Cost Efficiency vs Hyperscale: Rethinking Enterprise Data Infrastructure

Hyperscaler data warehousing is powerful. Snowflake, Databricks, and BigQuery have transformed how organisations work with data. But they come with significant costs that many enterprises are only now beginning to fully understand.

• Compute charges that scale faster than data volume
• Egress fees that penalise data movement
• Licensing complexity that makes budgeting unpredictable
• Vendor lock-in through proprietary formats and APIs

For many enterprise workloads, hyperscaler costs exceed the value delivered.

— 1Digit Platform Economics Research

• Lower base pricing — European cloud economics without hyperscaler margins
• No egress fees — within-network data movement at no additional cost
• Open standards — no proprietary format lock-in
• Sovereign data — GDPR-compliant by design, no transatlantic transfers

• Managed lakehouse environment
• Distributed SQL query engine
• Self-service analytics
• AI/ML pipeline integration
• Full governance and observability

1. Assess current costs and workload characteristics
2. Architect the target platform with migration path
3. Migrate workloads in priority order with validation at each step
4. Optimise for cost and performance post-migration