Let's talk about something many CTOs don't stop to calculate when thinking about cloud costs: the total cost of running a complex cloud setup with only your own team.
Most companies track cloud spending by looking at one number: the monthly invoice. That number is real. But it's not the total cost.
After managing infrastructure for dozens of companies at Quave ONE, we've found that the invisible costs are typically 2-3x larger than the bill itself. Here's what we mean.
The hidden cloud costs your invoice doesn't show
Engineering time on infrastructure instead of product
Ask any engineering leader how much of their team's time goes to infrastructure versus product. The honest answer is usually 30-40%.
In a team of ten engineers, that means three to four are effectively full-time on operations. Not shipping features. Not building product. Configuring scaling policies, debugging deployment pipelines, rotating certificates, watching alerts at 2am, and figuring out why staging broke again.
These are usually your most senior people - because junior engineers can't safely touch production. So your most experienced, most expensive talent is doing work that doesn't differentiate your product at all.
The math is straightforward. If those engineers cost $150-200k/year fully loaded, and 35% of a 10-person team is on ops, that's $525-700k/year spent on infrastructure management. None of that shows up on your cloud invoice.
Over-provisioned resources and cloud waste
Almost every company we've audited at Quave ONE has the same pattern: instances running 24/7 that could be scaled down, reserved capacity that is the wrong size, storage volumes nobody remembers attaching, and snapshots accumulating for months because deleting them feels risky.
This happens because provisioning is easy and de-provisioning is scary. An engineer spins up a staging environment, the feature ships, and the environment keeps running. Nobody owns the cleanup because nobody is measured on it.
The typical waste is 25-40% of the total bill. On a $10k/month bill, that's $2,500-$4,000 burning quietly in the background.
Your cloud provider's cost dashboard shows you what you spent. It doesn't show you what you didn't need to spend.
The opportunity cost of cloud complexity
Every sprint where your team is fixing infrastructure instead of building product is a sprint your competitors used to ship features. Every week your CTO spends evaluating cloud architecture is a week they didn't spend on hiring, strategy, or customers.
Most companies can't answer the question "what would our product look like if our entire engineering team could focus on the product?" because they've never experienced it.
Why cloud costs keep growing (regardless of provider)
The pattern is consistent whether you're on AWS, GCP, Azure, or any other provider.
Engineering provisions fast because they need to ship. Finance sees the bill but doesn't know what is running. DevOps tries to optimize without breaking anything while also being responsible for uptime, deployments, and security patches.
Everyone is partially responsible for cloud costs. So nobody is fully responsible. The bill grows 10-15% per quarter and nobody can explain exactly why.
There's also a complexity problem. Every major cloud provider has hundreds of services, each with its own pricing model. Some charge per hour, some per request, some per GB transferred, some per GB stored, and some differently by region. No human can hold all of that in their head while making real-time decisions.
So engineers do what is rational: over-provision to be safe and don't de-provision because the risk of breaking something outweighs saving a few hundred dollars.
How to actually reduce cloud costs
We've worked with companies across AWS, GCP, Azure, and OCI at Quave ONE. Here's what we've seen move the needle - and what doesn't.
Cloud cost strategies that don't work
Telling engineers to be more careful. They already know. The problem is structural, not behavioral.
Hiring a dedicated FinOps person. This helps with visibility but doesn't fix the root cause. One person can't optimize decisions made by 20 engineers in real-time.
Switching cloud providers. You'll get a better deal for 12 months. Then the same patterns emerge. The provider isn't the problem.
Cost alerts alone. Alerts tell you something happened. They don't prevent it, and they don't tell you what to do about it.
Cloud cost strategies that actually work
Visibility by default. Your team needs to see what is running, what it costs, and who owns it - without effort. If understanding your spend requires logging into a dashboard and running queries, it won't happen consistently.
Separation of concerns. Engineers should make product decisions. Infrastructure should be abstracted to a layer that handles provisioning, scaling, and optimization automatically.
Continuous right-sizing. Not a quarterly audit. Resources should scale to match actual usage in real-time, based on actual traffic and workload patterns.
Making de-provisioning as easy as provisioning. If spinning up takes one click but shutting down requires three dashboards and two teams, you'll always have drift.
Cost attribution to teams. When a team sees that their service costs $3,200/month and 40% is idle compute, they make different decisions than when they just see a company-wide bill of $47,000.
The real problem isn't cost - it's complexity
Most companies don't have a cloud cost problem. They have a cloud complexity problem that shows up as cost.
The bill is a symptom. The underlying issue is that managing cloud infrastructure well requires a level of specialization, tooling, and continuous attention that most engineering teams can't justify dedicating resources to.
The companies that have this figured out either have large, dedicated platform teams (which most can't afford), or they've found a way to abstract the complexity so their product engineers can focus on product.
There's no trick and no magic dashboard. It's a structural problem that requires a structural solution - regardless of which cloud provider you're on.
The Quave ONE team manages cloud infrastructure across On-premise, AWS, GCP, Azure, and OCI for companies ranging from early-stage startups to public companies with a lot of data and high traffic. We write about what we've learned along the way.