Operations | Monitoring | ITSM | DevOps | Cloud

“It started as an experiment.” That’s how it begins at most companies. A small team spins up a few GPU instances to train a proof-of-concept model. Maybe it’s a fraud detection algorithm. Maybe it’s GenAI for support tickets. Either way, it’s just a test. Then the results come in, and they’re promising. Suddenly, that model is powering new features. Teams are fine-tuning LLMs in parallel.

Vertical Pod Autoscaling (VPA) is a component within Kubernetes designed to automatically resize the CPU and memory requests of pods based on their observed, historical usage patterns. While Pepperdata Capacity Optimizer and VPA both change the resource requests of pods in response to changing application resource requirements, there are several key differences.

Earlier this year, OpenAI’s CEO Sam Altman admitted something that should make every engineering leader pause: they’re “currently losing money” on ChatGPT Pro subscriptions, which run $200 per month. Let that sink in. A company charging two hundred dollars a month for AI access — 10 times what most SaaS products dare to ask — is still bleeding cash on every user. This isn’t a pricing problem. It’s a physics problem.

The Kubernetes platform is the standard for orchestrating containerized applications. It’s ideal for large applications running on distributed instances. However, monitoring Kubernetes infrastructure can be notoriously challenging. This guide will cover Kubernetes monitoring in more detail, including what metrics to track to improve visibility and control over your K8s containers, apps, microservices, etc.

You’re probably not combing through cloud bills. That’s not your job as CEO. But if no one on your executive team can tell you what it costs to serve a customer, ship a feature, or launch a new product line, that’s a problem. Not a someday problem. A right-now, quietly-draining-your-margins kind of problem. FinOps tends to get lumped in with cost-cutting — some finance thing, some DevOps thing. But that framing misses the point. Done right, FinOps is a growth enabler.

Picture this. Gartner forecasts worldwide end-user spending for public cloud usage to total more than $720 billion in 2025 — up from $595 billion in 2024. Out of that spend, SaaS will make up a chunky $299 billion. For comparison, Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) will make up nearly $212 billion and $209 billion, respectively. Elsewhere, BetterCloud’s State of SaaS 2025 report found that the average organization uses 106 different SaaS tools.

If you’ve ever deployed a cloud application and wondered why some workloads seem faster, more resilient, or more expensive than others, the answer often lies in how you’ve used availability zones. In this guide, we’ll break down how GCP availability zones work, why they matter, and how to use them strategically to balance availability, compliance, and Google Cloud costs.

Remember when we thought deploying from our laptops was efficient? When FTPing files directly to production at 2 AM felt like peak productivity? We’ve been here before. As AI transforms how we write code, we’re about to learn the same lesson all over again — but this time with much bigger bills.

Kubernetes orchestration isn’t getting any simpler. Today, teams are pushing into AI/ML, edge computing, and multi-cloud automation. And with that, you may be looking beyond Rancher. This guide walks you through today’s top Rancher alternatives, from enterprise-grade platforms like OpenShift to leaner developer-first tools like Lens and Portainer. With this intel, you can then decide which one fits your evolving stack, budget, and business goals.

In this blog series we’ve examined Five Myths of Apache Spark Optimization. But one final, bonus myth remains unaddressed: I’ve done everything I can. The overprovisioned resources that lead to underutilized CPU and memory in my workloads is just the cost of doing business.