7 Best BCC Alternatives for eBPF Tracing in 2026

BCC (BPF Compiler Collection) pioneered accessible eBPF tracing on Linux with over 100 ready-to-use performance tools and Python APIs for custom development. While BCC remains widely deployed, its Python-based runtime requires kernel headers at compile time and can be resource-intensive for large-scale deployments.

Modern eBPF tooling has evolved beyond BCC's architecture. Newer alternatives use CO-RE (Compile Once, Run Everywhere) to eliminate kernel header dependencies, provide more concise syntax for ad-hoc investigation, or integrate natively with Kubernetes. Some focus on specific use cases like continuous profiling or security monitoring, while others offer managed observability platforms that handle the operational complexity of eBPF at scale.

Why Look for BCC Alternatives?

BCC established eBPF as a practical observability technology, but several limitations have driven development of alternative approaches:

Kernel header dependencies complicate deployment. BCC compiles eBPF programs at runtime, requiring kernel headers on every target system. This adds deployment complexity and storage overhead, especially in containerized environments where you'd need headers for each kernel version across your fleet.

Python runtime overhead affects resource-constrained environments. While manageable for most use cases, BCC's Python interpreter and compilation step consume more resources than newer CO-RE-based tools. This matters for embedded systems, edge devices, or high-density container deployments.

Verbose syntax slows investigation. Writing BCC tools requires C for kernel code and Python for userspace, plus understanding BCC's API conventions. For quick troubleshooting, this is heavier than needed compared to high-level tracing languages.

Limited Kubernetes integration requires extra work. BCC tools run on individual hosts and return process IDs without Kubernetes context. Correlating results with pods, namespaces, or deployments requires manual mapping or additional tooling.

Production observability needs more than CLI tools. Running BCC tools manually works for investigation, but production monitoring needs data retention, querying, alerting, dashboards, and team collaboration that BCC doesn't provide.

The Best BCC Alternatives in 2026

1. bpftrace

bpftrace offers a high-level tracing language inspired by awk and DTrace for concise one-liners and short scripts. If BCC feels too heavyweight for quick investigations, bpftrace provides the fastest path from question to answer.

bpftrace's minimal syntax makes complex tracing accessible immediately. Write one-liners that trace kernel or userspace functions, filter events, aggregate data, and print results without boilerplate code. The language handles compilation to eBPF bytecode, loading into the kernel, and displaying output automatically.

Unlike BCC's Python runtime, bpftrace compiles directly to eBPF. This reduces resource usage and eliminates the need for Python dependencies. The tool ships with a collection of ready-to-use scripts in its tools/ directory for common investigations.

Main Benefits:

Extremely concise syntax for ad-hoc tracing
No Python runtime overhead
Built-in aggregation functions and histograms
Supports all probe types (kprobes, uprobes, tracepoints, USDT)
Write and execute immediately without compilation step
Growing collection of example scripts

2. Inspektor Gadget

Inspektor Gadget brings eBPF tracing natively to Kubernetes as kubectl plugins. Where BCC requires SSH access to nodes and manual correlation with Kubernetes resources, Inspektor Gadget integrates directly with kubectl and automatically enriches traces with pod context.

Inspektor Gadget packages eBPF tracing tools as Kubernetes-native gadgets. Run kubectl gadget trace tcp to monitor TCP connections or kubectl gadget profile cpu to profile CPU usage, targeting specific pods, namespaces, or nodes. Results include pod names, namespaces, and labels automatically—no manual correlation needed.

Each gadget wraps proven eBPF programs in a container-aware interface. The project includes gadgets for DNS tracing, filesystem operations, network activity, and more. You can develop custom gadgets by packaging eBPF programs as OCI images and deploying through the framework.

Main Benefits:

kubectl integration for Kubernetes-native tracing
Automatic context enrichment (pod, namespace, labels)
No SSH access to nodes required
Collection of ready-to-use gadgets
Framework for custom gadget development
CNCF sandbox project with active development

3. Cilium Hubble

Cilium with Hubble provides comprehensive network observability using eBPF. While BCC focuses on general system tracing, Cilium specializes in Kubernetes networking with deep visibility into service-to-service communication at both packet and protocol level.

Hubble monitors network flows without sidecars or application instrumentation. See HTTP requests, gRPC calls, Kafka messages, and DNS queries with response codes, latency, and payload information. Cilium's eBPF programs run in the kernel's network stack, observing every packet without performance overhead from proxies.

