Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

Sizing infrastructure has always required a balance between performance, capacity, and risk. What has changed is the level of precision required to make those decisions. Hardware timelines are less predictable. Costs are under closer review. Decisions that were once routine now require clear justification. In many cases, the question is no longer just how much capacity is needed, but whether that capacity can be delivered when it is needed and whether the investment will hold up under scrutiny.

Your engineers run Claude Code. Your designers are in Cowork. Half the company has Claude open in a browser tab, and a few are on Cursor. It’s on their laptops, each person authenticated a different way, and none of it touches your gateway. The only record you get is one lump-sum bill at the end of the month. Now you can capture it where it happens – on the laptop.

Something unusual happened in the first half of 2026: the most productive AI coding tool on the market became the most financially dangerous. And the companies that discovered this the hard way read like a Fortune 50 roll call.

You’re scaling your IT infrastructure so you can do more — deploying across clouds and data center, adding servers, coding like crazy. Great! But how do you keep it all from falling apart? Policy as code is an approach to managing IT that strategically leverages infrastructure as code (IaC) and compliance as code to manage consistent policies across complex IT environments. Sounds perfect, right?

IPoDWDM is an architecture that integrates IP routing and Dense Wavelength Division Multiplexing into a single, converged platform. This integration is achieved by placing coherent optics directly into the ports of IP routers and switches, a fundamental shift from traditional network designs. Consequently, this approach eliminates the need for a separate, dedicated layer of optical transponders and their associated shelving.