Operations | Monitoring | ITSM | DevOps | Cloud

Cloud native cost optimization requires rethinking how you track containers, serverless, and microservices spend. Here's a framework for 2026.

CloudZero's new AWS Kiro integration puts cost intelligence directly in your agentic IDE. Ask plain-language questions about spend, attribution, and cost-per-serve without leaving your development workflow. We see a similar pattern playing out across engineering teams running agentic development tools: code gets shipped fast, something moves in the cost data, and understanding why still requires leaving your environment entirely.

Notes from a finance leader whose job this is. A few weeks ago, I traveled to Philadelphia for a conversation with a prospective CloudZero customer. We’d been working with the prospect’s engineering team for some weeks, demoing our platform in view of the RFP they’d drawn up. This stage had gone well, and so the next step was talking it over with the prospect’s CFO. We expected a conversation centered around the key criteria in the RFP.

Your LLM observability stack probably handles individual model calls well enough. Latency, token counts, error rates, maybe even evaluation scores....

You probably have Datadog. Maybe New Relic, maybe Dynatrace. Your observability stack has been solid for years — and you're still flying blind on AI cost. Here's why LLM observability needs a fourth pillar most tools skip, and how to build one that actually tells you what your models are costing you per request, per feature, per customer.

Meta's internal token leaderboard sparked a frenzy — and a reckoning. Tokenmaxxing without attribution is just cloud waste 2.0. Companies like Hudl and Duolingo use cost intelligence to connect every AI dollar to a business outcome.

Make AI do the dirty work while you focus your energy on strategy. CloudZero's Ryland Bowles shows you how. Every FinOps engineer is worried that AI is going to steal their job. I’ve worried about it. But I’ve also experimented extensively with AI, and I’ve got a pretty clear sense of what it can and can’t do in a FinOps context.

Claude Opus 4.7 holds at $5/$25 per million tokens — but a new tokenizer inflates costs up to 35% on identical text. Here's what Opus 4.7 actually costs at production scale, how it compares to Sonnet 4.6, and the six levers that determine where your bill lands.

Cloud rightsizing matches resources to actual workload demand. Learn to find overprovisioned instances, cut waste, and automate savings.

We look at the hidden economics of elastic scaling for AI inference, the scaling decisions that affect your cost per inference, and what you can do now to optimize your AI ROI.