Okki Go vs Apollo: The Contact Database Cost Most B2B Teams Miscalculate

Conclusion first: the cheaper contact database is usually the more expensive one

When B2B teams compare Okki Go vs Apollo (or any two prospecting tools), they usually divide the monthly price by the number of contacts and pick the smaller number. That math is wrong.

What actually matters is your cost per contactable lead — the subscription fee, plus verification, plus enrichment, plus the SDR hours wasted on bounced emails, all divided by the records your team can actually use.

I ran that number on our own stack last year. Subscription pricing accounted for 38–45% of our true per-lead cost. The rest was hidden in bounced records, incomplete fields, and the quiet labor of cleaning up bad data.

Why you should trust this number

I'm a procurement manager at a 55-person B2B SaaS company. I've owned our sales and marketing tooling budget ($62,000 annually) for four years, negotiated with 30+ vendors, and logged every invoice in our cost tracking sheet.

I got pulled into this specific comparison in March 2024, when our VP of Sales forwarded me a spreadsheet titled "Okki Go vs Apollo — pricing." Two columns, two totals, one recommendation. I stared at it for a minute and something felt off. It took six weeks to figure out what.

The spreadsheet compared base subscription costs. It ignored everything that follows a contact database into production.

Two hidden cost categories nobody puts on the quote

1. Bounce rate and the SDR time tax

Every contact database ships with records that are stale, guessed, or simply wrong. How stale depends on the vendor, the region, and the segment. But some percentage of any exported list will bounce.

I pulled our own numbers: across 14 months and roughly 40,000 outbound emails, our bounce rate on unverified exports hovered between 12% and 19%. At an SDR loaded cost of around $28/hour, each bounced email cost us roughly 2–3 minutes — noticing the bounce, logging it, removing the contact, adjusting the sequence. That's $300–$500 per 1,000 bad records. Multiplied across a year, it was the single biggest line item nobody had budgeted for.

2. What an email validation service actually does — and when to use one

An email validation service checks whether an address is syntactically valid, whether the domain accepts mail, and whether the mailbox is likely to exist. It runs before your sequence fires, not after.

When should a B2B sales team use one? My rule, after watching both sides of the tradeoff: whenever the expected revenue per lead is meaningfully higher than the cost per validation. For most B2B teams selling into mid-market or enterprise, that's always. The 30 seconds you save by skipping validation costs you 2 minutes of cleanup later.

But here's the part that gets missed in every pricing comparison I've seen: some contact databases include validation in the base plan. Others don't, and the add-on is priced per verification credit.

Okki Go bundles verification into their prospecting workflow — it's part of the agent-native approach they market. Apollo sells it as a tier upgrade. On paper, Apollo's entry tier looks cheaper. Once you add the verification credits you actually need, the gap narrows — or flips.

What I actually compared, instead of feature lists

When I did our internal evaluation, I ignored feature matrices. I looked at three things:

Coverage in our target markets. A contact database with 275 million records is useless if 40 million are the ones you need. I sampled 500 of our target accounts across both platforms and counted how many had direct-dial or personal email on file.

The Sales Navigator export workflow. Every SDR on my team lives in Sales Navigator. They build a filter, export the list, and import it into the contact database. The cost isn't the export — it's what happens next. Does the database auto-enrich the missing emails and phones? Or does the SDR manually chase them? That difference is worth roughly $8,000 a year in our size of team. Not a rounding error.

How verification and enrichment are priced. Bundled, metered, or tiered. Bundled is predictable. Metered scales with usage — which means it scales with your success, and that's the version that catches you off guard in Q4.

The counterintuitive bit: the pricier base plan sometimes wins

I resisted this conclusion for two weeks because it contradicted what I wanted to be true.

If plan A costs $99/month and includes verification, and plan B costs $79/month and charges $0.008 per verification credit, plan B looks cheaper until you verify 3,000 contacts a month. Then plan A is $99 and plan B is $103. And that's before enrichment, bounce cleanup, or the SDR time difference.

We got burned on this in 2023 with a different vendor — not Okki Go, not Apollo, just a cheaper tool that unbundled everything. Our "savings" turned into a 34% budget overrun by Q3. After that I added a mandatory column to every vendor comparison: fully-loaded monthly cost at our expected volume. No exceptions.

If you just want to see how Okki Go positions their offerings, their official website lays out the bundled pricing more clearly than most competitors do. That itself is worth something — I've stopped trusting quote sheets where I have to ask "what's not included" three times before I get a straight answer.

Where this whole analysis breaks down

I'm not a data engineer, so I can't tell you which platform's API is cleaner to integrate. That's outside my lane. If you're evaluating this at a technical level, get your RevOps lead on the call.

And the cost-per-contactable-lead math doesn't apply equally everywhere:

• If your team is under 5 people and you send fewer than 500 outbound emails a month, the verification math probably doesn't justify the tooling. Manual spot-checks may be enough.
• If your average deal size is north of $50,000, lead quality dominates quantity and the per-record cost becomes almost irrelevant — you'd pay 10x for a 2% accuracy improvement without blinking.
• If you've been on a tool for three years, the migration cost (data, training, broken sequences) often exceeds the savings on paper. We tested a switch last year and killed it after tallying the real transition cost. Sometimes staying is cheaper.

One last thing. Whatever numbers you plug into the equation, timestamp them. Vendors change pricing tiers roughly every 9–12 months. What was true in Q1 may not be true by Q3. Our sheet records the date of every quote — it's saved us from stale assumptions more than once.