COMPARE:
Clay or Apollo for building a target list
Clay against Apollo for building a target account and contact list, with the same seven questions asked of each and neither one flattered. Mamba Labs builds the Apify actors here, Prospect Engine included, and the page says where it is the wrong answer.
The short answer
Apollo gives you one database. Clay gives you a hundred of them and charges you per lookup. If the accounts you want are in Apollo, Apollo is cheaper and faster.
Dimension by dimension
| Clay | Apollo | Prospect Engine | |
|---|---|---|---|
| What it actually does | Builds and enriches a list in a table, with each column calling a provider and falling through to the next. | Finds contacts in its own database, then emails them from the same account. | Takes a market definition or a domain list and returns enriched company and contact rows. |
| Coverage and hit rate | The widest provider catalog anywhere, and a waterfall that tries them in the order you set. | A very large contact database, and a sequencer good enough that most users never add a second one. | Public sources, read directly, with the field's origin on the row. |
| What it costs | Credits, spent per column run, and a run that returns nothing still costs on most providers. | Per seat per month, with export credits as the real ceiling on how much data you get out. | Cents per row, charged on a returned row rather than on an attempt. |
| How it fits a workflow | The table is the workflow. Exports and webhooks push the finished rows onward. | Search, save a list, sequence it, all in one interface. Its API and CRM syncs are the weaker half. | One API call, or an HTTP column in Clay, and finished rows come back. |
| Where the data comes from | Other people's data, resold through one interface, plus whatever you point its agent at. | A contributed database, refreshed from user activity, so accuracy varies sharply by market and seniority. | The public web and public registers, named per field. |
| What it takes to set up | An afternoon to a first useful table, longer before the credit cost stops surprising you. | An hour to a first sequence. It is the fastest of these to something running. | Point it at a market or a list of domains and run it. |
| Where it stops | You cannot run your own code in it, so an unusual step is a step you cannot build. | Coverage outside the United States thins out, and its data is the same data your competitors bought. | It is not a table. Reviewing and editing rows is something you do elsewhere. |
The same seven questions are asked on every compare page here, in this order, so two of these pages can be read against each other.
Who wins what
- Clay wins on coverage and hit rate, how it fits a workflow. A spreadsheet that calls a hundred data providers, one column at a time.
- Apollo wins on where the data comes from. A contact database with a sequencer bolted on, sold as one seat.
- Prospect Engine wins on what it costs. One call that finds the companies, enriches them and returns finished rows.
- Nobody wins on what it actually does, what it takes to set up, where it stops. Clay and Apollo give the same answer on those, and we are not going to invent a difference.
Both products
What we would actually do
Apollo is enough for a common market: software companies, standard titles, English speaking countries. If that is your market, Clay is a lot of machinery to arrive at the same list. Clay earns its price when the criterion that defines your market is not a field in anyone's database, which is when you start waterfalling providers and scraping the answer. Watch the credit spend in the first month either way. Clay's cost is invisible until a column runs across ten thousand rows and returns nothing on most of them, and you paid for all of it.
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