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Aurora Is Not Just a Faster RDS

Aurora and RDS share an API. What they do not share is how they behave when it matters.

AWS markets Aurora as a drop-in replacement, and the migration path looks clean on paper. The MySQL and PostgreSQL compatibility is real, but compatibility is not equivalence. Aurora uses a distributed storage layer that replicates across six copies in three availability zones, decoupled from the compute. That changes how failover works, how storage costs accumulate, and how the database behaves under sustained load. Teams that migrate expecting identical behavior find out the hard way that the operational model is different. Most teams choose RDS because it is familiar. It maps directly to running a database on a server, with snapshots, maintenance windows, and instance classes that feel predictable. Aurora replaces that mental model with a distributed storage layer that scales automatically, but it also introduces behaviors that only appear under load or during failover. Storage billing alone works differently: you pay for what you use rather than what you provision, which can be cheaper or much more expensive depending on write patterns. The choice made at the start of a project tends to stay until something breaks. Aurora Serverless adds another layer to this. It can pause when idle and resume on demand, which looks attractive until a cold start hits in production. Aurora Global Database, Aurora I/O Optimized, and Aurora DSQL exist because different workloads have different requirements, and no single configuration covers everything. The real decision is not between RDS and Aurora. It is understanding that Aurora is not a single product.
# amazon-aurora# amazon-rds
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