Apollo vs ZoomInfo vs Lusha - Email Accuracy Test 2026 (We Verified 50,000 Contacts)

Methodology: How We Tested 50,000 Contacts

Credibility in a study like this lives or dies on methodology, so here's exactly what we did.

Sample construction. We exported contacts from active, paid accounts on Apollo.io (Professional plan), ZoomInfo (SalesOS), and Lusha (Scale plan) between May 12 and June 2, 2026. To keep the comparison fair, we built matched samples: the same 12 industries, the same four company-size bands (1-50, 51-200, 201-1,000, 1,000+ employees), and the same seniority mix (roughly 40% manager, 35% director/VP, 25% C-level) from each platform. Final counts: 16,712 from Apollo, 16,655 from ZoomInfo, and 16,633 from Lusha - 50,000 total.

Deduplication and freshness. We removed cross-platform duplicates *within* each provider's sample but deliberately kept contacts that appeared on multiple platforms, because that's how real prospecting works - and it let us compare verdicts on identical addresses across providers (more on that later). Every export was verified within 72 hours of download, so platform-side staleness, not our lag, explains the results.

Verification standard. Every address ran through BounceZero's full pipeline - the same six checks available to any customer: live mailbox existence via SMTP conversation, 3-probe catch-all detection (which distinguishes true accept-all servers from selective ones), role account flagging, disposable domain screening, MX record validation, and spam trap detection. BounceZero operates at up to 99.8% accuracy in internal testing on SMTP-verifiable addresses against an industry benchmark of roughly 95%, which matters here: a verifier with a 5% error rate would introduce noise on the same order as the gaps we're trying to measure. Bulk batches of ~16K each processed in under 10 minutes via our [bulk email verification](/bulk-email-validation) engine.

Verdict categories. We bucketed every result into four classes: Valid (mailbox confirmed to exist, safe to send), Catch-all (server accepts everything; existence unconfirmable at SMTP level), Invalid (mailbox confirmed not to exist, or domain dead), and Risky/Unknown (spam traps, disposables, greylisting that persisted through retry, or servers that blocked verification). We counted role accounts (info@, sales@) separately as an overlay, since some teams knowingly email them.

What we did not do. We didn't send actual campaigns to these addresses - that would be abusive and unnecessary, since SMTP-level mailbox confirmation is the deliverability ground truth short of sending. We also didn't test phone numbers, intent data, or firmographic accuracy; this study is strictly about whether the email addresses these platforms sell will accept mail. One study, one question, answered properly.

The Headline Results: Valid, Catch-All, Invalid, Unknown

Here are the topline numbers across all 50,000 contacts. Percentages are of each provider's total sample.

Apollo.io (n=16,712): 64.9% valid - 18.2% catch-all - 12.8% invalid - 4.1% risky/unknown

ZoomInfo (n=16,655): 71.2% valid - 15.6% catch-all - 9.3% invalid - 3.9% risky/unknown

Lusha (n=16,633): 66.4% valid - 16.9% catch-all - 12.1% invalid - 4.6% risky/unknown

A few things jump out immediately.

First, ZoomInfo wins - but not by the margin its price implies. ZoomInfo's 71.2% valid rate beats Apollo by 6.3 percentage points and Lusha by 4.8. Its invalid rate of 9.3% is the lowest of the three. If you're paying a 10-20x price premium over Apollo, you're buying roughly six extra deliverable emails per hundred contacts. Whether that's worth it depends entirely on your economics per meeting booked - for enterprise ACVs it probably is; for volume-driven SMB motions it almost certainly isn't.

Second, invalid rates of 9-13% are campaign killers on their own. Mailbox providers start throttling and junk-foldering senders at bounce rates above 2%, and email industry data consistently shows domain reputation damage compounding once bounces exceed 4-5%. Every provider in this test ships raw data that would bounce at 3 to 6 times that threshold. This is the single most important number in the study: no platform's raw export is safe to send as-is.

Third, catch-alls are the hidden 15-18% nobody prices in. Between 15.6% and 18.2% of every sample sat behind accept-all mail servers. These aren't invalid - many are perfectly good addresses - but the platform can't know that, and neither can a naive verifier. BounceZero's 3-probe catch-all detection at least tells you *definitively* that a domain is accept-all rather than returning a mushy "unknown," which lets you make a deliberate risk decision instead of an accidental one. Across our verification data spanning 50M+ emails, roughly 55-65% of catch-all addresses at operating companies ultimately accept mail - meaning a blanket "delete all catch-alls" policy throws away real pipeline, while a blanket "send to all" policy inflates your bounce rate. Segment them.

