Hey everyone! So many teams get burned by picking a CDP that crumbles under real data volume. I see it all the time. They demo well with a cute little 10k sample, but fall apart when you load your actual customer graph.
Here's a concrete method we used: before signing any contract, simulate a million-user load. Don't just take their word for it. Build a realistic profile schema (with 30+ attributes, nested objects for orders, arrays for engagement events). Then, script the ingestion of a million synthetic but plausible profiles. Watch for three things:
1. **Ingestion speed**: Does it slow to a crawl after 200k? Check their real-time API latency during the flood.
2. **Identity resolution time**: How long does it take to stitch new events to profiles *during* the load?
3. **UI responsiveness**: Can you still build segments and query the data while the simulation runs?
This stress test exposed huge differences between vendors for us. One platform's UI became unusable, another handled it smoothly. Saved us from a costly mistake.
Has anyone else run similar scalability tests? What were your deal-breakers? 🚀
Always optimizing.
That's a really useful checklist. I'm still pretty new to CDP evaluation and I've been wondering how to even approach a test like that without a dedicated engineering team. Did you build the simulation scripts yourself or use a third-party tool? Also, the identity resolution point scares me a bit - how do you measure when it's "done" stitching during an ongoing load? Seems like you'd need to watch a specific test profile.
Trying to figure it out.
Absolutely agree with this approach. We did something similar, but we also added a fourth metric: background job stability.
When we flooded the API, one vendor's system kept up with ingestion just fine, but their identity resolution jobs started failing silently in the queue. The UI was responsive, and new data was accepted, but the underlying profile merge logic fell hours behind. We only caught it because we scripted checks to see if our test "seed" events actually attached to the correct merged profile within a set time window.
Your point about the UI is so critical. It's the canary in the coal mine. If the marketer can't build an audience segment while data is flowing, the platform isn't really built for scale.
buyer beware, but buy smart