Datanyze vs Lusha: Data Enrichment Tool Comparison

Datanyze vs Lusha: Data Enrichment Tool Comparison

One of these two vendors would not let us read its pricing page. Datanyze answered HTTP 403 to every route we tried on 2026-08-28, over plain HTTP and through a headless browser. That is a failed fetch rather than evidence about its prices, and it changes what this comparison can honestly claim, so it is worth saying at the top rather than burying in a footnote.

The short answer

Pick Lusha if you want verified contact details at volume with a published, self-serve ladder. Its pricing page runs from a free tier at 40 credits a month to Premium at $259.95 a month billed yearly for 40,800 credits a year, with seats bundled rather than charged.

Pick Datanyze if technographic data is the point. Its historic positioning is identifying the technologies a company uses, which is a different job from finding a person's direct dial, and its published ladder has always sat well below Lusha's.

The figures we can give for Datanyze come from our own pricing dataset rather than from its page, and we flag them as such throughout.

What we could read, and what we could not

Lusha pricing

Three mechanics on that page are worth more than the headline rates. Credits are granted upfront for the whole year rather than monthly, so an unused month does not roll forward and a heavy month is not throttled. Seats are bundled rather than charged: one on Starter, two free on Pro, five free on Premium. And the annual discount is not flat across the ladder, running 25%, 30% and 35% as you climb, which means the annual commitment is worth progressively more at the top.

Source: Lusha pricing

Datanyze pricing

Datanyze answered HTTP 403 to every route tried on 2026-08-28, over plain HTTP and through a headless browser, so nothing below is quoted from its live page. These figures come from our pricing dataset, and we publish them labelled rather than dropping them, because a stale figure named as a stale figure is more useful to a reader than silence.

Treat those as a starting point for a conversation with Datanyze rather than as a quote. A non-200 is never evidence that a figure has changed, and it is equally never evidence that it has not.

Source: Datanyze pricing, which did not respond to us

$37.45Lusha Starter, yearly

4,800 credits a year, 1 seat

$259.95Lusha Premium, yearly

40,800 credits a year, 5 free seats

$29Datanyze Nyze Pro 1

dataset figure, page unreachable

A worked example on cost per credit

Here is an invented but arithmetically honest worked example. The volumes are made up to compare the two ladders on a single axis. No part of it comes from a customer, and the Lusha figures are the published ones.

Lusha grants credits annually, so the useful comparison is cost per credit across a year:

The per-credit cost falls as you climb, which is the ordinary shape, and it falls slowly: Premium is about 19% cheaper per credit than Starter while costing about seven times as much. So the reason to move up the Lusha ladder is the credit ceiling and the bundled seats rather than the unit economics.

We cannot run the same table for Datanyze, because the credit allowances behind its tiers are not something we could read today. That absence is the practical consequence of the 403: a comparison of unit costs needs both sides, and only one of them answered.

What a credit buys, and how fast it goes stale

The word credit does a lot of work on both vendors' pages and it is worth unpacking, because it is the unit the whole purchase turns on.

On Lusha a credit reveals a contact. Its page attaches the allowance to the year rather than the month, which changes the buying decision in a specific way: a team that builds lists in bursts, three heavy weeks and then nothing, is not throttled the way a monthly allowance would throttle it, and a team with steady low usage gets no benefit from the flexibility and simply pays for headroom.

What no credit buys is durability. B2B contact data decays continuously as people change jobs, and a record that was correct when revealed is not necessarily correct three months later. That matters commercially rather than philosophically: a list enriched at the start of a quarter and sent at the end of it will bounce at a higher rate than one enriched the week it was used, and bounces cost sender reputation on top of the wasted credit.

The practical consequence is that buying a large annual allowance and drawing it down slowly is worse than it looks on a spreadsheet. Enrich close to the send. That argues for sizing the tier on what you will actually consume in a quarter rather than on the cheapest per-credit rate available.

It also argues for verifying at send time regardless of provider. Neither vendor is claiming a record stays fresh, and neither should be blamed for the decay; what the buyer controls is how long the gap between enrichment and sending is allowed to get.

They are not really doing the same job

Both of these appear on data enrichment lists, and the shared category hides a real difference in purpose.

Lusha is a contact data tool. The unit it sells is a verified email address or a phone number for a named person, and its page is organised around credits, seats and buying-intent topics.

