Operations | Monitoring | ITSM | DevOps | Cloud

The AI sidekick called Dossinth I'm building is starting to take shape. So far, it became a moderately useful CRM, with a nice dashboard, and event log, daily briefing (which I am also receiving by email), account management and knowledge base. It's less AI and more CRM. For now. Here's a quick tour...

TLDR: Runtime Context is live, execution-level access to a running production system. It lets engineers and AI agents ask precise questions of running code and get answers immediately, without redeploying or interrupting users. This is the new baseline for reliability.

Earlier this year we launched Grafana Cloud Fleet Management to address the pain that comes with managing scores of telemetry collectors across departments and environments. We've been excited to see how organizations are using it to manage collectors at scale, but we've also heard from users who aren't sure how Fleet Management fits with their existing infrastructure-as-code tooling. The good news is Fleet Management is designed specifically to complement—not replace—tools like Terraform.

Cost-Intelligent Observability shifts your organization into a proactive and collaborative cloud cost management powerhouse.

The things that separate an MVP from a production-ready app are polish, final touches, and the Pareto ‘last 20%’ of work. Many of the bugs, edge cases, and performance issues will come to the surface after you launch, when the user stampede puts a serious strain on your application. If you’re reading this, you’re probably sitting on the 80% mark, ready to tackle the rest.

Data systems rarely operate as isolated components. Machines depend on sensors, services rely on other services, and devices exchange data through shared gateways. When something changes, the impact often spreads beyond a single metric. To trace how changes move through complex systems, many teams turn to graph-style analysis to map dependencies and follow cause and effect.

You ran Lighthouse and got flagged for a slow First Contentful Paint. The score is orange (or worse, red), and the help text says something about “the time at which the first text or image is painted.” But what does that actually mean for your users? And why should you care?

If you are into IT operations or leadership, you likely spent at least one weekend in 2025 huddled over a laptop while the rest of the world slept. For the last decade, our industry has pursued five nines (99.999% uptime) as the holy grail. We architected redundant systems, deployed across multiple availability zones, and optimized our code until it hummed. We convinced ourselves that if we just engineered hard enough, we could tame the chaos of the internet. We thought we could. We really did.

In a recent post, I argued that AI doesn’t fix weak engineering processes; rather it amplifies them. Strong review practices, clear ownership, and solid fundamentals still matter just as much when code is AI-assisted as when it’s not. That post sparked a follow-up question in the comments that’s worth sitting with: With AI speeding things up, how do teams realise something’s gone wrong before users do? It’s the right question to ask next.

This is an updated and expanded version of the older guide. According to the 2025 State of SaaS report, organizations use an average of 106 SaaS apps. Staying on top of your SaaS vendors' status is as important as monitoring your own services. The Cloudflare, AWS, Azure, and Google Cloud outages in 2025 were strong reminders of this fact.