Operations | Monitoring | ITSM | DevOps | Cloud

I'm happy to share some really exiting updates from the Flyway product teams this past month.

Seven years ago, our customers started telling the world what the ScienceLogic AI Platform does for their operations. They haven’t stopped. For the seventh consecutive year, that steady stream of verified customer reviews has earned the ScienceLogic AI Platform a TrustRadius Top Rated award, again. Seven years in a row shows that customers keep choosing to share their experience because the platform keeps delivering value. This recognition doesn’t come from us.

For Platform Engineering teams, the goal has always been clear: build a secure, scalable internal developer platform that reduces cognitive load and accelerates time-to-market. Yet, a massive obstacle often remains hidden in plain sight: the mainframe. While your distributed teams are shipping cloud-native microservices multiple times a day, your core backend mainframe applications frequently remain locked in an isolated silo, lagging behind on slow monthly or quarterly cadences.

Your Dynamic Host Configuration Protocol (DHCP) server already knows what’s connected to your network. The problem is that DHCP data rarely stays aligned with the rest of your infrastructure systems. Instead, it becomes fragmented across Windows servers, branch offices, spreadsheets, and disconnected operational tools. Lease data ages, assignments go untracked, and teams lose confidence in their network inventory.

During the Toronto KCD (Kubernetes Community Days), I attended an insightful talk on AI resource optimization that highlighted a staggering Gartner study: “AI infrastructure is adding $401 billion in new spending this year alone. Yet, real-world audits tell a much darker story, revealing that average GPU utilization in the enterprise is stuck at a dismal 5%”. While many people in the audience were shocked by that number, the data didn’t come as a surprise to us.

You’ve deployed Karpenter or Cluster Autoscaler and tightened your resource requests, but while you saw an initial dip in your cloud bill, your savings have flatlined. Organizations that thought they had the fundamentals of cloud cost under control are now seeing stagnation. The problem isn’t that they need another FinOps tool or better visibility. The problem is that the current state of enterprise cloud cost optimization strategy is fundamentally reactive.

AI SRE Platform’s new capabilities resolve structural blockers and optimize workload placement, eliminating cloud waste to drive up to 80% in total cost savings.

Prometheus is a wildly popular open source monitoring tool typically used for monitoring Kubernetes environments and containerized workloads. But how do you turn the mountains of metrics into a clear picture of health and performance? SquaredUp plugs directly into your Prometheus database to visualize and monitor your data. What sets SquaredUp apart from other Prometheus visualization options like Grafana and Perseus is just how easy it is to visualize, monitor and share Prometheus dashboards.

MSPs operate in high-pressure, customer-first environments where understanding exactly what is happening across client environments is the difference between proactive service and constant firefighting. Yet, many still struggle to get a unified view across their customers, tools, and teams.

Production secrets should live in one place and stay there, whether your next deployment is triggered by a developer or an AI agent. The Secret Manager integration connects AWS Secrets Manager, AWS SSM, or GCP Secret Manager to Qovery so secrets are referenced, never copied, and enterprise governance holds regardless of who deploys. Alessandro leads product at Qovery. He drives the changelog, roadmap, and product strategy - turning customer feedback into platform capabilities.