7 Best bpftrace Alternatives for Linux Tracing in 2026

bpftrace provides a high-level tracing language for Linux eBPF with concise one-liners inspired by awk and DTrace. Its minimal syntax makes complex kernel and application tracing accessible through simple commands, from capturing stack traces to building histograms of system behavior.

While bpftrace excels at ad-hoc investigation, several limitations drive users toward alternatives: the learning curve for its scripting language, lack of persistent data storage, minimal built-in visualization, and absence of team collaboration features. Some teams need more powerful analysis capabilities, GUI-based tools, or managed platforms that handle the operational complexity of production observability.

Why Look for bpftrace Alternatives?

bpftrace democratized eBPF tracing with approachable syntax, but specific use cases reveal gaps:

Limited data retention and querying. bpftrace outputs results to stdout and terminates. For historical analysis, correlating events over time, or building dashboards, you need external tools to capture and store bpftrace output. This adds complexity and doesn't scale well.

No built-in visualization beyond ASCII histograms. While bpftrace can generate histograms and frequency counts, visualizing results requires piping to other tools or manual processing. Flamegraphs, service maps, and interactive dashboards need additional tooling.

Single-user, single-host focus. bpftrace runs on one system at a time. Tracing across distributed systems, correlating data from multiple hosts, or sharing results with team members requires orchestration you must build yourself.

Learning curve for the scripting language. Though simpler than writing C, bpftrace's awk-inspired syntax still requires learning probe types, built-in variables, and the map-reduce model. Teams wanting immediate results without training may prefer GUI tools or managed platforms.

Manual deployment in Kubernetes. Running bpftrace on Kubernetes nodes requires SSH access or tools like kubectl-trace. Results don't automatically include pod context, requiring manual correlation with Kubernetes resources.

No alerting or anomaly detection. bpftrace is purely for investigation. Production monitoring needs automated alerting when metrics exceed thresholds or patterns deviate from baselines.

The Best bpftrace Alternatives in 2026

1. BCC (BPF Compiler Collection)

BCC provides over 100 ready-to-run eBPF tracing tools plus Python and C APIs for custom development. Where bpftrace requires writing scripts for each investigation, BCC offers finished tools that answer specific performance questions immediately.

BCC's single-purpose tools eliminate the need to write tracing code. Need to see slow filesystem operations? Run ext4slower. Want TCP retransmit details? Use tcpretrans. Each tool is production-ready with detailed man pages and practical examples. No scripting required for common investigations.

For custom instrumentation, BCC provides more control than bpftrace. The Python and C APIs let you build sophisticated analysis tools with custom output formatting, complex aggregations, and integration with external systems. Major tech companies rely on BCC for production observability at scale.

Main Benefits:

100+ ready-to-use tracing tools included
No scripting required for common use cases
Python and C APIs for custom development
More powerful than bpftrace for complex analysis
Battle-tested at Facebook, Netflix, and others
Extensive documentation and man pages

2. Inspektor Gadget

Inspektor Gadget brings eBPF tracing natively to Kubernetes through kubectl plugins. Where bpftrace requires SSH access to nodes and manual Kubernetes context correlation, Inspektor Gadget integrates directly with kubectl and enriches results automatically.

Inspektor Gadget packages eBPF tools as Kubernetes-native gadgets. Run kubectl gadget trace tcp to monitor TCP connections or kubectl gadget profile cpu to profile CPU usage. Results include pod names, namespaces, and labels automatically. Target specific pods, nodes, or namespaces using familiar kubectl selectors.

Gadgets wrap proven eBPF programs in container-aware interfaces. The collection includes DNS tracing, filesystem operations, network activity, and more. Each gadget eliminates the need to write bpftrace scripts for Kubernetes environments while providing richer context than raw process IDs.

Main Benefits:

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

3. Parca

Parca specializes in continuous profiling using eBPF with automatic flamegraph visualization. Where bpftrace requires running profiling scripts manually and processing output separately, Parca provides always-on profiling with built-in storage and interactive visualization.

Parca's eBPF profiler samples stack traces continuously at 19Hz by default. This creates a permanent record of CPU usage, making it possible to investigate performance issues after they occur rather than trying to reproduce them. The web UI displays interactive flamegraphs without manual processing.

Profile data compresses efficiently for long-term retention. Query profiles by time range, service, pod, or custom labels through the web interface. Compare profiles across deployments to identify performance regressions or validate optimizations. Differential flamegraphs show exactly which functions changed between two time periods.

Main Benefits:

Continuous profiling without manual script execution
Interactive flamegraph visualization built-in
Efficient storage for long-term retention
Multi-language support without per-language agents
Query interface for historical analysis
Differential profiling for before/after comparisons

4. Pixie

Pixie automatically instruments Kubernetes applications using eBPF for comprehensive observability without writing any tracing code. Where bpftrace requires crafting scripts for each investigation, Pixie captures distributed traces, metrics, and logs automatically.

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 without any manual instrumentation. No scripts to write, no probes to attach—just deploy and query.

Data stays on cluster nodes for fast queries through PxL (Pixie Language). The query language resembles pandas DataFrames, making it accessible to anyone familiar with Python data analysis. Results appear in milliseconds without data egress charges or ingestion delays.

Main Benefits:

Automatic tracing without writing scripts
Protocol visibility (HTTP, gRPC, PostgreSQL, MySQL, Redis, Kafka)
Edge storage for fast queries and low costs
PxL query language familiar to Python developers
Web UI with built-in dashboards
Open-source with community edition available

5. Cilium Hubble

Cilium with Hubble provides network observability using eBPF with built-in visualization. Where bpftrace can trace network events with custom scripts, Hubble specializes in Kubernetes networking with automatic service dependency mapping and protocol-aware monitoring.

