concept

Compute / storage separation

Compute / storage separation is the cloud-database architecture where compute (queries) and storage (data) scale independently — letting compute scale to zero while storage stays durable. Neon, Aurora Serverless, Snowflake, BigQuery pioneered this in 2026.

Traditional databases bundle compute + storage on the same machine; scaling means resizing both. Separation flips this: storage lives in a durable shared layer (S3 / EFS / proprietary), compute is ephemeral and stateless, multiple compute instances can read the same storage, compute can scale to zero while data persists. The unlocks: cheap idle databases (Neon scale-to-zero), per-query elastic compute (Snowflake warehouses), read replicas without storage duplication, instant database branching ([[database-branching]] uses storage CoW). Trade-offs: compute cold-start when scaling from zero (typically 100ms-3s in 2026), network latency between compute and storage requires careful caching. The architecture behind most modern serverless databases.

When to use compute / storage separation

Common mistakes

FAQ

What is compute / storage separation?

Compute / storage separation is the cloud-database architecture where compute (queries) and storage (data) scale independently — letting compute scale to zero while storage stays durable. Neon, Aurora Serverless, Snowflake, BigQuery pioneered this in 2026.

When should I use compute / storage separation?

Spiky workloads (dev DBs, batch analytics). Apps that need cheap idle states.

What are the most common mistakes with compute / storage separation?

Latency-critical apps may hit cold-start delays — pre-warm or pin compute.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/compute-storage-separation.md.