Snowflake is ditching bytes scanned for pricing

Snowflake is retiring the bytes scanned metric as its pricing unit, moving to a compute-and-storage model instead. The change was announced this week and is rolling out in the coming months. It affects every Snowflake user — from solo data folks to Fortune 500 teams — and it changes how you think about query costs in a way the old model never did.

The bytes-scanned metric was always a blunt instrument. Queries that touched the same data in different ways could cost wildly different amounts, and it was hard to tell whether a slow query was expensive because it was doing real work or because it was reading too much cold data. The new model ties cost to what the warehouse actually spends — compute cycles plus the storage you hold. That's a more honest ledger. You still pay for the data you keep, but you're not punished for running a query that happens to read a lot of bytes in one shot.

There's a companion project worth a look: EmbrasureAI's spark-observability-skills, which brings similar observability to Spark workloads. If you're running Spark on Databricks or a self-hosted cluster, it gives you a way to see how much compute each job is burning. Snowflake's move makes the same point — you need visibility into what's actually costing you, not just how much data you touched.

Why this matters for us: The shift to compute-based pricing hits the small teams and side businesses hardest, since they're the ones who can't afford to leave warehouses idling or run queries that scan terabytes for no reason — visibility is the difference between a $40 bill and a $400 one.