After a wave of consolidation over the past six years, many martech minds have wondered whether CDP is a better concept than a product.
Vendors in and around the digital experience space, in particular, were buying CDPs, and in some cases that was because the two vendors and their products were often used in tandem by customers anyway. Why not combine forces then?
If you were in the martech space, you remember those days. Twilio bought Segment; SAP bought Emarsys; Contentstack bought Lytics; Uniphore purchased ActionIQ, to name a few.
These were not legacy platforms. These were CDPs designed for a data problem. They were developed to collect and unify customer data and profiles, create audiences and activate campaigns. Their strengths lay in identity resolution and customer profile unification.
The concepts discussed by Hightouch and Databricks have a lot in common. But at a high level, what they are saying is this: the future depends not on customer profiles, but on customer decisions.
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If unifying customer profiles was CDP 1.0, composability was CDP 2.0 and agentic CDP is CDP 3.0, and it consists of unified customer data + AI decision + autonomous execution.
While CDP 1.0 saw data as the problem to be solved, it can be argued that CDP 3.0 views humans as a blocker. We are too slow to analyze data and make decisions. AI agents, on the other hand, move quickly and constantly.
As vice president and principal analyst at Forrester Joe Stanhope wrote:
“Agentic AI offers the opportunity to not only implement new capabilities that expand the CDP mission, but also develop a new paradigm for generating insights, targeting audiences, making decisions, and orchestrating customer journeys.”
Who will own the agent layer and where will it live?
Where Hightouch and Databricks diverge is in their philosophies on how it all plays out.
“Five years ago, we thought we were building a better CDP architecture. In reality, we were laying the foundation for intelligent agents. By moving audiences, journeys, and activation to the warehouse, Composable CDP directly connected marketing to an organization’s richest customer and business context.”
Hightouch’s vision is for agents to do their work in the data warehouse, without copying the data. This is true to the company’s roots in composability and it also adds an agentic layer to the conversation. For Hightouch, the agentic layer of CDP 3.0 is in a marketing platform on top of the data platform.
Databricks’ CustomerLake is an example of the company’s philosophy that data warehouses – in this case, Databricks’ Data Lakehouse technology – can also serve as an application platform. Databricks has already done this with enterprise security when it launched Lakewatch in March 2026. With CustomerLake, it is applying this philosophy to marketing. (It should be noted that CustomerLake may ingest data from sources beyond the Data Lakehouse.)
Databricks sees an advantage in building your CDP on the data platform because your governance, AI, and business context are already there. Don’t copy it, don’t move it, just do the work there.
Is the CDP broad enough for both models?
At first glance, from the cheap seats, it appears that Hightouch and Databricks are on a collision course. Let’s set aside for a moment the other players who will jump into the CDP 3.0 space as it matures. (BlueConic acquired Blueshift last week has a similar story regarding adding AI agents and actions to customer data.)
In reality, Hightouch and Databricks can coexist because they tend to target different organizations.
For Databricks, which sells a data platform, the primary buyer is often the data, AI, and platform teams on the technology side. Hightouch sells more often to CRM, marketing and lifecycle teams.
This means that each vendor often has a different starting point in their lead organizations. For Hightouch, there is a data warehouse that its product can build on and an existing martech stack is in place. For Databricks, there is a Lakehouse and an enterprise-wide AI strategy.
Since Databricks works at the enterprise level, it seeks clients with maturity in data engineering and AI. Hightouch seeks maturity in marketing operations.
Databricks’ enterprise focus means the time to value is potentially longer than for Hightouch customers.
But these significant differences mean the two companies will often fish in different ponds. Hightouch will appeal to large DTC players, consumer financial services, retailers, subscription businesses, and travel and hospitality companies, where the CMO will likely play a role in the decision.
Databricks, on the other hand, will find itself engaging with global financial services companies, telecommunications, large healthcare systems, and large enterprises with a centralized data organization in which the CIO will likely be heavily involved.
In other words, it is unlikely that either approach will win. Each approach can be a win-win for the right client.
The best we can hope for is that these platforms deliver on their promises and that marketers and their customers both win.
