Operations | Monitoring | ITSM | DevOps | Cloud

When the changelog doesn’t keep pace with the product, two things can happen. One, you keep working around something that was already fixed weeks ago. Or two, a behavior changes, you assume it’s a bug, and you spend an afternoon on triage and a support ticket before learning it was an intentional improvement. CloudZero now ships around 30 improvements a week, a pace driven by the Next Gen Platform and the AI-first approach we’re building for our customers.

AI costs are changing. As noted by research from EY, outputs that cost just $0.04 in 2023 now cost $1.20, a 30x increase over just three years. It’s worth noting that task operations and complexity have also changed. In 2023, the process was simple. Users input a question, retrieval engines found relevant data, and AI models returned a response. Today, many tasks are handled by orchestrated AI agents capable of much more complex reasoning and analysis.

What it takes to prove AI value with LLM observability Over six months, the Elastic IT team ran internal AI applications that returned $2.5 million in operational time to the business.1 A conversational support assistant moved us from zero digital resolution, where anything complex became a ticket, to 30% of support interactions closing without one.

You wrote the feature. It works locally. Then you spend the next two hours on things that have nothing to do with the feature: a Terraform plan that wants to replace a database you didn't touch, a Kubernetes manifest that needs a new ingress rule, an IAM policy that's one permission short of what the deploy needs. None of this is the job. All of it is the job today. Here's what that list actually looks like, and why none of it should be sitting on your plate.

When you choose a cloud platform, you're entrusting a provider with sensitive business information, customer data, critical applications, and a growing share of your operational resilience. Increasingly, you are also entrusting it with AI. And that changes the questions you should be asking. Marketing claims cannot answer these questions. Independent evidence can. Here is what that evidence looks like at Upsun and why it matters to your next supplier review.

Vulnerabilities used to move at human speed. A researcher found one, disclosed it, and defenders had days - sometimes weeks - to respond before it was weaponized in the wild. That window is gone. According to the Edgescan 2026 Vulnerability Statistics Report, it still takes an average of 55 days to fix a vulnerability - but the Zero Day Clock shows attackers going from disclosure to first exploit in as little as 6 hours.

Summary Downsampling turns high-frequency time series data into lower-resolution summaries. In InfluxDB 3, you can calculate those summaries by querying with SQL or materialize them on a schedule with the Python Processing Engine. Table of Contents This tutorial demonstrates both approaches using the InfluxDB 3 Processing Engine’s built-in bird tracking simulator plugin. You will generate telemetry, aggregate it into 10-second windows, and validate the result with SQL.

Power Usage Effectiveness has long been used as a metric to calculate data center efficiency. For roughly the last two decades, this popular metric has been used to manage data centers throughout the world. In Singapore, arguably one of the leading data center hubs in Southeast Asia with 1.4 gigawatts capacity, future PUE limits are likely to force operators of both new and existing data centers to modify their facilities and how they are managed.

With AI agents and AI-generated code becoming the norm in modern enterprise software, backend systems are evolving faster than ever. And it’s leaving most operations teams with an impossible choice: burn out senior talent on repetitive firefighting, or hand production over to untrained AI agents. With disruptions costing enterprises an average of $300,000 per hour, manual firefighting isn’t an option.

Say you get a support escalation about a page in the app that won’t load. But when you pull up your synthetic checks, they're all green: 100% uptime, probes are passing. Something's not adding up, but which one do you trust? If you’ve run Grafana Cloud Synthetic Monitoring, you’ve been on both sides of this. Sometimes it's the ticket: real users hit a wall on the path but your checks pass cleanly. Other times, it’s the inverse.