Clay Enrichment Providers Ranked: 500-Record Test on B2B Contact Coverage

Six months ago I moved a client's outbound enrichment stack from a custom waterfall script into Clay. The pitch made sense: instead of stitching together API calls to six different providers, you configure the cascade inside Clay and it handles the fallthrough logic. Cleaner ops, faster iteration.

What nobody told me clearly upfront is that the providers Clay connects to are not equal. Two of the six I configured gave me coverage so low I'd have been better off skipping them entirely. One provider I initially dismissed ended up being the strongest for the specific ICP I was working with. The differences weren't close.

Here's what I found across 500 records and why the defaults Clay suggests aren't necessarily the right order for your list.

The Test Setup

I pulled 500 LinkedIn profiles across three employee-count buckets:

• 10–50 employees (seed to early Series A, mostly SaaS)
• 51–200 employees (Series A to B)
• 201–1,000 employees (Series B to C, some bootstrapped)

All companies were B2B SaaS or SaaS-adjacent. Founding dates ranged from 2018 to 2023, which skews toward companies that weren't heavily indexed a few years back. For each profile I had a verified email address from prior outreach history or CRM confirmation — that was my ground truth.

I ran each record through the following providers inside Clay, in isolation (no waterfall, just single-provider lookups) to see what each could actually return:

• Apollo (via Clay's Apollo enrichment integration)
• People Data Labs / PDL (Person Enrichment endpoint)
• Clearbit (Person API)
• Hunter.io (Email Finder)
• Lusha (Enrichment API)
• Snov.io (Email Finder by Social URL)

I measured three things per provider: email found rate (did it return any email), verified delivery rate (NeverBounce / ZeroBounce clean on the returned email), and job title current accuracy (was the title it returned still the person's current role at the time of testing, cross-checked against LinkedIn).

Email Coverage: PDL and Apollo Lead, Clearbit Trails Hard

The raw coverage numbers surprised me more than the accuracy numbers. Starting from LinkedIn profile URLs and company domains, here's what each provider returned:

Provider	Email Found Rate	Verified Deliverable	Current Title Accuracy
PDL	71%	82%	76%
Apollo	68%	84%	79%
Lusha	54%	88%	81%
Hunter.io	49%	91%	83%
Snov.io	47%	79%	72%
Clearbit	31%	93%	88%

Clearbit's coverage is genuinely that low on SaaS companies under 200 employees. Its accuracy on what it does return is the highest in the table — 93% deliverable, 88% title accuracy — but 31% coverage means you're leaving 69% of your list unresolved if you stop there. For the specific profile of early-to-mid-stage SaaS, Clearbit's database has historically skewed toward companies with more public web presence. A 2020-founded SaaS company with 40 employees and a sparse domain footprint often doesn't make it in.

Hunter.io showed a similar pattern: excellent accuracy on what it finds (91% deliverable is the highest after Clearbit), but the pattern-inference approach that works well on domains with lots of public signals loses steam on smaller companies with less crawlable employee data.

The PDL vs Apollo Question

These two had the widest coverage and the closest numbers, so I dug into where they diverged.

PDL returned more records on companies founded 2020–2022. My hypothesis: PDL's aggregation model pulls from more diverse sources — job boards, conference speaker lists, professional association exports — that pick up newer companies earlier than web-crawl-dependent approaches. On companies with 10–50 employees, PDL coverage was 74% versus Apollo's 64%.

Apollo flipped the advantage on larger companies in the 200–1,000 range: 73% versus PDL's 68%. Apollo's reach in the mid-market has been built through its user-contributed data model — sales reps using Apollo verify contacts in real time, which improves accuracy for the company sizes where Apollo's user base concentrates.

The title accuracy gap matters for a different reason. Apollo's 79% on current title accuracy versus PDL's 76% sounds small, but a 3-point difference across a 500-record campaign means ~15 contacts where you're calling someone a "Head of Sales" when they're now the VP. That's a real mistake in an outbound sequence, not a rounding error.

Neither one is the clear winner. My rule now: for early-stage targets, PDL goes in the first slot. For mid-market, Apollo moves up.

Where Lusha Earns Its Cost

Lusha has a reputation for European phone number coverage, and that reputation is deserved in my experience — I covered that separately when testing DACH contacts. What surprised me here was its precision on verified emails even at 54% coverage. Its 88% deliverable rate is the third-highest in the table.

Lusha runs its own verification against email infrastructure at the time of the lookup, rather than serving cached verifications. That freshness shows up in deliverability. If your campaign has a very low bounce-rate tolerance (think inbox warming, or a domain you can't afford to burn), Lusha as a second-tier fallback for verified email matters more than raw coverage numbers suggest.

The credit cost inside Clay for Lusha enrichment is higher than Apollo or PDL per lookup. If you're building a high-volume stack, that math works out to paying more for fewer but cleaner records. Worth it for specific verticals, not worth it as a broad first-tier.

