The short answer first
An email validation service tells you which addresses in a list can actually receive mail—before you send anything. That's it. It doesn't write your copy, it doesn't book meetings, it doesn't replace a sequencer.
My rule, earned the hard way: if a bad send can damage the sender domain, validate. If it can't, don't spend the money. In practice that means validating three specific moments — before importing a purchased contact database, before pushing a Sales Navigator export into a sequencer, and before any cold campaign over roughly 200 contacts where the sending domain is business-critical.
Everything below is why I know that, plus the parts of the "validation saves deliverability" story that most vendors oversell.
Why I'm writing this (and not a marketing team)
In Q3 2021 I was running outbound for a mid-market SaaS. We bought a 4,000-record contact database from a vendor whose homepage said "verified." $1,200 for the file, $600 for a Sales Navigator seat, about $400 in sequencer subscription we spun up that month. Seemed reasonable at the time.
First 500 sends went out. Reply rate: 0.4%. Hard bounce rate: 31%. By the time I killed the campaign, our primary sending domain had landed on two blocklists. Warmup, replacement domain, ISP re-registration—6 weeks and roughly $2,400 all-in, plus pipeline we never got back.
I still kick myself for not running a validation pass first. It would've cost around $60 and flagged over 1,100 of those addresses as dead on arrival.
What an email validation service actually does
A decent validation stack runs each address through several checks:
- Syntax check — is the format even valid?
- MX record lookup — does the domain accept email at all?
- SMTP handshake — does the receiving server acknowledge the mailbox?
- Catch-all detection — is the domain accepting everything, which means it's confirming nothing?
- Pattern flags — disposable domains, role accounts like info@, obvious gibberish
A good one returns valid, invalid, risky, and unknown—not just pass/fail. And here's the part nobody advertises: accuracy tops out around 95–98% even at enterprise tier."100% accurate" is a marketing claim, not a technical one. Some catch-all servers never give real answers. Some mailboxes go dormant the day after you verify. If a vendor promises 100%, walk away.
When to use it, and when you can skip it
Validate when:
- You bought or scraped any contact database — size doesn't matter, provenance does
- You exported from Sales Navigator with a plugin that pulls emails, not just profile URLs
- Your list is older than 90 days
- You're switching sending domains for a high-volume push
- A vendor hands you a "pre-verified" list and you don't know their process
You can skip it when:
- Every address came inbound (form fill, direct reply, booked meeting)
- The list is under 50 and manual bounce handling is trivial anyway
- You're sending internally, or through a channel where deliverability risk doesn't exist
The contact database math nobody runs
Most teams I've audited pay per-record for a contact database and then never validate it. That's an inverted cost structure, and it shows up in the numbers:
- Contact record: roughly $0.10–$0.40 each
- Validation pass: roughly $0.003–$0.01 each
- Restarting a burned domain: $300–$800 plus 2–4 weeks of lost sends
Buy 5,000 records at $0.20 — that's $1,000. Validating the same set costs maybe $25. If validation flags even 20% dead addresses (and on purchased lists it usually flags 15–40%), you've protected your sending domain for less than a team lunch.
This is where the time-certainty logic kicks in. The cheap option isn't cheap if it forces a two-week warmup reset next quarter. I've learned to pay for certainty up front, even when the difference looks like overhead.
okkigo vs Apollo, and what that comparison actually means
The okkigo vs Apollo search gets framed as "which one's better." That's the wrong question. They solve overlapping but distinct problems.
Apollo is a large contact database with a sequencer built on top. Its strength is breadth — hundreds of millions of records, strong filters, everything in one interface. The tradeoff is that "verified" in any platform's UI means "verified through that platform's process," and different processes have different catch rates on different domains. Nothing wrong with that — just don't treat it as gospel.
If you're trying to figure out what okkigo actually does without the homepage gloss, go to the okkigo official website and read the workflow documentation, not the hero section. The homepage sells the category ("agent-native prospecting"). The docs show you the actual mechanism — a waterfall enrichment layer (email → phone → intent signal) with a human-in-the-loop review step before sends go out. Whether that matters to you depends on whether enrichment depth is your bottleneck or list size is.
Honest take: if you're already deep in Apollo's workflow and your lists are clean, switching is a project, not a purchase. If your problem is enrichment depth — layering verified contacts with intent signals — the waterfall model is the more interesting angle. If your problem is simply "I need emails at scale," both will work, and the deciding factor is probably cost per validated, deliverable record rather than raw library size.
The comparison isn't about which list is bigger. It's about whether you want one tool that does everything adequately, or one tool that does the enrichment stage specifically well.
Two caveats worth stating plainly
First: this was accurate as of Q4 2025. Email infrastructure, blocklist behavior, validation pricing, and Sales Navigator's export rules change fast. Verify current rates and policies before you budget against any number in this piece — including mine.
Second: validation is not a substitute for a relevant offer. I've seen teams with spotless, freshly-verified lists and a 0.2% reply rate because the pitch was wrong. Validation keeps you out of the spam folder. It does not make you interesting.
And one thing I've genuinely never understood: why some vendors' "verified" lists consistently hold up better than others' on the same source data. My best guess is refresh cadence and how aggressively they downgrade aging records — but I'd love to hear it from someone who actually knows.
