Operations | Monitoring | ITSM | DevOps | Cloud

AI adoption continues to explode, and so do their costs. By mid-2025, enterprise LLM spend had already hit $8.4 billion, more than double the year before. And in a major shift, Anthropic recently overtook OpenAI as the enterprise leader. Their Claude models are now core tools for companies adding generative AI technology into their products and workflows. CloudZero recently announced we are the first cloud cost platform to integrate with Anthropic.

The popular narrative around AI economics is changing. At one time, Moore’s Law conditioned us to expect that smarter, faster computing would steadily get cheaper. When it comes to AI, that expectation holds true at the unit level. Per-token costs are indeed declining. But the number of tokens consumed per task is growing exponentially, making total costs spike. The tension here is important: on paper, inference is getting cheaper.

Managing containerized applications or microservices can be difficult. It is even more demanding and prone to error if you do it manually. So, what’s the alternative? Container orchestration. Container orchestration is an automation technology that enables engineers to coordinate when containers start and stop, schedule and execute tasks, manage failovers, and perform recovery processes. The technology helps automate these tasks throughout a container’s lifecycle.

Snowflake’s scalable architecture, minimal latency, advanced analytics, simplified data handling, flexible pay-as-you-go model, and always-on security make the data cloud a top choice for many businesses. You can also purchase Snowflake resources on demand or upfront. But if you struggle to control your Snowflake costs, you’re not alone. With the help of this guide, you’ll know how to manage your Snowflake costs better.

Kubernetes, also known as K8s, is an open-source, portable, and scalable container orchestration platform. With K8s, you can reliably manage distributed systems for your applications, enabling declarative configuration and automatic deployment. Yet, K8s can be resource-intensive and costly, with a rather steep learning curve. But in 2019, a lighter, faster, and potentially more cost-effective alternative appeared: K3s. Still, K3s is not a magic wand that works for all Kubernetes deployments.

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%.