Datanyze is a technographics tool. Its historic proposition is telling you what software a company runs, which is a targeting signal rather than a contact detail. A team building a list of companies using a particular platform is doing something Lusha does not primarily offer.

Lusha lists

• Verified emails and phone numbers as the core unit
• Credits granted upfront for the year rather than monthly
• Bulk enrichment and CSV enrichment
• Buying intent topics, 5 on Starter and unlimited results on Premium
• Contact and company lookalikes
• API access from the Pro tier
• A browser extension for professional networks

Datanyze lists

• Technographic data on the tools a company uses
• A Chrome extension for prospecting
• Contact data alongside the technology signal
• A free tier
• Integrations with Salesforce, HubSpot, Outreach and Salesloft
• A published tier ladder well below Lusha's at the top
• Company-level firmographic filters

The Datanyze column is drawn from its published integration and feature listings rather than from today's page, for the reason given above. Read it as orientation rather than as a current specification.

Who should pick which

Datanyze or Lusha

• ✓Yes: You need verified emails and direct dials at volume. Pick Lusha.
• ✓Yes: You are targeting companies by the software they run. Datanyze is the technographics tool of the two.
• ✓Yes: You want a published, self-serve price you can read before talking to anyone. Lusha publishes one today; Datanyze did not respond.
• ~Depends: You need several seats. Lusha bundles 2 free on Pro and 5 on Premium rather than charging for them.
• ✓Yes: Your credit usage is lumpy across the year. Lusha grants the full annual allowance upfront.
• ~Depends: You want buying intent signals in the same tool. Lusha publishes an intent topic allowance per tier.

Switching between them

Data tools are easier to change than sending platforms, because nothing about your sending infrastructure depends on them. Three things still catch people.

Credit accounting does not transfer. Lusha grants a year of credits upfront. Leaving mid-year means walking away from the unused balance, which is a real cost that a monthly-billed competitor does not impose. Time the move to a renewal.

Re-verify before you send. Contact data ages, and a list enriched six months ago by either vendor is not a list you should send to today without checking. The migration is a good moment to re-verify rather than to trust an export.

Keep the technographic signal separate from the contact record. If you are moving away from Datanyze, the technology-stack field is the thing you will miss and it is not something a contact tool replaces. Export the signal before you lose access to it, and decide deliberately whether the targeting depended on it.

One practical note from today: if you are evaluating Datanyze and its site does not answer, that may be a network-level block rather than an outage. Ours was consistent across plain HTTP and a headless browser, which usually points at bot filtering rather than at the vendor being down.

Where enrichment sits in a cold outbound stack

Enrichment is upstream of everything. It decides who is on the list and whether the address you have for them is real, and no amount of sending sophistication recovers from getting either wrong.

Two consequences for how you should read the credit arithmetic above. Every credit spent on a person you should not have targeted is spent twice, once on the data and once on the sending capacity it consumes, so the cheaper credit is not automatically the better buy if the targeting behind it is loose.

And because we send one message per prospect per campaign, with no bumps and no thread replies, a wrong or stale address is not something a later touch corrects. The first send is the whole campaign for that person, so verification quality matters more under this discipline than under a cadence that would have three more chances to land.

Whichever tool you choose, meetings get qualified against criteria agreed in writing before launch, never against how confident a data provider was about a record. For the enrichment field beyond these two see the enrichment tool roundup.

The decision, in one paragraph

If you need verified contact details, Lusha is the answer and it is the only one of the two publishing a current, readable ladder: $37.45 a month billed yearly for 4,800 credits at the bottom and $259.95 for 40,800 at the top, with seats bundled and the whole annual allowance granted upfront. If you need technographics, Datanyze is the tool built for that job and the figures in this article come from our dataset rather than its page, so treat them as a starting point and ask the vendor directly. A page that will not load is not a price list, and we would rather say so than pretend otherwise.

Related Reading

• Best Datanyze Alternatives in 2026
• Best Lusha Alternatives in 2026
• Lusha vs ZoomInfo: Data Enrichment Tool Comparison
• Clay vs Lusha: Data Enrichment Tool Comparison
• Best Data Enrichment Tools for Cold Email Teams

If you would rather have the list built and the campaign run for you, RevenueFlow books qualified meetings on a pay-per-meeting basis and publishes client results.

Pricing and features are taken from the vendors' own pages. Verify current terms with the vendor before relying on them.