The Hubble UI visualizes service dependencies discovered from actual traffic. When pods can't communicate, Hubble shows exactly why—network policy drops, DNS failures, or response issues. The service map updates in real-time based on observed behavior rather than static configuration.

Main Benefits:

Zero instrumentation network observability for Kubernetes
Layer 7 protocol visibility (HTTP, gRPC, Kafka, DNS)
Real-time service dependency mapping
Network policy debugging with detailed flow logs
CNCF graduated project with strong adoption
Combines observability with networking and security

4. Parca

Parca specializes in continuous profiling using eBPF. Where BCC requires running profiling tools manually, Parca provides always-on CPU profiling across your infrastructure with low overhead and efficient storage.

Parca's eBPF profiler samples stack traces continuously at 19Hz by default. This creates a permanent record of where CPU time went, making it possible to investigate performance issues after they occur rather than trying to reproduce them. The eBPF approach profiles all languages from a single collector without language-specific agents.

Profile data compresses efficiently for long-term retention. Query profiles by time range, service, pod, or custom labels. Compare profiles across deployments to identify performance regressions or validate optimizations. Differential flamegraphs show exactly which functions consumed more or less CPU between two profiles.

Main Benefits:

Continuous CPU profiling with eBPF
Multi-language support without per-language agents
Low overhead suitable for always-on production use
Interactive flamegraph visualization
Differential profiling for before/after comparisons
Efficient storage for long-term retention

5. Pixie

Pixie automatically instruments Kubernetes applications using eBPF for comprehensive observability without code changes. Unlike BCC's CLI-focused approach, Pixie provides a complete platform with automatic instrumentation, edge storage, and a query language.

Pixie's eBPF programs trace network system calls and library functions to reconstruct application behavior automatically. This captures HTTP requests, database queries with parameters, gRPC calls, and DNS lookups from observing traffic without explicit instrumentation. Legacy applications and third-party services get monitored without modification.

Data stays on cluster nodes for fast queries and low costs. Unlike traditional platforms that stream everything centrally, Pixie stores recent data locally on nodes. Queries run against this edge storage, returning results in milliseconds without data egress charges or ingestion delays.

Main Benefits:

Automatic tracing without code changes or SDK installation
Protocol visibility (HTTP, gRPC, PostgreSQL, MySQL, Redis, Kafka)
Edge storage for fast queries and cost savings
PxL query language familiar to Python developers
Open-source with community edition available
Acquired by New Relic with continued development

6. Tracee

Tracee focuses on runtime security and forensics using eBPF. While BCC provides general tracing capabilities, Tracee specializes in security event capture with signatures mapped to the MITRE ATT&CK framework.

Tracee records system calls, process events, file operations, and network activity with complete argument capture. This creates forensic-grade audit trails for investigating security incidents or debugging complex issues. Event capture runs continuously with configurable filters to focus on relevant activity.

Security signatures detect known attack patterns automatically. These identify privilege escalation techniques, container escape attempts, credential theft, and defense evasion tactics. Tracee can replay captured events to reconstruct incidents showing which processes ran, files accessed, and network connections made.

Main Benefits:

Forensic-grade event tracing with full argument capture
Security signatures detecting known attack patterns
MITRE ATT&CK framework mapping
Event capture and replay for incident investigation
Container and Kubernetes context awareness
Open-source with commercial support from Aqua Security

7. kubectl-trace

kubectl-trace brings bpftrace to Kubernetes without requiring node SSH access. Where BCC tools need manual deployment to cluster nodes, kubectl-trace packages bpftrace as Kubernetes Jobs that run on specific nodes or against specific pods.

kubectl-trace handles bpftrace deployment and cleanup automatically. Provide your bpftrace script, target a pod or node, and kubectl-trace runs it for you. This makes powerful eBPF tracing accessible to anyone with kubectl access without needing node SSH keys or manual tool installation.

Target traces using familiar Kubernetes selectors. Specify pods, nodes, or namespaces with standard kubectl syntax. The trace runs on the appropriate node, automatically scoped to the right container.

Main Benefits:

Run bpftrace programs through kubectl
No SSH access to nodes required
Target specific pods, nodes, or namespaces
Automatic deployment and cleanup
Integrates with Kubernetes RBAC
Open-source from the BCC/bpftrace community

Commercial eBPF Observability Platforms

While open-source tools like BCC excel for hands-on investigation, production observability at scale requires platforms that handle data retention, querying, alerting, dashboards, and team collaboration. Commercial platforms provide managed eBPF capabilities without operational complexity.

