Azure SQL Database vs Cosmos DB for Java Apps

Azure SQL Database and Cosmos DB solve different problems — Azure SQL for relational workloads where consistency and joins matter; Cosmos DB for global distribution and schema-flexible document data.

Azure SQL Database — relational, JDBC-compatible

• Azure SQL Database is a fully managed SQL Server (or PostgreSQL/MySQL via Azure Database for PostgreSQL/MySQL) — which means Spring Data JPA and Hibernate work without code changes; ACID transactions, foreign keys, and complex joins are first-class citizens
• Built-in HA with Availability Zone redundancy; automatic failover; geo-replication for disaster recovery; elastic pools for cost-effective multi-tenant SaaS applications

Cosmos DB — globally distributed, multi-model NoSQL

• Cosmos DB replicates data across Azure regions with configurable consistency — from Strong (linearizable) to Eventual; reads from the nearest region give sub-10ms latency globally, which means a user in Tokyo reads from a Tokyo replica while a user in London reads from a London replica
• Spring Data Cosmos provides repository-style access with derived query methods on the SQL API; the partition key design is critical — all queries that don't include the partition key do a cross-partition scan, which is expensive
• Cosmos DB offers five consistency models: Strong, Bounded Staleness, Session, Consistent Prefix, Eventual — Session consistency (default) guarantees read-your-writes within one session, which is the right default for most applications

Choosing between them

• Azure SQL: transactional workloads, complex relational queries, existing JPA/Hibernate code, multi-table joins, strong ACID requirements
• Cosmos DB: globally distributed applications where read latency in multiple regions matters, schema-flexible data (product catalogs, event logs, user profiles with varying attributes), workloads where the partition key design naturally aligns with query patterns