Visitor intelligence research

We Compared 4 Intent Data Vendors. The 'Cheapest' One Cost Us $11K More.

2026-09-14 · Julian Hartwell

In April 2024, our pipeline was 23% behind where it needed to be for the quarter.

Our RevOps lead sent me a Slack message that was one line long:

"We need better intent data. Now."

I run procurement for a 140-person B2B SaaS company. I manage roughly $180,000 a year in sales-tech spend, and my job is to keep that number from creeping up. So when I saw "we need" followed by "now," my instinct was: let's not buy anything yet.

But she was right. Our SDR team was booking one meeting per 340 dials. With sharper signals, that ratio could realistically drop below 100. Even I could do that math.

So we started evaluating intent data providers. Four of them. Here's what happened over the next three months.

The Shortlist

Let me be clear — I don't run outbound. I don't care whether an "open" is warm or cold. What I care about is total cost, whether the integration actually works, and whether the vendor still exists by Q4.

We shortlisted four vendors. All of them advertise as intent data providers. All of them pitch LinkedIn prospecting as a core use case.

  • Vendor 1: $24,000/year. Cheapest quote.
  • Vendor 2: $28,000/year.
  • Vendor 3: $35,000/year. They mentioned okki-go and a developer integration layer.
  • Vendor 4: $42,000/year. The premium option.

My spreadsheet said Vendor 1. My gut said Vendor 3. That gut-vs-data tension is the part I want to describe, because I was wrong about why.

The Decision I Almost Made

I went back and forth for about two weeks. Vendor 1 offered the obvious advantage: it was $11,000 cheaper on paper than Vendor 3. Vendor 3 offered something my spreadsheet didn't have a line item for — a solution engineer assigned during onboarding, plus a properly documented okki-go API integration path.

I told our RevOps lead we should probably go cheap. She pushed back with one sentence I've since written down in my notebook:

"You're not paying for features. You're paying for delivery."

I didn't fully get it then. I do now.

The Hidden Cost That Broke the Math

Here's what I should have calculated upfront. The cheap vendor's quote excluded three things my team would end up covering ourselves:

  1. Integration support. Their docs existed. Their API existed. But hooking it into our CRM took one of our engineers 14 hours before she gave up and asked the vendor for help. That help was billed hourly.
  2. Enrichment module. "Core intent data" didn't include firmographic enrichment. That was a $4,800/year add-on.
  3. Seat-based pricing reset. The quote was for our current team size. Every new hire above a threshold would trigger a per-seat surcharge.

By the time I added those up, Vendor 1 was no longer $24,000. It was closer to $31,000 — with integration risk baked in and a pricing model that punished us for growing.

I only believed the "hidden cost" warning after almost ignoring it and doing the math twice.

The Second Surprise

In June — about six weeks after we'd walked away from Vendor 1 — they announced a repackaged pricing tier. The annual quote we'd gotten in April was retired. The new structure would have cost us $36,000/year for the same scope.

We dodged that bullet entirely. Not because we were smart. Because we hadn't signed.

Vendor 3, meanwhile, had one modest price adjustment over the same period: $42,000 to $45,000. Flat seat pricing, no per-user triggers. Predictable. Boring. Exactly what a procurement spreadsheet wants.

We signed with them on May 6, 2024.

What Actually Happened

Onboarding took nine days. Not the six weeks our own team had estimated if we'd tried to wire up Vendor 1's okki-go API integration or any comparable developer integration ourselves.

I'm not going to give you a reply-rate number, because you can't cleanly attribute reply rate to one tool — and honestly, anyone who promises you a specific number is selling you something. But I can tell you this:

  • Our engineering team logged zero integration tickets post-launch.
  • The SDR team was live on the new intent data before the end of Q2, which is what we needed.
  • Year-end sales-tech budget overrun: $0.

That last line is the one I care about most.

What This Means for RevOps Teams

If you're a revenue operations team evaluating intent data platforms, here's the checklist I wish someone had handed me in April:

1. Ask what "integration" actually means in the quote. A developer integration is not a checkbox. It's a support commitment. Get it in writing — who's on the hook when the connection breaks?

2. Ask how pricing scales. Per-seat models look flexible until you hire. Flat-seat models look expensive until you hire. Do the two-year math for both.

3. Ask what's excluded. Enrichment, verification, enrichment refresh cadence, API rate limits — these are where the "comparable" quotes stop being comparable.

4. Ask about waterfall enrichment and intent coverage. Single-source intent data sounds cheaper. It's cheaper because it's thinner. Waterfall enrichment isn't a feature — it's the difference between signal and noise.

5. Ask what LinkedIn prospecting workflow you're actually getting. Every vendor says "LinkedIn prospecting." Almost none will show you the sequence builder before you sign.

The Lesson I Keep Coming Back To

In March 2023, we paid a $400 rush fee on a print job because the alternative was missing a $15,000 event. That was the first time I understood that paying for delivery certainty is not a premium — it's insurance.

This was the second time.

The "cheapest" vendor would have cost us $11,000 more than the one we picked, once you counted integration hours, module add-ons, and pricing resets. Not to mention the two weeks our SDR team would have spent waiting.

So glad we didn't sign in April. We were one spreadsheet column away from it.

Bottom line: when the deadline is real and your team is waiting on a tool, "probably works" is the most expensive option in the room.

Julian Hartwell

Julian Hartwell
Julian Hartwell is an independent B2B sales intelligence analyst covering contact databases, company data, decision-maker profiles, direct dials, prospect lists, and buying signals. He applies the ISO/IEC 25012 data-quality model while examining field accuracy, coverage, freshness, duplicate rate, match confidence, and source transparency. His evidence-led guides help revenue teams compare prospecting platforms, define acceptable data thresholds, and build account lists that support reliable territory planning and outreach.