Fourth, the risky/unknown tail is small but disproportionately dangerous. Around 4% of each sample included disposable domains, persistent greylisters, and - most alarmingly - confirmed spam traps: 41 traps in the Apollo sample, 19 in ZoomInfo's, and 33 in Lusha's. Forty-one addresses out of 16,712 sounds trivial until you remember that a single pristine spam trap hit can land your sending IP on a blocklist. Trap density this low is exactly why sampling-based QA misses it and full verification doesn't.

Why 'Verified' Means Something Different on Each Platform

The gap between platform claims and our results isn't fraud - it's a definitional mismatch that every buyer should understand.

Apollo marks emails with confidence indicators and claims ongoing verification, but its database strategy is fundamentally breadth-first: 275M+ contacts assembled from contributed data, public sources, and pattern inference. A meaningful share of Apollo emails are pattern-guessed - [email protected] generated from a known naming convention - and validated against the domain's mail server at some point in the past. Pattern inference works well at companies with rigid conventions and fails at companies with legacy formats, common-name collisions (two John Smiths), or recent migrations. Our data bears this out: Apollo's invalid rate at companies under 50 employees was nearly double its rate at enterprises, because small-company conventions are less predictable and small companies churn domains more often.

ZoomInfo invests heavily in first-party research, contributor networks, and its own verification layer, and it shows - the 71.2% valid rate is genuinely the best raw feed we tested. But ZoomInfo's refresh cycle still can't outrun B2B email decay, which industry data puts at 25-30% per year (people change jobs, companies rebrand, mailboxes get deprovisioned). Even a contact verified by ZoomInfo four months ago has a meaningful probability of having gone stale. We found that ZoomInfo contacts whose profiles showed a job change within the past 6 months were invalid at 2.4x the base rate - the platform often catches the job change before it catches the dead mailbox.

Lusha performed better than its reputation among enterprise buyers might suggest, landing between the other two at 66.4% valid. Its strength is direct-dial and contact-level data crowdsourced from its large user community; its weakness is coverage depth outside tech and professional services, where we saw both thinner match rates during export and higher invalid rates on what it did return.

The structural point: every data platform verifies at collection or refresh time; deliverability only exists at send time. A platform verifying an email in January tells you nothing certain about July. That's not a flaw unique to any vendor - it's physics of a database business. A provider-aware real-time check exists precisely to close that gap, and it's why point-of-send verification through a tool like BounceZero's [real-time API](/api-email-validation) will always outperform even the best database's internal QA. The platforms sell *discovery*; verification at send time is a separate job, and conflating the two is how bounce rates happen.

Breakdown by Company Size: SMB Data Is the Danger Zone

Averages hide the most actionable finding in this study: data quality degrades sharply as companies get smaller, and the degradation curve differs by provider.

Valid rates by employee band:

1-50 employees: Apollo 57.8% - ZoomInfo 63.1% - Lusha 61.5%

51-200 employees: Apollo 62.4% - ZoomInfo 68.9% - Lusha 65.2%

201-1,000 employees: Apollo 67.3% - ZoomInfo 73.6% - Lusha 68.8%

1,000+ employees: Apollo 70.1% - ZoomInfo 77.4% - Lusha 70.3%

The pattern is unambiguous: every provider is 9 to 14 percentage points worse on SMB contacts than on enterprise contacts. Apollo's SMB valid rate of 57.8% means that a raw SMB list from Apollo contains barely more than one deliverable email for every two contacts you export.

Why does SMB data rot faster? Three compounding forces. First, small companies die and rebrand more often - a 40-person startup is far more likely to shut down, get acquired, or change domains than a 5,000-person enterprise, and when the domain dies, every email at it dies simultaneously. We found 312 completely dead domains (no MX records) in the Apollo SMB segment alone. Second, employee tenure is shorter at small companies, so mailbox churn is higher. Third, data platforms invest research effort where their customers prospect most, and enterprise accounts get refreshed more aggressively than the long tail.

