How we use Wiv.ai on our own infrastructure, and why it keeps our engineers building product instead of babysitting scripts.

Running engineering at Wiv.ai comes with an odd twist: we build a platform for cloud operations and FinOps automation, and we’re also a cloud-heavy software company with the same cost problems as our customers.

Like everyone else, we need to keep spend under control, find waste, and run efficiently. The quick fix is to write a script every time we spot something to optimize. Sometimes that’s the right call: a one-off cleanup doesn’t need more than a few lines of Python. But for recurring work, that approach doesn’t hold up.

So for anything ongoing, we started running our own cloud operations on Wiv.ai.

Scripts Are Cheap. Maintaining Them Isn’t.

Writing a Python script to clean up unused resources takes an afternoon. Keeping a few dozen of them working as your infrastructure changes is a different story.

Every new service, config change, or optimization idea adds one more thing someone has to maintain. Before long, your engineers are debugging internal tooling when they should be shipping features.

That’s the part people miss. The real cost isn’t only the cloud waste. It’s the engineering hours you spend chasing it. For us, it would mean spending those hours on exactly the problem our product exists to solve.

Script or Process? How to Tell

Not everything needs a platform. If it runs once, touches nothing critical, and nobody will miss it when it’s gone, write the script and move on.

But a few signs tell me a script is really a process in disguise:

It runs more than once. If you’re scheduling it, putting it on a cron job, or rerunning it every time someone asks, it’s not a one-off anymore.

It needs a decision. The moment someone has to check “is it safe to delete this?” or “who owns this?”, you have a workflow with a human step, not a script.

It needs context the code doesn’t have. Knowing which customer a resource belongs to, whether a workload is about to spike, or whether a team is mid-migration. Hardcoding that into a script is where things start to break.

Someone needs to know it happened. If it has to notify a team, log an approval, or leave an audit trail, it’s already a process.

It would hurt if it broke silently. Scripts fail quietly. If a missed run means real money or a production issue, it needs monitoring and ownership.

If two or three of these apply, it’s time to stop treating it as a script.

Using Wiv.ai on Ourselves

A good example from our own infrastructure is multi-tenant cost attribution. On paper, it sounds like a script: pull the bill, split it by customer, done. In practice, it hits almost every sign above.

Our customers share infrastructure, so the total AWS bill tells us very little. We need to know which customers, workflows, and executions are actually driving consumption. That’s context no script has on its own.

To get it, we built an attribution layer that ties execution metadata to resource configurations and AWS pricing. And it doesn’t run once. It runs continuously, because usage changes every day.

Then comes the part that makes it a process: acting on what it finds. When costs spike for a customer or a workflow, someone needs to know, and sometimes someone needs to decide what to do. Wiv.ai handles that end to end, from spotting the anomaly to triggering the right action and routing approvals where they’re needed.

The nice side effect is a tight feedback loop. Every issue we run into on our own infrastructure points to a gap in the product, and closing it makes Wiv.ai better for everyone.

Where FinOps Meets Engineering

That “someone needs to decide” step is where people come in. Our VP FinOps and our engineering team work closely on it.

The VP FinOps finds the cost drivers and the opportunities to save. Engineering brings the architectural context and decides which actions are safe to automate.

Wiv.ai sits between them, with reusable workflows, governance policies, and controlled execution.

The point isn’t to take engineers out of the loop. It’s to take the repetitive work off their plate and give them better information when a decision really does need their judgment.

What Dogfooding Has Taught Us

Three lessons keep coming up, both in how we run our own cloud and in how we build the product:

Context matters more than thresholds. Early on, it’s tempting to automate on numbers alone. What we’ve learned is that the right action depends on the workload, the customer, and what happens operationally when you act.

Automation needs guardrails. Cloud actions should run within clear permissions, policies, and approval flows. The riskier the action, the more oversight it gets.

FinOps never finishes. Infrastructure keeps changing, so automation needs ongoing monitoring, validation, and tuning to stay useful.

Frequently Asked Questions

What does dogfooding mean at Wiv.ai?

We run our own cloud operations and FinOps on Wiv.ai, and use what we learn to improve the platform.

Why move away from custom cleanup scripts?

Each script fixes one problem and adds one more thing to maintain. Wiv.ai gives us reusable, governed automation instead of a pile of one-off tools.

How do you decide what should be a script and what should be a workflow?

If a task runs repeatedly, needs a human decision, depends on business context, requires notifications or an audit trail, or would cause real damage if it failed silently, it belongs in a governed workflow. Simple one-off tasks can stay as scripts.

How does Wiv.ai help optimize cloud infrastructure?

It connects cost insights to automated workflows, so optimization opportunities get acted on within the policies and approvals you define.

What is multi-tenant cost attribution?

It maps shared cloud usage back to individual customers, workflows, and executions. That makes unit economics more accurate and cost anomalies easier to catch.

Is automated FinOps safe?

Yes, when it’s governed properly. Automation runs within defined permissions and policies, and higher-risk actions can require a human to approve them.

What’s the business value of dogfooding?

We run more efficiently, and we test Wiv.ai against real-world problems every day. Both our engineering team and the product get better as a result.

We Build What We Use

As CTO, I want our engineers building the product, not maintaining scripts that should have been processes.

Running our own operations on Wiv.ai means we feel the same pain our customers do, and we can turn that pain directly into product improvements.

For us, cloud automation isn’t only something we sell. It’s how we work.