Operations | Monitoring | ITSM | DevOps | Cloud

If you’re struggling to contain cloud costs in this suddenly volatile AI-fixated environment, it might be time to consider FinOps as an exercise in granular allocation and unit economics, with a focus on outcome.

Cloud cost management is a big deal right now. For instance, 58% of organizations say their cloud costs are too high, according to our State of Cloud Cost report. Over the last five years, several other studies have shown that controlling cloud spend is a top cloud computing challenge. There’s more. As AI adoption accelerates, a new challenge has emerged: managing the rapidly growing costs of AI in a scalable and intelligent way.

Fall is here again! These headlines might not be as good as a maple-sugar doughnut and a pumpkin-spiced coffee, but they’re excellent news for FinOps practitioners nonetheless (and they won’t put you into a sugar coma).

We’ve seen the same story play out time and time again in numerous SaaS companies: The problem often begins when engineers — with a technical understanding of cloud costs and a deep understanding of how to build robust products — struggle to communicate the actual business impact of their efforts to company leaders.

Finance and engineering both thrive on efficiency. So when companies realize they’re wasting cloud spend, but aren’t sure where or how, both teams become frustrated. It’s remarkable how common this scenario has become, particularly over the past five years. Before the pandemic, Gartner reported that companies wasted over $14 billion on cloud services. During the cloud adoption surge in 2020, IDG found that 70% of US businesses overspent by as much as 62%.

Amazon Athena is heralded for its simplicity, pay-per-query pricing, and speed. Others say Athena has its fair share of limitations, which could affect your overall analytics experience and quality of analyses. So, what is what? In this post, we’ll share what AWS Athena does, compare it to Amazon Redshift, and share how to understand and control your Athena costs in AWS (with just one tool).

If your SaaS organization is experimenting with OpenAI, your cloud bill just got a new line item. And unless you know exactly what drives it, that line item can go from manageable to margin-killer, fast. It’s also worth clarifying that OpenAI is not the same as ChatGPT. ChatGPT is the familiar end-user app with a flat monthly subscription. OpenAI, meanwhile, is the platform behind it — a mix of models, features, and usage-based pricing that shifts depending on what you build.

The most challenging question in AI today isn’t how to build with it. It’s whether you can prove it’s worth what you’re spending on it. Every week, I hear the same thing from engineering and finance leaders: “We know the AI bills are big.

The cloud, as we know it today, was created as recently as 2006. For most of its lifespan since then, companies have been throwing money at cloud services with abandon. The competitive edge gained by having the newest, best, and most powerful tools at their disposal made it worthwhile for companies to spend ever-increasing amounts without too much worry.

If your team is experimenting with Google’s latest Gemini models, you’ve probably noticed the pricing can get murky. And like any cloud service, usage-based billing means those features can quickly rack up your SaaS costs. Free tiers fade fast. Token usage gets fuzzy. And before long, your GenAI bill feels more like a guessing game than a budget line item.