Better Stack

Better Stack provides comprehensive eBPF-based observability with automatic instrumentation, unified dashboards, and AI-powered analysis. Where BCC requires running tools manually and processing results yourself, Better Stack handles the entire observability pipeline from collection to alerting.

Better Stack's collector uses eBPF to instrument Kubernetes and Docker environments automatically. Deploy it once and start gathering distributed traces, logs, and metrics without touching application code, restarting services, or adding language-specific SDKs.

The eBPF approach captures network-level telemetry that reveals application behavior: HTTP requests with latency and status codes, database queries with parameters and execution time, gRPC calls, message queue operations, and cache interactions. This works for any application, including legacy systems and third-party services you can't modify. Better Stack's collector recognizes PostgreSQL, MySQL, Redis, Memcached, and MongoDB instances automatically.

Better Stack generates service dependency maps by observing actual network traffic with eBPF. The map shows which services communicate, request volumes, latency distributions, and error rates, all derived from observed behavior rather than static configuration.

Better Stack provides real-time log analysis with Live Tail for immediate visibility into your systems. Filter by severity levels, search for specific patterns, or track user sessions across multiple requests as they happen.

Create custom dashboards using drag-and-drop builders or SQL queries to visualize metrics and traces. Better Stack's logs-to-metrics feature automatically extracts structured metrics from your log data, eliminating the need for separate instrumentation.

eBPF's in-kernel aggregation reduces data volumes dramatically compared to traditional tracing. Better Stack pairs this with ClickHouse storage to deliver distributed tracing at up to 30x lower cost than competitors like Datadog. Store complete traces without sampling. Run SQL or PromQL queries across billions of spans with sub-second response times.

Better Stack is OpenTelemetry-native, combining eBPF's automatic instrumentation with OpenTelemetry SDK flexibility. Use eBPF for infrastructure-level visibility and OpenTelemetry SDKs for application-specific context. Both data sources flow into the same query engine and dashboards.

Set up anomaly detection alerts to receive notifications when your logs show unusual patterns. Get unlimited phone call and SMS alerts when critical system errors occur, disk space runs low, or security events are detected.

Better Stack's AI SRE uses eBPF-collected telemetry for automated root cause analysis. During incidents, it correlates network traces from eBPF, service dependencies, error patterns, and performance metrics to suggest likely causes.

Main Benefits:

eBPF auto-instrumentation for Kubernetes and Docker
Automatic database monitoring without agents
Service maps generated from eBPF network traces
30x cheaper than Datadog with predictable pricing
OpenTelemetry-native for vendor flexibility
SQL and PromQL queries with sub-second response times
ClickHouse storage for cost-effective retention
AI-powered root cause analysis
Integration with Better Stack Uptime for comprehensive observability
Available in 4 regions with custom deployments
SOC 2 Type 2, GDPR, and ISO 27001 compliant
60-day money-back guarantee

Final thoughts

BCC remains a powerful eBPF tracing toolkit with an extensive library of tools and proven production reliability. Its Python API enables sophisticated custom instrumentation, and its comprehensive documentation helps engineers learn eBPF concepts.

However, modern alternatives address BCC's limitations in specific ways. bpftrace offers more concise syntax for investigation, Inspektor Gadget integrates natively with Kubernetes, Parca provides continuous profiling, and Better Stack delivers managed observability at scale.

Start by evaluating which BCC limitations affect your workflows most. If deployment complexity or kernel header dependencies are pain points, explore CO-RE-based alternatives. If Kubernetes integration matters, try Inspektor Gadget. If you need managed observability with automatic instrumentation, Better Stack eliminates operational overhead entirely.

The best approach often combines tools—keeping BCC for specific investigations while adopting alternatives for ad-hoc troubleshooting, Kubernetes operations, or production monitoring. The eBPF ecosystem is rich enough to support mixed strategies that leverage each tool's strengths.

Got an article suggestion? Let us know

Explore more

10 Best Cron Job Monitoring Tools in 2026

Cron jobs are a programmer's helper in the automation of repetitive yet critical tasks in the life cycle of every program. That's why they should work as they are expected to. However, we know, that flawless program runs and fairies live in the same realm, and in order to keep the magic alive, we need to check our cron jobs on a regular basis. That is achievable with cron job monitoring.

8 Best Open-Source eBPF Tracing Tools in 2026

Discover the best open-source eBPF tracing tools for Linux observability. Compare BCC, bpftrace, Cilium, Pixie, and more for performance analysis and debugging.

10 Best Linux Monitoring Tools in 2026

Linux is a go-to platform for most developers. However, Linux machines are often left paralyzed by their own design and monitoring can help you prevent it.