Operations | Monitoring | ITSM | DevOps | Cloud

Want to know how to get a job in FinOps? You’re not alone. FinOps careers are rapidly emerging as essential roles in tech, helping companies manage cloud costs without slowing down innovation. These roles sit at the intersection of finance, engineering, and cloud operations. FinOps roles and responsibilities are expanding fast. In this guide, you’ll learn what FinOps professionals do, how to frame your skills for the job, what certifications help, and how to grow your FinOps career over time.

Picture this. You recently shipped a new feature, and things were working smoothly — until they didn’t. Now, one service is timing out. Another is overloaded. You dig in and realize the issue is with how your systems communicate. Messages are not arriving when or where they should. Your team had set up Amazon SNS for notifications and Amazon SQS for processing tasks. But somewhere along the way, the difference between SQS vs. SNS (and how they’re wired together) got lost in translation.

When rideshare drivers talk about a “side hustle”, they mean working a few hours on weekends to make extra cash. That’s fine for pocket money, but it’s catastrophic when the “hustle” is controlling your cloud and AI spend. Right now, too many companies run FinOps the way they run the office coffee pot: A volunteer refills it when things look empty.

The finance team says gross margins are slipping. Engineering says costs are under control. Then you get THE question.

The year is 2025, and I’ve been watching teams discover what happens when you give developers AI superpowers without giving them AI super-governance. It’s like the merchandising scene from Spaceballs: “Vibe Coding: The Flamethrower. The kids love this one.” But here’s the thing: I’m not here to take away the flamethrowers. I’m here to hand out fire extinguishers and maybe suggest we practice in a safe room instead of the living room.

Many teams buy RIs with the best of intentions (predictability and up to 72% savings) only to realize later that they’ve either overcommitted or left money on the table. Without clear visibility, what starts as a smart cost-saving move can slither into silent waste. This guide will help you get ahead of that. We’ll walk you through the ins and outs of Azure Reserved Instances, compare them to other savings options, and share best practices to help you avoid common pitfalls.

You know you’re bleeding money in the cloud. Maybe not everywhere, but enough to feel it. Your engineers know it too. You’ve got idle resources humming away, AI workloads scaling like wildfire, and nobody can quite explain why last month’s bill jumped by 17%. So, you bring up the idea of investing in a cloud cost optimization product. Cue the skeptical glance from your CFO.

FinOps is booming as a function. The global cloud FinOps market will grow from $13.5 billion in 2024 to $23.3 billion in 2029 — a compounded annual growth rate (CAGR) of 11.4%, according to Research and Markets. That’s in response to sharp increases in cloud spend. About $723 billion is expected to be spent on public cloud services in 2025, up from $596 billion the year before according to a Gartner report.

In 2008, Nuance hired me to join their Healthcare Speech Recognition team as a “Release Engineer.” DevOps wasn’t a thing yet — Patrick Debois and Andrew Shafer wouldn’t hold their first “DevOpsDays” until 2009. But I was lucky that “Release Engineer” at Nuance meant “jack of all trades” who wrote Makefiles, bash scripts, Perl, and Java to build and release code to a fleet of hundreds of on-premise Linux machines.

Cloud-native tools promise speed, scalability, and resilience. The catch is you have to pick the right ones and use them well. Without the right foundation, they can mean more complexity, hidden costs, and a false sense of control. In this guide, we’ll help you avoid that trap. From infrastructure to observability and CI/CD tools, we’ll cover the solutions shaping modern cloud stacks.

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.

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.

Author Jeff Duntemann said a good tool improves how you work, whereas a great tool transforms your thinking. Companies that want to improve their cloud-based operations can rely on cloud metrics as an effective tool for transforming their cloud operations. You can’t fix what you don’t measure. Cloud metrics are the logs of data that a cloud infrastructure or application generates.

Google Cloud released Google Kubernetes Engine (GKE) as a commercial version of native Kubernetes (K8s). GKE promises a user-friendly, reliable, and cost-effective service. Yet calculating GKE costs can be daunting, including understanding what you’re paying for and maximizing your return on investment. In this GKE pricing guide, we’ll discuss how GKE pricing works, what it costs, and more.