Operations | Monitoring | ITSM | DevOps | Cloud

We're thrilled to announce that we've added Cherry Servers, a European infrastructure provider, as another native integration to the Cycle platform. With this integration, Cycle clients have more choice in where, and how, they deploy their applications as Cherry offers a variety of bare metal server configurations across 7 data centers located around the globe. For a more formal announcement, please see our joint press release with Cherry Servers.

Infrastructure automation tools by layer: provisioning (Terraform, OpenTofu, Pulumi, CDK, Crossplane) vs. orchestration (Spacelift, env0, Scalr, Qovery). Melanie leads content at Qovery. She covers platform engineering trends, Kubernetes operations, FinOps, and the tools that help engineering teams ship faster.

The new Mobot is a conversational AI assistant that helps teams explore and understand their data using natural language. We’re now extending those same capabilities to one of the most time-consuming tasks in observability: creating and configuring monitors.

Full-Pipeline Blueprints are the headline, and Scoped API Keys went to preview. Here is the rundown.

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.