TL;DR
Big data platforms like Snowflake and BigQuery impose high pricing floors, like 60-second minimums and capacity commitments that can run well into four figures a month, that actively punish small, spiky startup workloads.
Most teams have less than 50TB of data and do not require massive distributed architectures. A "scale-up" architecture is vastly more efficient for SQL analytics.
For a few terabytes of data, open-source DuckDB allows you to run lightning-fast analytics locally on your laptop for free.
When you need massive concurrency or petabyte-scale lakehouse capabilities, serverless scale-up architectures like MotherDuck eliminate DevOps overhead and bill compute by the second, cutting analytics costs for startup workloads that would otherwise pay for idle warehouse time.
