Real-time sounds impressive in the demo. Then you see the invoice. Refreshing 100 segments hourly instead of daily: 25x the compute cost. Every 5 minutes? 50x.
The math doesn’t lie. Neither does your budget.
Most of what we call “urgent” can wait until tomorrow.
The economics of real-time data have shifted dramatically as cloud costs compound. What seemed like a reasonable infrastructure investment in 2024 looks increasingly unsustainable in 2026. Snowflake compute credits, AWS data transfer fees, and streaming infrastructure costs scale linearly with refresh frequency, but business value rarely does. A McKinsey analysis of enterprise data architectures found that organizations spending on real-time capabilities see diminishing returns beyond specific use cases: fraud detection, inventory management, and time-sensitive bidding justify the premium. Email personalization at 9am vs. 9:05am? Not so much.
The hidden cost isn’t just infrastructure, it’s organizational complexity. Real-time systems require 24/7 monitoring, incident response protocols, and specialized engineering talent. When your customer data pipeline breaks at 2am, someone needs to fix it immediately, or your entire personalization engine stops. That operational burden transforms your marketing technology into a mission-critical system with all the overhead that entails.
Smart organizations are adopting tiered refresh strategies: truly real-time for high-value use cases, near-real-time (15-30 minute refresh) for most personalization needs, and daily batch processes for analytics and reporting. This pragmatic approach cuts infrastructure costs by 60-70% while delivering 95% of the business value. The question isn’t “can we make it real-time?”, it’s “does the incremental value justify the exponential cost?” Most honest answers are no.
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Pal Erik Waagbo 