Alaz: Open-Source, Self-Hosted, eBPF-Based K8s Monitoring
Hello Everyone, I'm excited to introduce a new open-source observability platform and would love to hear your feedback. We are aware that there are lots of open-source/commercial tools out there. However, we believe that monitoring the clusters and extracting actionable insights requires deep know-how about the tools/domain. We mainly focused on this problem. - Alaz is an eBPF agent installed on your K8s cluster as DaemonSet. Thanks to eBPF, Alaz collects traces directly from Linux kernels. This means there's no need for sidecars, instrumentations, or service restarts. - The UI not…
In plain words
Alaz is an open-source, self-hosted Kubernetes monitoring platform that uses eBPF technology to collect observability data directly from Linux kernels without requiring sidecars or service instrumentation. Installed as a DaemonSet on K8s clusters, it provides a UI that visualizes traces and delivers actionable insights through service maps, showing latencies, request rates, and performance issues like slow queries and zombie services. It targets DevOps teams and cluster operators seeking deep cluster visibility and easier troubleshooting.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hello Everyone, I'm excited to introduce a new open-source observability platform and would love to hear your feedback. We are aware that there are lots of open-source/commercial tools out there. However, we believe that monitoring the clusters and extracting actionable insights requires deep know-how about the tools/domain. We mainly focused on this problem. - Alaz is an eBPF agent installed on your K8s cluster as DaemonSet. Thanks to eBPF, Alaz collects traces directly from Linux kernels. This means there's no need for sidecars, instrumentations, or service restarts. - The UI not only visualizes data but also provides actionable insights. Using the Service Map, you can: - View latencies and RPS between services. - Detect zombie services and underperforming SQL queries. - Monitor golden signals, such as 5xx status codes. In addition, Alaz can capture system resources like CPU, Memory, Disk, and Network through the Prometheus Node Exporter, which is embedded in the agent. Setting up is straightforward: just install Alaz as a DaemonSet, and the platform will handle the rest. Finally, the combination of Alaz and Ddosify Performance Testing makes it possible to do load testing and simultaneously monitor the system to find bottlenecks instantly. For those interested, check out Alaz on GitHub: https://github.com/ddosify/alaz Your feedback would be greatly appreciated!
Does the same job
all alternatives →- CECoroot – eBPF-based, open source observability with actionable insights2025 · github.com · ▲162
A common open source approach to observability will begin with databases and visualizations for telemetry - Grafana, Prometheus, Jaeger. But observability doesn’t begin and end here: these tools require configuration, dashboard customization, and may not actually pinpoint the data you need to mitigate system risks. Coroot was designed to solve the problem of manual, time-consuming observability analysis: it handles the full observability journey — from collecting telemetry to turning it into actionable insights. We also strongly believe that simple observability should be an innovation…
- PIPixie, Instant Kubernetes-Native Application Observability Using EBPF2020 · pixielabs.ai · ▲108
- WYWrite your BPF programs in Go, not CMay 2026 · github.com · ▲112
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
