During a proof of concept last year, an engineering team at one of the world's largest cybersecurity companies ran 56 API requests.
Out of 10,000 allocated.
Their EVP of Product stopped the session. "The speed is almost suspiciously fast," he said. "Is this actually live data?"
It was. And that question — asked in mild disbelief — contained everything you need to understand about the problem Vetric was built to solve.
The Tax Nobody Talks About
Every company that builds products on public data eventually hits the same wall.
You build a pipeline. It works. Six months later, a platform changes its interface, shifts its access patterns, or updates its terms. Your pipeline breaks. An engineer drops what they're doing, figures out what changed, and rebuilds it. A month later, you're back to where you were — not ahead, just back to square one.
Now multiply that by four platforms. And repeat it every six months.
That's up to eight engineering months per year spent on infrastructure that exists only to stay in place. Not to improve. Not to scale. Just to survive.
We call this the infrastructure tax. It doesn't show up on a roadmap. It doesn't get celebrated in a sprint review. But it quietly consumes the time, focus, and momentum of the engineers you hired to build something that matters.
The Problem Beneath the Problem
The rebuild cycle is the visible part. What sits underneath it is worse.
When engineering capacity is locked into maintenance, everything else suffers. Coverage stays shallow because there's no bandwidth to go deeper. New platforms stay off the roadmap because adding one means another multi-month build with no stability guarantee. Manual processes that should have been automated years ago keep running because nobody has time to replace them.
The cybersecurity company in our proof of concept knew this intimately. Their analysts were capped at five pages of search results, running at weekly cadences — because the infrastructure couldn't support anything more demanding. Their Disruption team was manually clicking through hundreds of links to verify whether reported content had been taken down. Their VP of Product asked about expanding to new platforms every quarter. Engineering never had a good answer.
It wasn't a talent problem. It wasn't a prioritization problem. It was an infrastructure problem masquerading as a capacity problem.
The Build vs. Buy Question, Answered Honestly
Most engineering teams, when they first encounter this problem, reach the same conclusion: we'll build it ourselves. We have the talent. We understand the requirements. It'll be faster than evaluating vendors.
That logic holds — right up until the first rebuild.
The real question isn't whether your team can build a data pipeline. They almost certainly can. The question is whether you want them rebuilding it every six months, indefinitely, while your competitors are shipping products.
Public data infrastructure is not a one-time engineering project. It's an ongoing operational commitment. Platforms change constantly. Access patterns shift. What works today requires maintenance tomorrow. Owning that layer means owning every future change that comes with it.
The director of product management at the company in our case study put it plainly: "If we can buy that more effectively than we can build it in-house, that frees up our time for risk detection, platform experience — the things that actually need our attention."
What Happens When the Tax Goes Away
When this company moved to Vetric, the results were immediate and compounding.
Four platforms went live in three months — each previously requiring a month of engineering effort to build and another month to rebuild every time something changed. Their search depth moved from five pages weekly to fifteen pages daily, surfacing threats that had been invisible before. The Disruption team went from manual spot-checks to 200,000+ automated verifications per month. Five separate internal teams adopted the infrastructure independently, without a single additional commercial conversation.
Within ten months of their first API call, they signed a multi-year production contract at five million requests per month — twenty-five times their original volume.
But the number that doesn't appear in any of those stats is the one that matters most: the engineering hours that stopped going into maintenance and started going into detection models, AI, and the platform features their clients actually pay for.
Talented engineers hired to build innovative products shouldn't spend their careers keeping the lights on. When infrastructure becomes someone else's problem, teams get back something harder to measure than requests-per-month — the ability to actually move forward.
The Infrastructure Should Be Invisible
The best data infrastructure is the kind you never have to think about. It's there when you need it, at the depth and scale you need it, regardless of what changes on the platform side. Your engineers wake up focused on the product — not on what broke overnight.
That's what Vetric is built to be.
If your team is spending time on rebuilds, reach out. The first conversation is free, and the proof of concept is faster than you'd expect.