Shipped: Your Explorer setup, remembered
CloudZero Explorer now tracks your query history and lets you save up to 12 configurations and 10 favorite dimensions, so your most-used cost analyses are one click away.
Operations | Monitoring | ITSM | DevOps | Cloud
The Place Where Modern Operations & Technology Come Together
CloudZero Dimension Studio: A drag-and-drop UI at the foundation of AI ROI
The core of ROI is visibility. If you can clearly see … 1. What it costs to produce the thing you make, and 2. How much money it makes you … then calculating ROI is easy. But with AI, as with the cloud before it, getting that visibility is extremely challenging. Why? Because the cost data associated with each is inherently chaotic.
The Four Pillars of AI Observability in 90 Seconds
AI applications can behave unpredictably, potentially leading to errors such as hallucinations or data leaks, even when classic monitoring indicates a successful response. To effectively monitor AI systems, four key areas should be focused on. Implementing these pillars can enhance trust in AI deployments, help manage costs, and identify safety issues before they impact users.
What's New in Grafana 13.1: Git Sync - Easier Importants, Easier READMEs & Signing
Grafana 13.1 cuts down on GitOps pain by making Git Sync stronger — import dashboards to Git with a click, see your repo READMEs inside Grafana, and sign every commit for security-strict teams.
How Grafana Cloud Ingests Your Data | Data Sources, Alloy & OTel Explained
Learn the two main ways to get data into Grafana Cloud. In this video, we break down how Grafana Cloud connects to over 150 external data sources (like Salesforce, Postgres, and CloudWatch) where your data stays in place, and how you can send raw telemetry into Grafana’s fully managed databases for logs, metrics, traces, and profiles.
Why you should use Language Server Protocol (LSP) with Claude Code
Agentic coding tools like Claude Code can write, refactor, and debug across an entire codebase, but by default they read code as plain text, the way grep does. The Language Server Protocol (LSP) changes that: it’s the same code-intelligence layer an IDE uses, and wiring it into an agent lets it read code by meaning instead of by string match. The bigger the codebase, the more a wrong guess about a symbol costs, and the more that structural view pays off.
Network Monitoring, the Netdata Way: Topology, NetFlow, SNMP, and Traps
Interface counters tell you a port is busy. Bytes in, bytes out, errors, drops. That’s enough to know a link is saturated, but not enough to know which conversations are saturating it, which devices are involved, or how a problem propagates across your network. For that you’ve traditionally needed dedicated network performance monitoring tools, usually expensive, usually a separate console from the rest of your monitoring.
How Git Worktrees Changed My Development Workflow
Since I started using Claude Code more frequently, I kept noticing a “worktree” checkbox popping up whenever I started a session in a Git repository. I had no idea what it meant, so I did what any curious developer would do and started digging. What I found was a Git feature I somehow never came across before: git worktrees.
Sentry 201: Making Sentry More Useful
This 3-part workshop series covers everything already included in your Sentry subscription and how to actually use it.
Multi-Agent Architectures - What we shipped, what broke, and what we'd do differently
At LLMday Lisbon, our Software Engineer, Viktor Vasylkovskyi, highlights the realities of building production AI agents with LangGraph - sometimes getting it right, often learning the hard way. This talk is about what was actually shipped, including a distributed multi-agent setup at PagerDuty. Viktor breaks down the real tradeoffs between LLM-driven and deterministic orchestration, what broke, and how he’d approach it differently now.