Hubble monitors network flows at both packet level and application level without any configuration. See HTTP requests, gRPC calls, Kafka messages, and DNS queries with response codes, latency, and payload information. The Hubble UI visualizes service dependencies and traffic flows automatically.

When pods can't communicate, Hubble shows exactly why. See if connections are dropped by network policy, DNS resolution failures, or response issues. The service map updates in real-time based on observed behavior, eliminating the need to write tracing scripts for network debugging.

Main Benefits:

Zero configuration network observability for Kubernetes
Layer 7 protocol visibility (HTTP, gRPC, Kafka, DNS)
Real-time service dependency mapping
Network policy debugging with detailed flow logs
Web UI with built-in visualization
CNCF graduated project with strong adoption

6. perf with eBPF support

perf is the standard Linux profiling tool with growing eBPF support. Where bpftrace focuses exclusively on eBPF, perf provides a broader toolkit including hardware performance counters, CPU profiling, and event tracing with optional eBPF extensions.

perf offers battle-tested profiling with optional eBPF enhancement. Use traditional perf features like perf record for CPU profiling or perf stat for hardware counters, then augment with eBPF when needed. This hybrid approach combines perf's maturity with eBPF's flexibility.

The perf trace command provides system call tracing similar to strace but with eBPF filtering. Custom eBPF programs can filter events, aggregate data, or extract additional context before sending to userspace. Integration with kernel tracepoints and PMCs provides insights beyond pure eBPF tracing.

Main Benefits:

Standard Linux tool included in kernel source
Hardware performance counter support
CPU profiling with perf record
Optional eBPF enhancement when needed
Mature tooling with extensive documentation
Flamegraph support with external scripts

7. ply

ply provides a lightweight tracing language similar to bpftrace but optimized for embedded systems. Where bpftrace requires LLVM for compilation, ply compiles to eBPF with minimal dependencies.

ply's syntax resembles bpftrace but produces smaller binaries. The entire ply binary is under 100KB, making it practical for embedded devices, containers, or systems where storage is constrained. Scripts use familiar probe syntax with actions for printing, aggregating, and filtering.

Compilation happens without LLVM dependencies. ply uses its own compiler that generates eBPF bytecode directly, reducing the installation footprint significantly. This makes ply suitable for resource-constrained environments where bpftrace's dependencies are prohibitive.

Main Benefits:

Lightweight alternative to bpftrace (under 100KB)
No LLVM dependency required
Familiar syntax for bpftrace users
Ideal for embedded systems and containers
Lower resource usage than bpftrace
Active development from iovisor project

Commercial eBPF Observability Platforms

While open-source tools like bpftrace excel for ad-hoc investigation, production observability requires platforms that handle data retention, visualization, alerting, and team collaboration. Commercial platforms provide managed eBPF capabilities without operational complexity.

Better Stack

Better Stack is a complete observability platform built on eBPF. It offers automatic instrumentation, unified dashboards, and AI-powered analysis in one place. While bpftrace requires you to run scripts manually and analyze the output yourself, Better Stack manages the whole process for you—from collecting data to sending alerts.

First, Better Stack’s collector uses eBPF to automatically instrument Kubernetes and Docker environments. You only need to deploy it once. After that, it starts collecting distributed traces, logs, and metrics without any tracing scripts or changes to your application code.

Because it works at the eBPF level, it captures detailed network telemetry that shows how your applications behave. This includes HTTP requests with latency and status codes, database queries with parameters and execution times, gRPC calls, message queue activity, and cache interactions. All of this works automatically for any application, with no manual instrumentation required.

In addition, Better Stack creates service dependency maps by observing real network traffic through eBPF. These maps show which services talk to each other, how many requests they handle, their latency patterns, and their error rates. Unlike temporary bpftrace scripts, this data is stored, visualized, and continuously updated.

For real-time visibility, Better Stack offers Live Tail log analysis. You can filter logs by severity, search for specific patterns, or follow a user session across multiple requests as events happen.

You can also build custom dashboards using drag-and-drop tools or SQL queries. Instead of reading raw output like in bpftrace, you get real-time dashboards with built-in visualizations for trends, distributions, and unusual behavior.

To stay informed about issues, Better Stack includes anomaly detection and alerting. You can receive unlimited phone calls and SMS alerts when critical errors happen, disk space runs low, or security events are detected. This replaces the need for manual monitoring with bpftrace.

Finally, Better Stack’s AI SRE uses the telemetry collected by eBPF to help with root cause analysis. During incidents, it connects network traces, service dependencies, error patterns, and performance metrics to suggest likely causes. In this way, it delivers the deep insights you might get from bpftrace, but with automated analysis and AI support.

Main Benefits:

eBPF auto-instrumentation without writing scripts
Automatic database monitoring without manual tracing
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

bpftrace revolutionized Linux tracing with its concise syntax and powerful eBPF capabilities. Its one-liner approach makes complex kernel instrumentation accessible to engineers who don't want to write C code or understand BPF internals.

However, modern alternatives address bpftrace's limitations in specific ways. BCC provides ready-to-use tools that eliminate scripting for common cases. Inspektor Gadget integrates natively with Kubernetes. Parca offers continuous profiling with built-in visualization. Better Stack delivers managed observability that handles collection, storage, alerting, and analysis automatically.

Start by evaluating which bpftrace limitations affect your workflows most. If writing scripts slows investigation, explore BCC's pre-built tools. If Kubernetes integration matters, try Inspektor Gadget. If you need persistent storage and dashboards, managed platforms like Better Stack eliminate operational overhead.

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

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