Operations | Monitoring | ITSM | DevOps | Cloud

The inevitable happened. A while back, Gartner projected that in 2026, 30–50% of all new SaaS product features would use LLM inference. That meant OpenAI-style costs would become a standard part of SaaS COGS. Today, OpenAI has become one of the most operationally significant line items for SaaS companies. But for many teams, this creates an uncomfortable gap. Engineering sees OpenAI as a fast path to innovation.

In 2025, Oracle shocked the market. Its cloud growth was so aggressive that Oracle’s stock surged, briefly making founder Larry Ellison the world’s richest person. That didn’t happen by accident. Oracle closed fiscal 2025 with $57.4 billion in revenue, mainly driven by cloud services. Oracle Cloud Infrastructure (OCI) grew roughly 50% year over year, driven by enterprise databases, AI workloads, and network-intensive applications migrating from more expensive platforms.

Right now, 48% of organizations say they’re being asked to measure or report on AI-related costs. The problem is that they’re still figuring out how to do it. That was a very telling stat from a recent CloudZero webinar on AI and profitability, and speaks loudly to the reality that many organizations are still struggling to get a grasp on AI spend which our data shows to be rising sharply as a part of total spend in recent months.

Think of a stateful application like a conversation with a barista who remembers your order every time you walk in. They know what you had yesterday, how you like it prepared, and what you’ll probably want next. That memory makes the experience smoother, but it also means that if that barista isn’t around, your experience can break down entirely. A stateless application, on the other hand, is similar to ordering from a self-service kiosk.

On paper, Gemini pricing looks straightforward. You pay per token. Input tokens cost one amount, output tokens cost another, and different models come with different rates. But once Gemini is wired into a production SaaS product, that simplicity disappears. Fast. That’s because token usage compounds across context, retrieval, and output — not across requests. The same “API call” can cost pennies in one feature and dollars in another.

When CloudZero CTO Erik Peterson joined the SourceForge podcast in January 2026, he didn’t just talk about cloud costs. He reframed them as a launchpad for innovation, survival, and competitive advantage. Whether he was describing the “trough of lost innovation,” the “freemium tax,” or why efficiency is the next frontier of engineering culture, Erik’s expert insights go beyond FinOps hygiene.

Consider this: traditional cloud cost monitoring was like checking your fuel gauge once a month — after the trip was already over. That model worked when infrastructure scaled slowly. You provisioned resources predictably and paid for stable, linear usage. AI breaks that model. Today, AI costs behave like a high-performance engine with a hypersensitive throttle. A small input, like a prompt change or a single power user, can dramatically increase your fuel burn in seconds.

With all we’ve seen from AI in the last several years, it can be easy to forget that it’s still in its very early days. As torrid as its evolution has been thus far, it will only intensify. As SVP of Engineering at a B2B SaaS company, I’ve had a front-row seat for much of this evolution. Here are three ways I see AI heading in 2026.

Choosing between AWS and OCI is a common decision for organizations moving workloads to the cloud. Both Amazon Web Services and Oracle Cloud Infrastructure offer global infrastructure, robust security, and broad service portfolios. On paper, the platforms can look interchangeable. They are not. AWS and Oracle Cloud differ in pricing, compute models, storage options, networking, and managed services. These differences affect scalability, reliability, and day-to-day operations.

Welcome to January’s Cloud Economics Pulse, CloudZero’s monthly look at cloud spend as AI moves from vibe to prod. And this related news flash — AI spend keeps hitting new highs. pilots to production. In last month’s Pulse, we explored the compounding effect of AI becoming part of everyday cloud operations. This month, we see that pattern harden into year-end results.