Snov.io: Lower Accuracy Than I Expected from Social URL Input

I ran Snov.io via the Social URL endpoint — feeding it LinkedIn profile URLs directly — which is supposed to be its strength relative to domain-only lookup. The 47% coverage and 79% deliverable rate were both lower than I expected.

Part of this is the test design: all 500 records were SaaS companies, which is Hunter's strongest segment per my prior testing. Snov tends to outperform Hunter on professional services domains where Hunter's pattern inference loses confidence. On tech-first SaaS, Snov's position in this stack is probably third-tier, not second.

The social URL approach also hit Clay's rate-limit handling awkwardly on a couple of batch runs — Snov's API throttling behavior inside Clay caused some retries that weren't clearly surfaced in the error logs. Workable, but add buffer time if you're running large batches.

Provider Ordering Matters for Cost, Not Just Accuracy

The coverage and accuracy numbers above tell you what to expect from each provider. What they don't tell you is how much each costs in Clay credits, and that changes the ordering logic.

Inside Clay, providers don't all bill equally. At the time I ran this test, rough credit costs per lookup were:

• Hunter.io: ~1 credit
• Apollo: 1–2 credits
• Snov.io: 1 credit
• PDL: 2–3 credits
• Lusha: 3–5 credits
• Clearbit: 2–3 credits
• ZoomInfo: 5–10 credits (I didn't include it in my six-provider test, but it's available in Clay)

In a waterfall, every provider in the cascade consumes credits when it's called — even if the record already has a value from a prior tier (unless you configure an explicit "skip if not null" condition). Most people set that condition correctly, but I've seen Clay templates in the wild where it's missing and every provider runs on every record regardless.

Assuming you do configure the skip-if-found logic: the cost-optimal ordering puts cheap, high-coverage providers first, expensive providers last. So the order changes depending on whether you're optimizing for credit spend or accuracy.

If I was minimizing credits: Hunter.io → Apollo → Snov.io → PDL → Lusha. Hunter's 1-credit lookups absorb the easy finds cheaply. Apollo catches the mid-tier. Expensive providers only run on records that survived three cheaper passes.

If I'm optimizing for accuracy and don't mind the cost: Lusha → PDL → Apollo → Hunter.io. Lusha's 88% deliverable rate upfront means less bounce risk, even if fewer records return from the first pass.

The right optimization depends on your volume and bounce tolerance. For a 200-record campaign on a cold domain where deliverability risk is high, pay for accuracy upfront. For a 10,000-record list where you're budget-constrained and have a good inbox warming buffer, go credit-cost-first.

What Clay Actually Adds (and What It Doesn't)

Clay as an orchestration layer adds real value: visual waterfall configuration, automatic fallthrough when a provider returns null, a readable audit trail showing which provider returned which field. For ops teams that don't want to maintain custom enrichment code, it's worth the platform cost.

What it doesn't add is magic. The data quality is entirely downstream of the providers you configure. Clay can't make Clearbit's coverage better on small companies. It can't fix PDL's title staleness. The platform is a router, not a data source.

The provider defaults Clay suggests in its templates are based on general popularity, not your ICP. Before you set a cascade live, run a sample of your actual list through each provider in isolation, the way I did here. The ranking you find will be different from mine if your ICP is enterprise or if you're targeting industries outside SaaS.

One gap I hit that Clay doesn't solve: starting from social handles rather than LinkedIn URLs or company domains. If you're enriching from a Twitter handle or Facebook profile where the company domain isn't known, none of the six providers above have a clean path inside Clay's current interface. You need a separate enrichment step before the cascade.

What I Actually Use

For SaaS lists starting from LinkedIn URLs, my current order inside Clay is: PDL → Apollo → Lusha → Hunter.io. That combination gets me above 90% email coverage on typical SaaS IPCs before I hit the fourth tier, and the accuracy at each fallthrough stage is predictable.

For mid-market (200+ employees), I swap the first two: Apollo → PDL.

I skip Clearbit as a primary enrichment tier for anything under 500 employees. It goes in as an intent-signal and technographic layer — it's excellent there — but not for contact discovery.

When I'm working from social profiles (Twitter/Facebook handles) instead of LinkedIn URLs or domains, I step outside Clay and run those through Ziwa first. It handles the social-handle-to-email path faster than resolving the company domain manually and then re-entering the Clay waterfall. The output slots back into the same enrichment stack once I have a domain or verified email to work with.

RocketReach occasionally appears in comparisons like this — I've tested it as a fifth tier and found marginal lift on executive-level contacts above VP. Below VP title, the cost per marginal find isn't worth it for most lists.

The right cascade depends on your ICP. If you're in enterprise B2B, the Clearbit accuracy numbers look a lot more attractive when your list is all Fortune 1000. If you're doing high-volume SMB outreach, you care more about PDL's coverage than Lusha's precision. Run the sample test before you commit to a configuration.