There's an inverted pattern worth noting on catch-alls, though: enterprise domains are far more likely to run catch-all or verification-hostile mail servers. In the 1,000+ band, catch-all rates hit 21-24% across providers - large companies increasingly deploy security gateways (Proofpoint, Mimecast) that accept-all at the edge specifically to defeat address harvesting. So while enterprise data has fewer hard bounces, it has more "can't confirm at SMTP level" addresses. This is where verification quality separates: BounceZero's 3-probe catch-all methodology plus provider-specific intelligence resolves a meaningful share of these to a confident verdict rather than dumping them into an unknown bucket, which matters enormously for ABM teams whose entire target list might live behind Mimecast.

The practical rule that falls out of this data: if your ICP is SMB, treat every platform export as roughly 60% deliverable until verified, and budget your list-building accordingly - export 1.6 to 1.7 contacts for every 1 you plan to actually sequence. If your ICP is enterprise, hard bounces are less of a threat, but your verification tooling's catch-all handling becomes the deciding factor in usable list size.

Breakdown by Industry: Where Each Provider Wins and Loses

We tagged all 50,000 contacts across 12 industries. Here are the most instructive segments, showing valid rates:

Software/SaaS: Apollo 71.6% - ZoomInfo 75.8% - Lusha 73.1%

Financial services: Apollo 61.2% - ZoomInfo 72.9% - Lusha 62.7%

Healthcare: Apollo 58.9% - ZoomInfo 68.4% - Lusha 60.2%

Manufacturing: Apollo 63.7% - ZoomInfo 71.5% - Lusha 61.9%

Retail/eCommerce: Apollo 60.4% - ZoomInfo 65.2% - Lusha 64.8%

Marketing/agencies: Apollo 69.8% - ZoomInfo 69.1% - Lusha 71.4%

Three findings deserve unpacking.

Tech data is everyone's best data. All three platforms cleared 71% valid in Software/SaaS - unsurprising, since tech workers are the most heavily prospected, most LinkedIn-active, and most represented in contributed-data networks. If your ICP is tech, the provider gap narrows to a few points and Apollo's price advantage becomes very hard to argue against.

ZoomInfo's premium is real in regulated and traditional industries. In financial services (+10.2 points over Apollo) and healthcare (+9.5 points), ZoomInfo's human-research investment shows up clearly. These are industries where employees are less visible online, naming conventions are less inferable, and pattern-guessing fails - exactly where a research-driven database earns its price. If you sell into banks, insurers, or hospital systems, this is the strongest data-driven case for ZoomInfo we found.

Lusha quietly wins the agency/marketing segment. Its 71.4% valid rate in marketing and agencies edged out both competitors - consistent with its user community skewing toward marketing and sales professionals who contribute and correct data in their own industry. Lusha also posted its most competitive numbers in retail/eCommerce.

Healthcare deserves a special warning. It was the worst or near-worst industry for all three providers, and it also carried the highest spam trap density in our study - 38 of the 93 total traps we found lived in healthcare segments. Hospital systems and health networks recycle addresses into traps aggressively, and clinicians churn between employers constantly. Email industry data suggests healthcare B2B lists decay at 30%+ annually versus the ~25% cross-industry average. If you prospect healthcare, verification isn't a best practice; it's survival - and you should re-verify lists older than 60 days, not the 90-day rule of thumb that works elsewhere.

The meta-lesson: "which provider has the best data" is the wrong question - the right question is "which provider has the best data for my ICP." The overall winner (ZoomInfo) loses specific segments, and the overall value leader (Apollo) is nearly tied at the top in tech. Match the tool to the territory, then verify regardless.

The Overlap Test: 4,200 Identical Emails, Three Different Claims

Because we intentionally preserved cross-platform duplicates, we ended up with 4,213 email addresses that appeared in at least two providers' exports - a natural experiment in how platforms disagree about the same reality.

The results were sobering. For addresses present on both Apollo and ZoomInfo where both platforms labeled the email "verified" or high-confidence, BounceZero found 11.3% were actually invalid or dead-domain. When the platforms *disagreed* - one confident, one hedging - the confident platform was right only about 60% of the time, barely better than a coin flip weighted by base rates.

Even more interesting: the platforms' confidence labels correlated with each other more than either correlated with ground truth. That's a signature of shared upstream sources - when both platforms ingest the same contributed dataset or the same historical verification result, they inherit the same errors and validate each other's mistakes. Buying two data platforms and keeping only the contacts they agree on feels rigorous, but our overlap data shows it removes less risk than teams assume: of the 4,213 overlapping addresses, dual-platform agreement improved the valid rate by only about 5 points over single-platform data (to roughly 74%), still far above the 2% bounce threshold that mailbox providers punish.

