Cloud waste isn’t about Visibility it’s about Timing, says Atmoz CEO

“Cloud waste isn’t created by bad engineers. It’s created by systems that show problems too late. Once I saw that, it became clear, the solution wasn’t better reporting. It was prevention.” – Atmoz CEO Yael Shatzky

Yael Shatzky didn’t set out to build a company around cloud costs. What she noticed, after more than 25 years across enterprise technology, product marketing, and growth at organisations including Amdocs and Microsoft’s R&D ecosystem, was a pattern.

Not just rising cloud spend, but a deeper structural disconnect in how it’s managed.

If you were introducing yourself and Atmoz to someone outside tech, where would you begin?

I’d say I’m building a company that changes how people think about waste—specifically cloud and AI waste.

Imagine a house where electricity prices constantly change depending on what you use and when, but no one knows the cost. Lights stay on, AC runs all day, and while you know you’re wasting about 30%, you have no way to prevent it. The only signal you get is last month’s bill.

That’s how companies operate in the cloud today.

Atmoz changes that by bringing cost awareness into the moment decisions are made, helping teams make smarter choices without disrupting how they work. The result is simple: waste is prevented before it happens.

What is the core problem Atmoz is solving—and where has the market gone wrong?

The market has focused on visibility, dashboards and reports that explain what already happened.

But the problem isn’t visibility.
It’s timing.

By the time companies see the data, the money is already spent and systems are already in production. Even with perfect visibility, nothing changes.

Atmoz works at the moment engineers are building, engaging them with immediate, simple recommendations that don’t slow them down. That’s where prevention becomes possible.

What does ‘AI-first’ product development look like at Atmoz?

We built a data foundation that reconstructs cost signals as resources are created, before billing data exists. That’s the hard part.

On top of that, we use AI where it matters most: interaction and execution. Our AI agent takes accurate, contextual data and delivers actionable recommendations directly within developer workflows.

Because the system is grounded in precise data, the guidance isn’t just intelligent, it’s reliable and immediately usable.

What are the biggest challenges in getting engineers to trust AI-driven recommendations?

Interestingly, it’s not trust in AI, it’s the belief that prevention is even possible.

For years, companies have been told they can reduce costs, yet around 30% of cloud spend is still wasted. That’s because most tools analyse waste after it happens, they don’t stop it.

Once engineers see an issue flagged in real time, with clear context and a simple fix, the skepticism disappears. It becomes tangible.

What is one leadership mistake that fundamentally changed how you operate?

Focusing too much on the product, and not enough on marketing early on.

Great products don’t speak for themselves, especially when you’re creating a new category. Marketing isn’t something you layer on later; it shapes how the product is understood and adopted. Starting early makes a significant difference.

Where do you see the biggest inefficiencies today?

The biggest inefficiency is the disconnect between engineering decisions and their financial impact.

Every time a developer deploys infrastructure or triggers an AI workload, they’re making a financial decision, without visibility into its cost implications.

AI is amplifying this. Costs are more volatile, and traditional feedback loops can’t keep up.

Atmoz brings cost awareness into that decision point, making efficiency part of the engineering discipline, much like security became over time.

At this stage, how do you define success?

Success isn’t a single milestone, it’s a series of moments.

Signing a new customer. Launching a capability that impacts spend. Getting a call from a customer excited because they just saved $30K on something they didn’t even know was happening.

Those moments are what drive us forward.

You’re defining a new category. What does it take to change long-held assumptions?

It starts with conviction. You’re asking people to question something they’ve accepted as normal.

But conviction alone isn’t enough, proof is everything. Category change happens when someone sees it working in their own environment and has that “aha” moment.

That’s why we focus on immediate, tangible value. When waste is prevented in real time, the mindset shift follows naturally.

Resilience also matters. When you challenge established models, you will be dismissed. The key is to stay grounded in the problem and keep showing evidence.

Has the industry been solving cloud waste the wrong way? Why hasn’t it changed?

I wouldn’t say wrong, FinOps tools solved the problem they were designed for. They brought visibility and governance, which was critical.

But they were built on the assumption that cost is something you analyse after it happens.

Today, cost is created instantly, when infrastructure is provisioned or AI workloads run. But feedback still comes later. That gap is the issue.

What’s changed is the pace of engineering. With AI, decisions are faster and costs are more dynamic. What used to be inefficient is now unsustainable.

That’s why prevention isn’t just an improvement, it’s becoming essential.

How will engineering teams work differently in five years?

Cost will no longer be treated as something external, owned by finance. It will become part of the engineering feedback loop, like performance or reliability.

Atmoz brings that awareness into everyday workflows, guiding better decisions without adding friction.

Over time, this shifts behaviour. Waste isn’t something you detect and fix later, it simply doesn’t get created.

The result is not just lower cost, but faster teams, better decisions, and more room to innovate.