Operations | Monitoring | ITSM | DevOps | Cloud

Sentry’s job is to tell you when your app breaks. To do that, we group individual errors into issues. First by fingerprinting, which lexically matches errors based on their structure, then by an AI fallback: when fingerprinting can’t find a match, an ML model compares the new error’s stacktrace against existing issues and merges it if they’re semantically similar.

If anyone knows how to operate under scrutiny, it’s database teams within finance organizations. It’s a given considering the more rigorous compliance requirements and processes they must follow. But the 2026 State of the Database Landscape: Finance Edition reveals something more specific, and more uncomfortable, than the familiar story of regulatory pressure.

It's 7am. An alert fired overnight. You open your monitoring solution, navigate to the alert, cross-reference the waits, check the query plans. Twenty minutes later: it should not have fired. You knew that before you started, but you had to check anyways. The feeling of being overwhelmed by alerts is real. And so is the cost. Thresholds set once and forgotten, firing on patterns that have been normal for months. The inbox fills. DBAs learn to ignore most alerts. The workaround becomes the workflow.

We rebuilt large parts of the Qovery Console: new navigation, overviews at every level, dark mode, and a modernized frontend architecture with TanStack Router and React Suspense. Rémi is a staff frontend engineer at Qovery. He writes about frontend architecture, developer experience, and building scalable UI systems for platform engineering tools. Théo is a senior product designer at Qovery.

When incidents are not addressed – or not addressed quickly enough – businesses incur significant costs. Mean Time to Resolution (MTTR) increases. In the worst cases, the financial impact extends beyond your organization to customers and partners. Automated alerting reduces response times and notifies the right people when action is needed.

Many teams invest time building an on-call rotation, but inbound calls often ignore that structure completely. A support number forwards to a single phone. One engineer ends up taking every call. Sometimes the call goes unanswered and the voicemail lands in a shared mailbox that nobody checks until the next morning. Even worse, the team might have several engineers on duty, but the phone system has no awareness of who is actually responsible at that moment.

The 2am war room hasn’t gone anywhere. Ten years after Gartner coined the term AIOps, the platforms are bought, the licenses are renewed, the dashboards are live — and serious incidents still get resolved by engineers paging across multiple consoles, trying to work out where the fire actually is. MTTR has barely moved. Alert fatigue hasn’t eased. The outcomes the category promised, in most enterprises, have not arrived. Matt Lowe’s recent article on AIOps names the shortfall well.

GPU utilization is one of the most expensive metrics in cloud infrastructure to get wrong. A GPU running at 30% utilization costs the same as one running at 90%, but it's doing a third of the useful work. For workloads measured in tens of thousands of GPU-hours, the difference between average utilization in the 30s and average utilization in the 70s is hundreds of thousands of dollars across the life of the workload.

Most network teams only find out their firewall is overloaded after users start complaining. A slow VPN, dropped calls, and random packet loss at 2 pm every day. The usual suspects get blamed first: the ISP, the switch, the application server. The firewall gets a pass because the dashboard says 40% CPU and everything looks fine. Here is the problem with that picture. Standard SNMP monitoring polls every 5 minutes. A CPU spike that peaks at 95% and recovers within 90 seconds never shows up.

Agentic workflows are reshaping how engineering teams operate, fetching context, synthesizing decisions, and shipping results across systems without human intervention. But the same design that makes them powerful adds risk in production. Agents do not crash when they hit bad data; they synthesize around it, substituting a stale value, an empty page, or a missing field for the result they were supposed to capture.