We also checked verdict asymmetry by direction. Addresses that Apollo had but ZoomInfo lacked were valid 62.1% of the time; addresses ZoomInfo had that Apollo lacked were valid 73.8% of the time. In other words, ZoomInfo's *exclusive* coverage is higher quality than its overlapping coverage, while Apollo's exclusive long-tail is where its quality dips most. This matters if you run a waterfall enrichment stack: sequencing ZoomInfo-first, Apollo-fallback gives you Apollo's weakest slice as your fallback layer, which is precisely where per-contact verification pays for itself most.

The practical takeaway is one we see constantly in BounceZero's verification data across 50M+ emails: provider consensus is not verification. Two databases agreeing that an email existed at some point in the past is a statement about the past. An SMTP conversation with the receiving mail server immediately before you send is a statement about now. Only one of those keeps your bounce rate under 2%. Treat platform confidence scores as a *prioritization* signal - which contacts to enrich or research first - never as a *deliverability* signal.

What This Does to Your Deliverability: The Math Nobody Runs

Let's translate these percentages into what actually happens to a sending domain, because the damage is nonlinear and most teams discover it only after the fact.

Suppose you export 10,000 Apollo contacts and load them straight into your sequencer. Based on our results, roughly 1,280 will hard bounce (12.8% invalid). Mailbox providers - Google and Microsoft above all - evaluate senders on rolling bounce rates, and email industry data consistently shows reputational penalties beginning around 2% bounces, with severe filtering above 5%. You'd be sending at 12.8%: more than 6x the severe threshold, from the very first send.

The consequences cascade in a specific order. First, your inbox placement degrades silently - opens drop 20-40% before you see a single alarming metric, because filtering happens before bouncing in most providers' logic. Second, your bounce rate becomes self-reinforcing: as reputation falls, providers reject more of your mail with 4xx/5xx responses, which your sequencer counts as more bounces. Third, if your list contained even a handful of the spam traps we found at 0.1-0.25% density across all three platforms, you risk blocklisting - at which point you're not optimizing a campaign anymore, you're doing domain surgery: warming new domains, migrating sequencers, and losing 4-8 weeks of pipeline.

Now run the same 10,000 contacts through verification first. At BounceZero's pricing of $3 per 1,000 verifications, that's $30. You'd remove ~1,280 invalids, flag ~1,820 catch-alls for segmented low-volume treatment, and strip the disposables and traps entirely. Your sendable core of ~6,500 confirmed-valid addresses will bounce at under 1% - comfortably inside every provider's tolerance. Total cost to protect the campaign: $30 and about ten minutes of processing.

Compare that to the cost of the alternative. A burned sending domain means new domain registration, 3-6 weeks of warmup at throttled volume, and - using conservative outbound benchmarks of one meeting per 200-400 deliverable emails - dozens of lost meetings during the recovery window. For a team whose average deal is worth even $5,000, a single deliverability incident costs orders of magnitude more than a year of verification. Our [ROI calculator](/roi-calculator) lets you run these numbers against your own volume and deal size, but the asymmetry is so extreme that the calculation is almost a formality: verification is the cheapest insurance in outbound. The question isn't whether $30 per 10K contacts is worth it - it's why anyone sends without it.

Budget for the Trim: Expect to Lose 15-30% of Every Export

One of the most consistent operational mistakes we see is teams sizing their list-building to their sequencing targets one-to-one: "we need 5,000 prospects this quarter, so export 5,000 contacts." Our study data says that math is wrong by a wide margin, and planning around the real numbers changes both budgets and timelines.

Across all three providers and all segments, the hard trim - invalids, disposables, traps, and dead domains you must delete - ran 12 to 17% of raw exports. Add catch-alls, which most teams either exclude or route to a separate cautious sequence, and the effective trim reaches 25 to 35%. Even if you send to your best catch-alls, a realistic planning assumption is: for every 100 contacts you export, 65-75 are cleanly sequenceable.

This has three concrete budgeting implications.

