versions

Best for: Always-on CPU profiling with eBPF integration for flame graphs and performance bottleneck identification.

9. Parca

Open-source continuous profiling with low overhead eBPF data collection

Parca is an open-source continuous profiling tool designed for resource efficiency and low overhead data collection using eBPF. It captures CPU profiles across your infrastructure and provides flame graphs, differential analysis, and correlation with infrastructure metrics to help identify performance bottlenecks.

Key Features:

• eBPF-based profiling with minimal CPU and memory overhead
• Open-source project with self-hosted and cloud deployment models
• Flame graphs and differential profiling for performance analysis
• Integration with Prometheus and OpenTelemetry for metric correlation
• Multi-language support for compiled and interpreted languages

Pricing: Open source, Cloud pricing TBD

Pros:

• Very low overhead eBPF profiling suitable for production environments
• Open-source with no vendor lock-in
• Strong community support and active development
• Self-hosted deployment keeps profiling data under your control

Cons:

• Profiling-focused, not a full observability platform
• Requires manual integration with other observability tools for logs and traces
• Cloud offering still in development, self-hosted requires operational overhead

Best for: Continuous profiling focused on resource efficiency and low overhead eBPF data collection.

10. Groundcover

APM and infrastructure monitoring with eBPF instrumentation and cost analytics

Groundcover is an APM and infrastructure monitoring platform that uses eBPF for automatic instrumentation. It captures application traces, metrics, logs, and Kubernetes infrastructure data without requiring code changes, and includes cost analytics to help teams understand the financial impact of their observability infrastructure.

Key Features:

• eBPF-based APM and infrastructure monitoring for Kubernetes
• Automatic service maps, traces, and metrics without manual instrumentation
• Cost analytics for understanding observability infrastructure spend
• Real-time alerting and anomaly detection
• SaaS deployment model

Pricing: Custom enterprise pricing

Pros:

• Comprehensive eBPF observability with cost visibility
• Strong Kubernetes integration with infrastructure and application monitoring
• Automatic instrumentation reduces onboarding friction
• Designed for enterprise teams with complex Kubernetes environments

Cons:

• Enterprise pricing only, not transparent or self-service
• SaaS-only deployment, no on-premises or BYOC option
• Requires eBPF and kernel permissions that may not be available in all environments

Best for: APM and infrastructure monitoring with eBPF instrumentation and cost analytics.

How to Choose the Right eBPF Monitoring Tool

Choosing an eBPF monitoring tool depends on five factors: whether you need a complete observability backend or just the instrumentation layer, your deployment model requirements, Kubernetes maturity, team size, and budget predictability.

Do You Need a Backend or Just the Instrumentation?

Full-stack platforms like CubeAPM, Metoro, Coroot, and Pixie include the entire observability stack, storage, querying, dashboards, and alerting. These are the right choice if you want one unified platform and do not want to manage separate backends.

Instrumentation-only tools like Grafana Beyla and Odigos generate telemetry using eBPF but require you to send it to another backend like Grafana Cloud, Tempo, Prometheus, or a commercial vendor. Choose these if you already have an observability backend and want to add eBPF instrumentation without replacing your entire stack.

Profiling-focused tools like Grafana Pyroscope and Parca solve a specific problem, always-on CPU profiling, but do not replace your APM or log management system. Use these alongside your existing observability stack when performance bottlenecks are your primary concern.

Deployment Model: SaaS, Self-Hosted, or BYOC?

If data residency, HIPAA, GDPR, or cost control around data egress are concerns, self-hosted or BYOC tools like CubeAPM, Coroot, or Pixie self-hosted are the right fit. These run inside your cloud or on premises, keeping telemetry within your infrastructure.

SaaS-only tools like Metoro, Anteon, and Groundcover are faster to onboard and require no infrastructure management, but telemetry leaves your environment. That creates egress costs on AWS/GCP/Azure and may conflict with compliance requirements.

Hybrid tools like Grafana Beyla and Odigos are vendor-neutral, you control where telemetry is sent and can choose between SaaS, self-hosted, or on-premises backends.

Kubernetes Maturity and Use Case Fit

If your workloads are primarily Kubernetes-based, tools like Metoro, Coroot, Pixie, Anteon, and Odigos are purpose-built for Kubernetes and provide first-class integration with Pods, Services, Namespaces, and cluster metadata.

If you run mixed workloads, VMs, serverless, and Kubernetes, full-stack platforms like CubeAPM and Groundcover provide broader coverage beyond just Kubernetes.

If you are debugging specific performance issues rather than building a full observability stack, Pixie’s in-cluster live debugging or Grafana Pyroscope’s continuous profiling may be the right tool for the job.

Budget Predictability and Pricing Model

CubeAPM and Coroot use ingestion-based or per-core pricing with no per-user fees, making costs predictable as teams grow. Metoro uses per-node pricing with included data, which scales linearly with infrastructure. Grafana Beyla, Odigos, Grafana Pyroscope, and Parca are open source with no instrumentation costs, but you pay for backend storage separately.

Anteon and Groundcover use custom enterprise pricing, which works for large teams with procurement processes but lacks transparency for smaller teams evaluating costs upfront.

Monitoring eBPF Infrastructure with CubeAPM

CubeAPM provides full-stack observability for eBPF-instrumented workloads by combining eBPF auto-instrumentation with APM, logs, infrastructure monitoring, and Kubernetes visibility. It deploys as a DaemonSet inside your cluster, captures telemetry at the kernel level, and stores it in your own cloud or on premises, eliminating data egress costs and compliance concerns.

CubeAPM connects to your Kubernetes cluster via OpenTelemetry or Prometheus and surfaces infrastructure monitoring metrics including node health, pod resource usage, and service-to-service latency. Its eBPF-based instrumentation captures HTTP, gRPC, database queries, and Kafka messages without requiring code changes, then correlates those traces with logs and metrics for full-context troubleshooting.

The platform uses AI-driven smart sampling to reduce storage by up to 95% while retaining traces with high latency, errors, or anomalous behavior. This approach is particularly valuable in eBPF environments where raw telemetry volume is high because eBPF captures every kernel event, not just sampled application traces.

For teams running Docker containers or MongoDB databases inside Kubernetes, CubeAPM’s eBPF instrumentation captures container-level metrics and database query performance automatically, eliminating the need for separate monitoring agents.

Frequently Asked Questions

Disclaimer: The information in this article reflects the latest details available at the time of publication and may change as technologies and products evolve. Features, pricing, and plan limits can change over time. Always verify the latest information directly with the vendor before making purchasing or deployment decisions.