Operations | Monitoring | ITSM | DevOps | Cloud

In FinOps, rightsizing means adjusting cloud resources (instance types, number of CPUs, amount of memory, storage, databases, containers, and many other configuration parameters) to match actual workload requirements. It’s one of the most powerful levers in the FinOps toolbox, and for good reason. Consider: Average CPU utilization across Kubernetes clusters sits at just 10%, according to Cast AI’s 2025 Kubernetes Cost Benchmark Report.

You wouldn’t buy an industrial dumpster for your kitchen, and you don’t need an EC2 T3 to do an M5d’s job.

All the major cloud providers enable users to attach business context to their infrastructure in some way. This process — known as cloud cost allocation — is how companies map spend to the teams, products, or features driving it. Done well, cost allocation fuels smarter business decisions. It connects cloud bills to business value, helping teams not just control spend but also understand unit economics and margins.

AI is reshaping the cost landscape. As a positive person, I’m going to call this change exciting! FinOps teams are integrating AI into cloud platforms and incurring the spend that comes with it. As a FinOps strategist who has helped several companies optimize cloud spend across industries, it became evident that clarity around AI spend unlocks swift, smart decisions. That’s AI … optimized.

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.

Experience seamless control, clarity, and cost optimization with the CloudSpend app on iOS 26. This update integrates Apple’s new Liquid Glass design and secure, on-device AI summaries to deliver instant insights into your cloud spending, empowering you to act decisively from anywhere.

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.