Been looking at real-time CDP implementations lately. The data is interesting - a relatively low % of leaders say their systems actually deliver real-time insights. Not surprising when you dig deeper. The technical complexity is significant. Most teams underestimate what it takes to integrate diverse data sources while maintaining speed and accuracy at scale. Even for relatively simple profile stores, we're seeing typical builds require 3-5 specialized engineers over 1-2 years, easily passing $1M in R&D. What's less discussed is the ongoing cost structure. Operating these systems is expensive and unpredictable. Unlike batch processing, streaming costs are harder to estimate. The expertise needed for maintenance is rare and costly. Ultimately, the reality is that "real-time" often means minutes, not milliseconds. And that's fine - most use cases don't need millisecond responses. What matters is getting actionable insights at the right moment. Looking at successful implementations, the common thread is solutions that minimize R&D costs and maintain predictable operating expenses while delivering reliable performance. *Originally published [here](https://x.com/barusebi/status/1857789586568577141)*