Open-Source Observability · pixie-io

pixie

Pixie is an open-source Kubernetes observability platform that automatically collects telemetry data (requests, metrics, profiles) using eBPF without code instrumentation. It stores and queries data locally in-cluster with minimal overhead (typically <5% CPU) and provides UI, CLI, and API interfaces powered by PxL, a Python-like query language.

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Source: GitHub — github.com/pixie-io/pixie

6.5k

GitHub stars

501

Forks

C++

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	pixie-io/pixie
Owner	pixie-io
Primary language	C++
License	Apache-2.0 — OSI-approved
Stars	6.5k
Forks	501
Open issues	388
Latest release	release/cloud/v0.1.9 (2025-01-24)
Last updated	2026-06-22
Source	https://github.com/pixie-io/pixie

What pixie is

Pixie uses eBPF to auto-capture full-body requests, resource metrics, and application profiles across Kubernetes clusters. Data is processed and queried at the edge (in-cluster) via PxL scripts, enabling low-latency distributed tracing, service maps, infrastructure monitoring, and continuous profiling without agent instrumentation or external data pipelines.

Quickstart

Get the pixie source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/pixie-io/pixie.git2cd pixie3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Microservices Debugging & Request Tracing

Instantly visualize full-body HTTP/gRPC/database requests flowing between services, identify latency hotspots, and troubleshoot errors without code changes or log parsing.

Infrastructure & Network Health Monitoring

Monitor pod/node resource usage, TCP retransmits/drops, DNS flows, and network topology in real-time with flame graphs and service dependency maps.

Production Performance Profiling

Continuous application profiling and database query analysis without recompilation; identify CPU/memory bottlenecks and slow queries in running production workloads.

Implementation considerations

Verify kernel eBPF support (Linux 4.14+, ideally 5.8+) across all worker nodes; older kernels may disable auto-telemetry features.
Plan for in-cluster storage overhead; while CPU footprint is low (<5%), storage retention and eviction policies must match observability SLAs.
PxL scripting requires familiarity with Python-like syntax; team training may be needed for custom dashboards and advanced queries.
Evaluate protocol support coverage (HTTP, gRPC, PostgreSQL, MySQL, Redis, etc.) against your application stack; unsupported protocols fall back to network-layer visibility.
Plan RBAC and network policies carefully; Pixie Vizier agents require cluster-wide visibility and may conflict with strict zero-trust policies.

When to avoid it — and what to weigh

Non-Kubernetes Environments — Pixie is tightly coupled to Kubernetes. Traditional VMs, bare metal, or serverless platforms are not supported.
Compliance Requiring External Data Residency — All data is stored and processed in-cluster by design. If regulations mandate data export to external SIEMs or compliance repositories, Pixie alone may not meet requirements.
Requirement for Long-term Historical Analytics — Pixie focuses on real-time and near-real-time queries. Long-term metric storage and historical trend analysis are not primary strengths; consider pairing with external time-series databases.
Minimal Kernel eBPF Support — eBPF requires Linux kernel >= 4.14 (ideally 5.8+). Environments with older kernels or non-Linux OS nodes will not support core telemetry collection.

License & commercial use

Pixie is licensed under Apache License 2.0 (Apache-2.0), an OSI-approved permissive license allowing commercial use, modification, and distribution with attribution and liability disclaimers.

Apache-2.0 permits commercial use, deployment, and modification without royalty or license restrictions. No commercial support or SLA is implied by the license; support is available through community channels (Slack, GitHub Issues) or via a backing organization (Pixie is CNCF-affiliated). Consult with legal if commercial warranty or indemnification is required.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

eBPF programs run in kernel space with elevated privileges to capture network and process telemetry; kernel vulnerabilities could expose cluster. Data is processed in-cluster, reducing exfiltration risk but requiring network policy enforcement. RBAC should restrict Pixie API access. OpenSSF Scorecard badge and CII Best Practices certification suggest security-conscious development. Specific vulnerability disclosure policy, third-party security audit results, or hardening guides are not mentioned in provided data.

Alternatives to consider

Datadog APM + Infrastructure Monitoring

Hosted SaaS observability with broader integrations, longer retention, and built-in alerting; trade-off is agent-based instrumentation, external data residency, and higher cost.

Jaeger (distributed tracing) + Prometheus + Grafana

Open-source, modular stack offering tracing and metrics; lower eBPF overhead but requires more operational glue (log shipping, metric exporters, dashboard management).

New Relic One

Full-stack observability with AI-driven insights and pre-built dashboards; requires instrumentation, external data export, and subscription; better for large enterprises with compliance needs.

Software development agency

Build on pixie with DEV.co software developers

Pixie's eBPF-based observability requires kernel support and cluster-wide permissions. Review kernel compatibility, RBAC policy, and network topology before installation. Evaluate protocol support against your stack and plan retention policies. Contact our team to assess fit and design your deployment.

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pixie FAQ

Do I need to instrument my application code to use Pixie?

No. Pixie uses eBPF to automatically capture telemetry at the kernel and system level without code changes. However, some advanced use cases (custom metrics, dynamic logging) require optional integration.

What happens if my Kubernetes nodes use non-Linux operating systems?

Pixie relies on Linux eBPF. Windows or macOS nodes will not support automatic telemetry collection. Mixed clusters will collect data only from Linux nodes.

Can I export Pixie data to external monitoring systems?

Pixie is in-cluster by design and does not have native exporters to Prometheus, Datadog, or similar platforms. Data export requires custom PxL scripts and API calls; integration is manual.

What is the expected resource footprint in production?

Per the README, Pixie typically uses <5% cluster CPU and in most cases <2%. Storage depends on retention policy and cluster size; specific benchmarks for your workload should be tested.

Custom software development services

Need help beyond evaluating pixie? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source observability integrations — and maintain them long-term.

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