SigNoz simplifies observability for engineering teams. Instead of juggling multiple monitoring tools, get metrics, traces, and logs in a single open-source platform. Built with OpenTelemetry-native architecture and designed for cloud-native environments. Trusted by developers worldwide with 24,000+ GitHub stars and backed by Y Combinator.
External link for SigNoz
San Francisco, CA 94114, US
Bangalore, Karnataka 560001, IN
SigNoz reposted this
New guide from SigNoz. Convex users can send Log Streams to SigNoz using Convex’s webhook destination. SigNoz created a setup guide that covers how to route Convex deployment logs into SigNoz, so teams can debug them alongside traces, metrics, and other application logs. Guide in the first comment.
Trending worldwide on Github 💪
SigNoz is Top 3 trending worldwide on GitHub 🚀 Two shifts happening at once: Agents write more code → more production issues to debug Agents love open source → they can read it, run it, extend it SigNoz sits exactly at that intersection Open source observability, where humans and agents debug production together Is debugging still a human job on your team, or are agents already in your logs?
Edition #38 of Observability Real Talk is LIVE! 📢 This time, we're digging into something hiding in plain sight: more than half the traffic hitting your infrastructure isn't human. And your dashboards? They have no idea. The 2026 Imperva report puts automated traffic at 53%. Cloudflare says 57.5% of HTML requests are now bots. The crossover happened faster than anyone predicted, and it's overwhelmingly AI-shaped. We went through our own traffic at SigNoz, and the findings were… illuminating. A quarter to a third of what our analytics classified as "human" turned out to be bots we couldn't identify at log time. Here's what's covered 👇 🔹 The data: Imperva, Cloudflare, and what SigNoz sees in its own traffic logs. 🔹 The 4am ghost: a bot that quietly logs hundreds of page views every single morning from a plain Linux x86_64 user-agent. 🔹 Why AI crawlers, LLM-powered scrapers, and scraping-as-a-service are driving the inflection, and none of it is malicious. 🔹 The OTel attribute (user_agent.synthetic.type) that almost solves this and why it's not enough. 🔹 Why agentic browsers like Claude in Chrome, Atlas, and Comet are dissolving the line between bot and human entirely 🔹 The uncomfortable truth: "is this a human?" is becoming one of the most important dimensions in your data, and almost nobody treats it as one We're not just losing the traffic count; we're losing the ability to tell the two teams apart on the dashboards we lean on every day to make decisions. If your percentiles, capacity plans, or conversion funnels have ever felt slightly off, this might be part of why. Full breakdown in the latest edition of Observability Real Talk in the comments 👇
You can now monitor your Neon Postgres databases in SigNoz 🚀 Neon's OpenTelemetry integration sends Postgres logs and infrastructure metrics directly to SigNoz. Setup takes a few minutes from the Neon console, with no intermediate collector needed. What you get: • Postgres logs from your Neon computes in the SigNoz Logs Explorer • Database and host metrics like connection counts, DB size, CPU, and memory • Dashboards and alerts on top of your Neon data • Database behavior correlated with the rest of your application telemetry Works with both SigNoz Cloud and self-hosted. Neon also published a step-by-step guide on their site. Find both links in the first comment 👇
$2,000 a month. That was Datadog's quote to Alien Intelligence after a two-week trial, and for a 3-person team. Leo Blondel's reaction says it all (it's the first thing you'll hear 👇). He'd already had his eye on SigNoz: open source, OpenTelemetry-native, and fast to set up. So they switched, then used SigNoz MCP to connect an AI agent to their live telemetry. His take after wiring it together: "MCP's pretty dope, honestly." Full story in the first comment.