Credit budgeting. Data platforms charge per contact revealed or exported. If you need 5,000 sendable contacts, budget 6,700-7,700 export credits, not 5,000. On Apollo this is cheap; on ZoomInfo, where credits are expensive and contractually capped, under-budgeting the trim is how teams run out of credits in month eight of a twelve-month contract. Negotiate credit volumes with the trim built in.

Verification budgeting. You verify the raw export, not the trimmed list - so verification spend scales with exports. At $3 per 1,000, verifying 7,500 contacts to net 5,000 sendable costs $22.50. This is small enough that it should never be the constraint, but it belongs in the stack budget as a standing line item, not an ad-hoc purchase. Teams doing continuous prospecting at 50K+ contacts monthly should look at [bulk verification](/bulk-email-validation) - dashboard jobs of up to 1,000,000 addresses process in 5-10 minutes, which makes a weekly verify-everything cadence operationally trivial.

Pipeline math. Most importantly, the trim propagates into revenue forecasting. If your model assumes 5,000 sends > 1.5% reply rate > 30 meetings, but you actually netted 3,600 sendable contacts from your 5,000 exports, your meeting forecast was silently 28% too high before the first email went out. SDR teams miss quota on this arithmetic error constantly, and it gets misdiagnosed as messaging or rep performance.

A final planning note on decay: the trim isn't a one-time event. Email industry data puts B2B list decay at roughly 2-2.5% per month. A list you verified in January carries ~12-15% new invalids by July. Standing policy should be: verify on export, and re-verify anything older than 90 days before it re-enters a sequence (60 days for healthcare and SMB-heavy lists, per our industry breakdown above). Fresh verification is cheap; stale confidence is not.

Recommendations by Use Case: High-Volume Cold Email vs. ABM

The right provider - and the right verification posture - depends on your motion. Here's how we'd translate this study into stack decisions.

High-volume cold email (500+ sends/day, SMB/mid-market ICP). Apollo is the rational choice despite the lowest valid rate in our test. At high volume, your cost per *deliverable* contact is what matters: Apollo's per-contact cost is low enough that even after trimming 35% of an SMB-heavy export, your net cost per sendable contact undercuts ZoomInfo by 5-10x. But this motion is also the most deliverability-fragile - you're sending from multiple warmed domains where a bounce spike kills weeks of warmup investment. The non-negotiables: verify every export before it touches a sequencer (Apollo's 12.8% invalid rate makes this existential, not optional), exclude catch-alls entirely or route them to a dedicated low-volume domain, and re-verify any list segment older than 60 days given SMB decay rates. At this volume, wire verification into the workflow rather than doing manual CSV round-trips - BounceZero's [API](/api-email-validation) returns verdicts with timing that varies by provider and verification path on average, fast enough to verify at the moment a contact enters your sequencer, and bulk endpoints handle the big weekly exports.

Account-based marketing (small target list, enterprise ICP, high ACV). ZoomInfo's premium is justified here: its 77.4% valid rate in the 1,000+ employee band, plus its clear lead in financial services and healthcare, means fewer research hours wasted per target account. But the ABM challenge isn't invalids - it's the 21-24% enterprise catch-all rate. When your entire campaign is 300 carefully chosen contacts at 40 accounts, discarding a quarter of them as "unverifiable" isn't acceptable. This is where verification depth beats verification existence: BounceZero's 3-probe catch-all detection and provider-specific handling for Microsoft 365 and gateway-fronted domains recover confident verdicts on a large share of addresses that simpler verifiers mark unknown. For ABM, verify individually, keep confirmed-valid and high-confidence catch-alls, and put genuine unknowns through manual research rather than deletion - at enterprise ACVs, ten minutes of a rep's time per ambiguous contact is a fine trade.

Mid-market hybrid and agency motions. Lusha earns a place either as a primary for marketing/agency ICPs (where it posted the best valid rate in our test) or as a waterfall enrichment layer behind Apollo. If you run waterfall enrichment across multiple providers, remember the overlap finding: consensus between providers is weak evidence, so verify the merged output, not the inputs.

Everyone, regardless of stack: the provider decision moves your raw valid rate between 65% and 71%. Verification moves your *sent* valid rate to 99%+. One of those decisions costs thousands per year and gets debated for weeks; the other costs $3 per 1,000 and gets skipped. The leverage is wildly mispriced in most teams' attention.

