While cost visibility has improved, cost control hasn't. Fixing that starts with resourcing the people who can act on what's driving the spend.
A cloud cost optimization strategy now benefits from more visibility than in past years, thanks to wider FinOps adoption and cloud platforms that flag unusual spending automatically.
Yet, according to Flexera's 2026 State of the Cloud report, managing cloud costs remains the top challenge for 85% of organizations, ahead of security for the fourth year in a row. Wasted cloud spend also increased to 29% this year, reversing five years of decline.
So why isn’t better visibility translating into lower spend?
The answer comes down to the fact that identifying waste and eliminating it are two different capabilities. And it’s the exact pattern we keep hearing about from the hiring managers we speak with across technology teams.
Cloud cost optimization has a visibility problem solved and an execution problem ignored. Teams can see exactly where money is being wasted but rarely have the engineering bandwidth to fix it. Closing this gap requires dedicated FinOps, cloud engineering, and DevOps resources treating cost control as an ongoing delivery project.
Why cloud cost optimization is a delivery problem, not just a finance one
Tracing cloud spend isn’t the hard part anymore, as organizations can now identify exactly where their cloud dollars are going. Instead, acting on that information fast enough is where most teams fall behind.
Flexera points to the added complexity introduced by AI and new cloud service offerings as a driver of this year’s increase in cloud waste. When organizations scale AI workloads in the cloud, 30% cite skills gaps as their top challenge.
The FinOps Foundation's 2026 report describes a similar pattern on the ground. As one practitioner put it, they've already hit the 'big rocks' of waste, and the smaller opportunities that remain take more effort to find.
We call this the optimization execution gap. It's the space between spotting a way to save money and having the people available to act on it. A recommendation only becomes savings once someone puts in the work to make it happen. And that work needs to be resourced just like any other project.
Key roles for cloud cost optimization: FinOps, cloud engineering, and DevOps
Closing the execution gap starts with knowing who’s responsible for it.
Cost visibility shows you where the money is going, but turning that into savings means changing how a workload is built, deployed, or scaled. That's engineering work as much as it is a finance one.
AWS's own cost optimization guidance backs this up. It describes cost ownership as a multidisciplinary function spanning project management, data science, financial analysis, and software or infrastructure development, not a single role
For cloud cost optimization specifically, that multidisciplinary function breaks into three roles that matter most:
- FinOps specialists: To establish financial visibility, cloud cost allocation, and unit economics
- Cloud engineers: To rearchitect, rightsize, and optimize cloud workloads
- DevOps engineers: To build automation, CI/CD guardrails and policies that sustain long-term savings
A gap in any one of them leaves the execution gap open, and closing each one means asking whether it's a capability worth owning, or a delivery load that just needs to be cleared.
Closing the cloud optimization execution gap with contract talent
Sometimes it makes sense to prioritize speed over cost savings. Shipping a feature, hitting a migration deadline, or clearing a security backlog can matter more than a rightsizing project.
But the longer a cost-saving recommendation sits unactioned, the more it ends up costing.
Even mature teams aren't solving this through headcount growth alone. According to the FinOps Foundation, they're scaling through federation, automation, and embedded champions instead.
The same logic applies whether the gap sits in FinOps, cloud engineering, or DevOps; the fix isn't always a new permanent hire.
That points to a better question than "do we need to hire." You need to ask whether the constraint is a capability the organization needs to own over the long term, or a delivery load that needs to be cleared once.
Consider a permanent hire when the work is ongoing and the context builds over time:
- An ongoing FinOps operating model
- A cloud governance function
- A platform team building reusable automation
Consider contract talent when the work is bounded and has a clear endpoint:
- A defined optimization backlog
- A migration deadline
- A temporary spike in engineering demand
Contract talent closes the specific gap without pulling the existing team off other priorities or committing to headcount the work won't need once it's done.
How to reduce hiring friction while protecting project quality
The fastest hire isn't always the right one, and a slow search just lets the backlog grow. The way through is knowing exactly what you're hiring for before the search starts.
Is the gap in visibility, engineering capacity, or automation, or is there enough expertise already and simply not enough time to use it?
Hiring managers who answer that question first move faster, because they're evaluating candidates against a specific gap instead of a general job description. They also protect project quality because the person they bring on actually matches the constraint that's stalling the work.
How we help source cloud optimization talent
Cloud cost is one part of a larger delivery equation. Skills gaps, capacity constraints, and competing priorities can put an optimization initiative at risk just as easily as technical complexity can.
For more than 32 years, S.i. Systems has helped Canadian organizations build and scale their IT delivery teams. Today, we source the FinOps, cloud engineering, and DevOps talent needed to turn cost insights into action, whether that’s a single specialist, full team, or delivery POD.
Not sure where to begin? Our IT Project Delivery Assessment highlights where skill and capacity gaps are most likely to affect delivery.
