COMPARE:
Clay or Apify for building an enriched list
Clay and Apify both do building an enriched company list, and this page puts them on the same seven questions. Mamba Labs builds the Apify actors here, Prospect Engine included, and the page says where it is the wrong answer.
The verdict
They are not the same kind of thing. Clay is a table with a hundred providers behind it. Apify is a place to run code against the public web. Most people who ask this question want the table, and then want the cost per row that the table cannot give them.
The same questions, both tools
| Clay | Apify | 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. | Runs a program against the public web on a schedule or on demand, and hands back a dataset. | 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 few thousand published actors, plus anything you write yourself, which is the part that matters. | 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 event or per compute unit, depending on the actor, and it is usually cents per row. | 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. | An API, a scheduler, webhooks, and a dataset you pull or push onward. Clay calls it as an HTTP step. | 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. | The public web, read directly, which means you can see exactly where a field came from. | 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. | Running a published actor takes minutes. Writing your own takes a developer. | 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. | There is no table, no interface for a list, and no view of your accounts. | 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.
What each is actually better at
- 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.
- Apify wins on what it costs, where the data comes from. A platform for running scrapers and data actors, yours or somebody else's.
- Prospect Engine wins on what it takes to set up. One call that finds the companies, enriches them and returns finished rows.
- Nobody wins on what it actually does, where it stops. Clay and Apify give the same answer on those, and we are not going to invent a difference.
Where to get them
What we would actually do
If a person needs to look at the list, argue with it and edit it, you want Clay and you should accept the credit cost as the price of that. If the list goes straight into a system and nobody reads it row by row, running actors on Apify costs a fraction of the same work. It also tells you where every field came from. The two sit together comfortably: plenty of Clay tables call an Apify actor as an HTTP column precisely because the provider catalog does not cover the thing they need. That is the setup we run ourselves.
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