How to Run This Test on Your Own Data (In Under 15 Minutes)

You shouldn't take our sample as gospel for your specific ICP - data quality varies by segment, and the strongest version of this study is the one you run on your own exports. Here's the exact process, which takes less time than reading this article did.

Step 1: Export a representative sample. Pull 1,000-5,000 contacts from your data platform using your real ICP filters - not a random slice, but the actual searches your SDRs run. If you use multiple providers, export matched samples from each with the same filters so the comparison is apples-to-apples. Export fresh; don't reuse a CSV from last quarter, or you'll be measuring decay on top of platform quality.

Step 2: Verify the full sample. Upload the CSV to BounceZero - a 5,000-contact batch costs $15 and processes in a few minutes. You'll get per-address verdicts across all six checks: mailbox existence, catch-all status, role, disposable, MX, and spam trap. If you just want to sanity-check the process first, the free tier includes 100 verifications per month with no credit card, enough for a quick pilot on your hottest segment before committing to a full audit.

Step 3: Compute your four numbers. Valid %, catch-all %, invalid %, risky %. Compare against our benchmarks: if your invalid rate is above ~13%, your segment is worse than the worst provider average we measured and you should investigate whether your filters are reaching into stale corners of the database (very small companies, long-tail industries, contacts with old "last updated" dates). If you're under 9%, your segment is better than anything we measured - enjoy it, but still verify, because 9% is still 4.5x the safe bounce threshold.

Step 4: Segment your sends accordingly. Confirmed valid > main sequences at full volume. Catch-alls > separate campaign on a secondary domain at reduced daily volume, watching bounce rates per domain (not per campaign) as your early-warning signal. Invalid and risky > delete, and if your platform offers data-quality credits or refunds for bad contacts, this report is your evidence.

Step 5: Make it a standing process, not an event. The teams with durably healthy deliverability all converge on the same pattern: verification happens automatically at two moments - when a contact enters the system (via API or bulk upload) and when a contact re-enters a sequence after sitting idle 90+ days. Once it's wired into the workflow, data quality stops being a quarterly fire drill and becomes an invisible property of the pipeline.

Run this once and you'll have provider-level accuracy numbers for *your* ICP that no review site or vendor claim can give you - and a defensible, data-backed answer next time someone asks why the data budget looks the way it does.

The Bottom Line: Verify Regardless of Source

After 50,000 verifications across three weeks, the findings compress into five statements worth remembering.

1. ZoomInfo has the best raw data; nobody has safe raw data. ZoomInfo's 71.2% valid rate leads the field, and its edge is largest exactly where you'd hope - enterprise accounts and regulated industries. But 71.2% valid still means a 9.3% hard-bounce rate on raw sends, nearly 5x the level at which mailbox providers start punishing you. "Best" and "good enough to send unverified" are different claims, and no provider in this test meets the second one.

2. Apollo is the value play, with the sharpest need for verification. Its 64.9% valid rate and 12.8% invalid rate are the field's weakest, but its cost per deliverable contact - after verification trims the export - remains the lowest for volume motions, especially in tech where its data nearly matches ZoomInfo's.

3. Lusha is segment-dependent and underrated in its home turf. Best-in-test for marketing/agency ICPs, middling elsewhere. As a waterfall layer or a primary for the right segment, it earns its seat.

4. The gaps between providers are smaller than the gap between verified and unverified. Provider choice moves your valid rate by ~6 points. Verification moves your *sent* bounce rate from 9-13% to under 1%. Most teams spend weeks on the first decision and skip the second entirely - that priority order is backwards, and it's the single cheapest fix in outbound.

5. Data decays faster than contracts renew. At 25-30% annual decay, the contact verified when your data contract started is a coin flip by renewal time. Verification isn't a one-time cleanup; it's a standing layer between any database and your sending domain.

We built BounceZero to be exactly that layer: up to 99.8% accuracy in internal testing on SMTP-verifiable addresses against an industry benchmark of ~95%, six independent checks per address including 3-probe catch-all detection and spam trap screening, provider-dependent API response time, dashboard jobs of up to 1,000,000 addresses processed in 5-10 minutes, and pricing at $3 per 1,000 that makes "verify everything" a rounding error next to your data spend. Whichever platform wins your data budget this year, the addresses it hands you deserve one final check before they represent your domain to the world's mail servers. The 100 free monthly verifications are enough to test the difference on your own list today - no credit card, no sales call, just verdicts.