If you're in a GTM role at a B2B SaaS company, you've probably heard of Clay. It's one of the fastest-growing tools in the outbound stack, and for good reason. But a question keeps coming up: how does Clay compare to Datamorf? Are they competing for the same job?

Short answer: not really. They tend to be used for different stages of the same workflow. Understanding the difference will help you get more out of both.

What Clay does well

Clay is a table-based enrichment and prospecting tool. You load a list of companies or contacts, connect it to a wide range of data sources (LinkedIn, Apollo, Clearbit, and many others), run enrichment columns, and see results instantly in a spreadsheet-like UI.

It's excellent for exploration. You want to test a new ICP? Load 200 companies, enrich them with a few signals, write an AI-powered icebreaker column, and push the results to Instantly, all in an afternoon. You can see every result, tweak the logic, and iterate fast.

Clay excels at:

• One-off prospecting lists and experiments

• Rapid enrichment prototyping, testing which data sources work best

• Manual campaigns where you review each contact before sending

• Teams that want full visibility at every step

Clay also has workflow capabilities and you can automate things with it. It's a genuinely powerful platform. The UI is built around making data exploration fast and visual, and it's very good at that.

Where the use cases diverge

Datamorf is built for a different scenario: operational processes that need to run automatically in the background, continuously, without anyone opening a browser tab.

Think about processes like:

• Enriching every new HubSpot contact the moment they're created

• Running a scoring workflow on your full CRM every night

• Triggering different routes based on CRM field changes or segment membership

• Processing thousands of records a week without manual input

These are NOT one-off tasks. They're ongoing operations. They need to run reliably, on a schedule or triggered by an event, and produce clean outputs every time without someone overseeing each run.

Datamorf is designed specifically for this type of workload.

How Datamorf works

Every Datamorf workflow has four sections:

1. Trigger: how the workflow starts. A webhook, a schedule, or the Extractor (which is reverse ETL: you define a segment in your CRM or database, Datamorf pulls the list and processes each record automatically)

2. Data sources: pull additional data from any API. CRM, databases, built-in integrations like HubSpot, Apollo, Snowflake

3. Transformations: apply logic. Pre-built functions, custom code, or AI prompting to clean, enrich, or score data

4. Destinations: push results anywhere. Back to HubSpot, into Instantly, Slack, Airtable, any API

You build the workflow once. Every new lead that comes in gets enriched, scored, and routed automatically. Every record in your CRM gets re-evaluated on schedule. No manual steps.

The mental model: experiment vs productionize

Clay = experiment. Datamorf = productionize.

This is probably the most useful way to think about it.

Use Clay to figure out what works. Test different enrichment sources. Try different AI prompts. Build a prototype of your outbound workflow on a small batch, validate it, see the results.

Once you know it works, rebuild it in Datamorf and let it run automatically at scale.

Real example:

1. You use Clay to test a waterfall enrichment: try Apollo first, then Clearbit, then Hunter for email. You run it on 300 contacts. You see a good hit rate. The logic is proven.

2. You rebuild that waterfall in Datamorf as a workflow triggered by new contacts entering HubSpot.

3. Now every new contact gets automatically enriched the same way, around the clock, without anyone doing it manually.

Clay found the formula. Datamorf runs it continuously.

Using both together

Many GTM teams use both tools, which makes a lot of sense.

Clay in the active stack for prospecting experiments, one-off campaigns, and list building. Datamorf in the operational stack for the automated processes that keep your CRM clean, your leads enriched, and your outbound sequences populated.

• Clay: new campaign ideation, one-off list pulls, testing new data sources

• Datamorf: ongoing lead enrichment, CRM scoring, automated routing, data sync

They're complementary. The experimentation you do in Clay becomes the operational process you run in Datamorf.

A quick note on pricing

Clay prices by the action: each enrichment credit, each row processed. Makes sense for experiments where volume is low and controlled.

Datamorf prices by workflow execution. One execution is one full run through the workflow, regardless of how many steps. A 10-step waterfall enrichment counts as 1 execution. This makes costs predictable when running workflows at scale across thousands of records.

Summary

• Clay is great for exploration and experimentation: visual, interactive, hands-on

• Datamorf is built for production operations: automated, deterministic, background

• They solve different problems at different stages of your GTM workflow

• Most mature GTM stacks use both

If you've been using Clay to build a workflow and thinking 'I wish this ran automatically every day', that's where Datamorf comes in.

See how Datamorf handles the operational layer your team is missing: datamorf.io