Operations | Monitoring | ITSM | DevOps | Cloud

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.

Your AI calls already emit OpenTelemetry: your LLM gateway exports it, and it’s the open standard your own services can speak. But you don’t have anywhere to turn those spans into spend you can allocate to an outcome. Now you can. CloudZero exposes an OpenTelemetry endpoint that doesn’t care what’s on the other end.

Getting InfluxDB 3 up and running is a pretty lightweight process with the installation script. Getting time series data into it is the next step, and for exploration, basic testing, or scenarios where you don’t have a stream of time series data ready to write, that can be a point of friction. That hurdle is particularly high when you want to test the rest of the system around the data you’d be writing.

When a major IT incident hits, most organizations know what it costs in the moment: lost transactions and missed SLAs. But according to the findings of our 2026 State of AI-First Operations report, the most significant consequences often don’t show up until long after the incident is closed—in customer relationships, team health, and brand reputation.