SigNoz reposted this
🚨 Our BIGGEST hackathon is here: Agents of SigNoz 🚨 Register here: https://lnkd.in/gAm6b-Kw Why you should participate? > 💻 Apple MacBooks, iPads and iPhones to every team member > 💰 $10,000 in AWS credits > 💸 $100 free AWS credits for every participant > 🧑💻 Job interviews at SigNoz for top winners > 🏎️ LEGO Ferrari SF-24 set worth $250 for best blogs > 🎁 Exclusive swag for top social media posts Build agent-native observability workflows, SRE copilots and AI/LLM dashboards powered by SigNoz and Amazon Web Services (AWS). If you can't observe your AI agents, you don't own them. 🕶️ $20,000 in prizes! 🌏 Complete details on website: https://lnkd.in/gJDDP6Je
SigNoz reposted this
QA is dead. Or at least, the old way of doing it is. AI now writes all your code. Does it still work? That's the gap: coding agents are scaling output exponentially, and bugs and incidents are scaling right alongside it. The best QA are the ones who know everything about your product: every edge case, every piece of business logic. Today, we give that knowledge to Momentic's most capable agent. Every Jira and Linear ticket, one-off threads in Slack, designs in Figma, PRDs in Notion, every PR and commit - Momentic knows all. In just the past few weeks, our agents analyzed over 70,000 test failures and created 600 tests, with a 73% merge rate that's climbing fast. We're grateful for the trust of our customers: some of the world's best engineering teams, including Notion, Xero, Webflow, Retool, Runway, Bilt rely on Momentic’s critical infrastructure every day.
SigNoz reposted this
🤖 I spent this weekend analyzing 20 open-source AI agents for SRE and DevOps. Not by reading blog posts. Not by watching launch videos. Not by trusting GitHub stars. I looked at one thing: What happens when production is on fire? After pulling GitHub data and scoring each project on incident-response capabilities, these are the six I'd actually consider putting near a Kubernetes cluster: 🔹 k8sgpt.ai — Scans your cluster and explains what's broken in plain English. Probably the easiest place to start. https://lnkd.in/dfXddSSh 🔹 @HolmesGPT — Give it an alert and it investigates logs, metrics, and Kubernetes state to identify likely root causes. https://lnkd.in/d_pahT22 🔹 @Keep — Correlates alerts from 100+ tools and helps separate signal from noise. github.com/keephq/keep 🔹 @Robusta — Adds context, runbooks, and troubleshooting data to Prometheus alerts. https://lnkd.in/dzFWvrzF 🔹 kagent — A CNCF Sandbox project for building AI agents that live inside Kubernetes. https://lnkd.in/dx4-fk_p 🔹 SigNoz — Not an agent, but the observability platform many of these workflows depend on. github.com/SigNoz/signoz The biggest takeaway? The tools that impressed me most weren't necessarily the smartest. They were the ones that integrated cleanly with existing observability, monitoring, and alerting stacks. Because during an incident, nobody cares how intelligent your AI is. They care whether it helps reduce the first 20 minutes of confusion. That's where the real value is. I write about AI in Platform Engineering, DevOps, SRE, Kubernetes, and Developer Productivity. 🌐 searchpankaj.com #SRE #DevOps #Kubernetes #AIOps #PlatformEngineering #CloudNative #Observability #OpenSource #AIAgents #DeveloperProductivity
New customer story is live: Kernel x SigNoz. Kernel builds browser infrastructure for AI agents. We spoke with Hiro Tamada, Founding Engineer at Kernel, about how their team uses SigNoz MCP in production. The story covers how Kernel uses agents to query telemetry, inspect traces/logs/metrics, and share analysis in Slack during triage, post-launch checks, and latency investigations. One highlight: they reduced browser acquisition latency from 140ms to 30ms. Full story in first comment.
Alert fatigue is not just a tooling problem. It is a trust problem. When too many alerts are noisy, duplicated, poorly tuned, or unactionable, teams stop believing the alerting system. Then the important signals have to fight for attention. With SigNoz MCP, you can ask your coding agent to help audit alert fatigue. Which alerts fire too often? Which ones overlap? Which thresholds look suspicious? Which alerts point to symptoms but not action? Which ones should be tuned, merged, or retired? This is a practical use case for teams that already have observability in place but want their alerts to become more useful over time. Less noise. More signal. Better on-call. Watch it in action below. Full use case docs in first comment 👇