Apollo vs ZoomInfo vs Lusha: Testing Data Quality

What each vendor currently publishes about its data

The honest starting point for a data-quality comparison is that all three descriptions below are vendor-reported. The linked pages are vendor-published and do not provide an independent audit of their headline database totals. None substitutes for testing against your own ICP.

ZoomInfo. ZoomInfo's privacy policy describes four categories of source: web sources gathered by its search technology, information contributed by customers and users, licensed data from third-party providers, and an in-house research team. It also describes a Contributory Network through which customers may choose to share certain integration data, managed from the admin portal.

Apollo. Apollo's own pages publish different figures depending on where you look. Its contact search page currently advertises a database of 240M+ contacts, while its data methodology page leads with monthly metrics instead: 5.3M contacts added and 150M refreshed per month, sourced from a contributor network, web crawling, third-party providers, and verification signals from its own engagement tools. The 98% email-accuracy figure on the same page is Apollo's self-reported number, produced by its own verification process.

Lusha. Lusha now positions itself as a B2B data platform rather than the Chrome-extension lookup tool it started as. Its data page publishes 300M+ contacts, 30M+ companies, 152M+ emails, and 300M+ direct dials, states that it does not scrape LinkedIn or social networks, and lists GDPR, CCPA, ISO 27001/27701, and SOC 2 Type II among its compliance certifications. Those certifications describe its compliance posture, not the accuracy of any individual record.

Why published database sizes do not establish accuracy

Three problems make headline totals unusable as a quality comparison.

First, the definitions differ. A "contact" may or may not require a verified email; a "direct dial" may be a mobile or a switchboard line; a refreshed record is not necessarily a correct one. Nothing forces the three vendors to count the same way, so 240M and 300M are not points on the same scale.

Second, the figures move, even within one vendor's own site: Apollo's pages currently carry different database descriptions, which is normal for marketing sites and why no single figure should be quoted as an audited fact.

Third, and most important, size says nothing about field accuracy on the slice you buy data for. A 300M-contact database with weak coverage of 200-person German manufacturers is worth less to a team selling into that segment than a smaller database that happens to be strong there.

The same discipline applies to comparative claims you will hear in sales cycles: that one vendor's records decay slowest, another's emails go stale fastest, or a third's mobile numbers stay accurate longer. We found no published test population behind any such ranking from these three vendors. Treat every version as a hypothesis your evaluation can test, not a fact to repeat.

A reproducible evaluation: the blinded offline bake-off

The common advice to pull the same contacts from every vendor and blast the same sequence at them measures the wrong things. Opens are heavily distorted by machine activity, a live test mixes data accuracy with copy and deliverability, and the same person may receive competing test sends. A defensible evaluation happens offline, against ground truth you control:

1. Build a stratified sample of known contacts. Use people whose employer, title, email, and phone you have independently verified: recent closed-won champions, partners, or contacts confirmed directly. Stratify by region, company size, and role so the sample resembles your ICP.
2. Give every vendor the same identifiers at the same time. Same names, same companies, same day, so no provider gets a freshness advantage.
3. Score blind. Have the scorer see fields, not vendor names. Score coverage (was a value returned), field accuracy against ground truth, duplicates, false positives, apparent recency, and cost per verified field.
4. Report by segment. A vendor that leads on US enterprise titles could trail on EMEA mobile numbers. A single blended score hides the information you are paying to learn.
5. Keep any live-outreach experiment separate. If you also want to measure pipeline impact, run it afterwards with non-overlapping, randomised groups, so no prospect hears from you twice and accuracy is not confounded with copy or timing.

Two hundred contacts can be a practical pilot, but it may be too small for stable conclusions within several strata. Choose the sample size from the number of segments and the minimum difference that would change the buying decision. Without independently verified fields, you are grading vendors against each other's guesses.

Workflow, pricing, and integration questions

Pricing models differ in kind, not just level, so precise comparative fractions rarely survive a real quote.

• Apollo publishes per-user plan pricing publicly, which makes it easy to model for a small team.
• ZoomInfo generally sells custom contracts through its sales team. Without a documented quote for your seat count and modules, any claimed multiple between the two is speculation.
• Lusha uses credit-based pricing, and the exchange rate matters: its pricing page currently charges 1 credit to reveal an email and 10 credits for a phone number. At equal reveal volumes, phone-number reveals consume ten times as many credits as email reveals, which can materially change the effective price per usable contact.

On orchestration: Clay documents integrations with all three providers (ZoomInfo, Lusha, and Apollo), typically requiring your own account and API access with the underlying vendor. That makes a waterfall (query one provider, fall back to the next for missing fields) straightforward to assemble. It is a reasonable architecture to test; your bake-off's cost-per-verified-field numbers will show whether it earns its complexity. We found no public evidence for the popular claim that top teams universally run three-provider waterfalls or that doing so reliably lowers cost.

Privacy, suppression, and deliverability checks

Compliance questions belong in the evaluation, not after the signature. Ask each vendor to document how it handles opt-outs and suppression requests, what its lawful basis is for records in the regions you sell into, and how quickly removals propagate. Do not assume a specific regulation mechanically explains a coverage gap: whether a provider has usable mobile numbers for your EU segments is an empirical question your regional strata will answer, and your own obligations as the sender are a matter for counsel.

On deliverability, rely on the guidance that exists. Google's sender guidelines require keeping spam rates in Postmaster Tools below 0.3%, advise staying under 0.1%, and recommend reducing volume when messages start bouncing or being deferred. Google publishes no numeric bounce-rate threshold and no fixed recovery timeline; treat rising bounces as a warning to act on, not a cliff with a known edge. Whichever vendor leads your test, consider an independent verification step and monitor bounces, deferrals, complaints, and provider responses before increasing volume through your cold email sequencer.

Reading the results without declaring a universal winner

Allow for a split decision. Segment-level reporting may show different providers leading on different fields, regions, or company sizes, which is why a single vendor-deck ranking is not enough. Make the renewal decision per use case: the provider with the best verified-email coverage on your core segment may not be the one supplying mobile numbers for your pre-call research.

Document the method, keep the scoring sheet, and re-run the bake-off before each renewal. Databases, pricing, and packaging change often enough that a result from last spring describes last spring.

The takeaway

• Quote vendors' numbers as vendor-reported, and test the slice you buy. Published totals are not on a common scale and move even within one vendor's site.
• Evaluate offline against ground truth. A blinded, stratified bake-off scoring coverage, accuracy, duplicates, and cost per verified field beats any live send as a data-quality measure.
• Buy per segment, not per brand. Expect split results, price the credit and contract models against your channel mix, and re-test before renewal instead of extending on reputation.

Source check: 26 July 2026. Vendor database figures, pricing mechanics, sourcing descriptions, and integration coverage were checked against the linked first-party pages. The bake-off method is SalesTap editorial guidance, not a measured benchmark.