composable - Martech Therapy

The inherited costs of the composable stack

In Martech, every pricing model hides a workload. Suites sell convenience. Composable platforms sell control. Either way, you pay for someone to hold the complexity, the only question is who.

Last week, in the first part of this series, I shared some experiences on how to read Martech pricing. In this second part, I want to examine the other end of the product spectrum and learn to live with it. Once you understand how vendors define usage, value, and fairness, a new question appears:

What happens when you try to escape those models altogether?

That’s where composability enters the story. It promises freedom from rigid licenses, bundled modules, and predefined ways of working. Build your own stack, plug in best-of-breed tools, let each component scale on its own terms.

From particle physics to privacy-first analytics

Mitzu's István Mészáros explains why the future of Martech runs on composable data

From CERN’s particle accelerators to the messy world of marketing data, István Mészáros has seen it all. In this episode of Couch Confidentials, we trace his path from working on the Large Hadron Collider to building Mitzu, a startup rethinking how companies approach composable analytics.

We talk about why centralizing KPIs matters more than shiny dashboards, how enterprise data culture often lags behind the technology, and why privacy is becoming as big a driver as scale.

I loved how István described the “people-pleaser” instinct that shaped his career, always chasing faster answers for colleagues, which eventually grew into a

Contextual CDPs: Composable, not chaotic

Part 5 of 6: Contextual CDPs need lightweight structure, not heavyweight rebuilds

By now, we’ve explored what context can unlock in a CDP, from dynamic profiling to real-time orchestration and trust. We’ve talked about layering memory and inference without blowing up your current stack. So in this fifth part, let’s look at what happens when you want to make these contextual layers stick… without making everything feel like a pile of duct-taped services.

Because that’s the risk. Composability gives us incredible flexibility, but without some architectural discipline, it becomes hard to explain, harder to maintain, and impossible to govern.

Let’s explore how to structure context-aware systems in

If the warehouse can think, what’s left for the CDP?

A critical reflection on Databricks, data gravity, and what comes next.

Nope, this isn’t an analysis of a press release. We’ve seen all Databricks partners do that generously all over LinkedIn. It’s a reflection, a personal one (I had some spare time over the weekend), on something that’s been building quietly in the background and what it means now that someone has taken a concrete step forward. That someone is Databricks.

Last week, Databricks launched something called Data Intelligence for Marketing. And sure, you could skim the headline and file it under “AI feature drop.” But that’s not what caught my attention.

What stood out was

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