Operations | Monitoring | ITSM | DevOps | Cloud

Containers and serverless computing are two of the most popular methods for deploying applications. With the rise of microservices and modern DevOps, teams need faster, leaner ways to build and release software. However, selecting the wrong architecture can slow down delivery, increase cloud costs, or lock you into tools that don’t scale with your business. Both methods have their advantages and disadvantages.

Everyone’s obsessed with speed right now. Ship fast. Stack features. Slap an LLM on it and call it v1. Amirite? But in the AI era, where cloud costs can spiral in a weekend, moving fast isn’t enough. The teams that track cloud unit costs from Day 1? They’re the ones who come out ahead. Most teams don’t start there though. They focus on building features and chasing traction, and the cloud bill just shows up like that subscription you forgot to cancel. Maybe someone glances at it.

A few weeks ago, a lead DevOps engineer at a fast-growing SaaS company hit an unexpected wall. “It used to just work… until we scaled,” the lead noted after their Terraform setup began buckling under the weight of a growing cloud footprint. Another chimed in: “We’re spending thousands on infrastructure every week, but we can’t trace it back to who deployed what, or why.” Sound familiar? You’re not alone.

“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.