PostgreSQL vs MongoDB for SaaS: Which Database Should Your Product Use?

Technically yes, but document-to-relational migrations are complex because the data models are fundamentally different. You need to design the target relational schema, write migration scripts to transform documents into rows, handle cases where the document structure does not map cleanly to a relational schema, verify data integrity after migration, and manage the cutover from MongoDB to PostgreSQL without downtime. The cost of this migration grows with the volume of data and the complexity of the document structure. If you expect to need PostgreSQL's capabilities eventually, starting with it is much less work than migrating to it later.

For most early-stage AI products with document collections up to a few hundred thousand vectors, pgvector is competitive with Pinecone on query latency when properly indexed with HNSW. The primary advantage of pgvector is consolidation: no additional database to manage, no additional service to pay for, and the ability to combine vector similarity queries with structured SQL filters in a single query. Pinecone outperforms pgvector at very large scale (tens of millions of vectors) and has a more purpose-built filtering and metadata model. Start with pgvector and migrate to a dedicated vector database if you hit pgvector's performance limits at scale.

Supabase is our primary recommendation - it runs on AWS and offers the eu-west-2 (London) region, making GDPR data residency straightforward. It includes pgvector, row-level security, real-time subscriptions, and an auth layer on top of PostgreSQL. Other options include Railway (easy setup, good for smaller projects), Render PostgreSQL, AWS RDS for PostgreSQL in eu-west-2, and Neon (serverless PostgreSQL with branch-based development databases). For enterprise-scale products, managed AWS RDS or Google Cloud AlloyDB in EU regions provide the reliability and compliance documentation that enterprise procurement requires.

Schema migrations in PostgreSQL can be performed without downtime using additive migration strategies: add new columns as nullable before making them required, create new tables before dropping old ones, and use background jobs to backfill data in large tables rather than blocking migrations. Tools like Prisma Migrate, Drizzle Kit, and Flyway support migration management with version history. For large tables, adding a non-nullable column requires careful planning - adding it as nullable with a default, backfilling data in batches, then adding the NOT NULL constraint in a separate step is the standard zero-downtime pattern. Supabase's migration tooling handles the common cases well.

GDPR obligations apply to the data controller, not the database. Both PostgreSQL and MongoDB can be operated in ways that satisfy GDPR requirements. The relevant questions are: where is the data stored (must be within UK or EEA for UK GDPR compliance, or have appropriate safeguards), who has access to the data, can you fulfil data subject rights requests (access, deletion, correction), and can you demonstrate appropriate technical and organisational measures. PostgreSQL's row-level security provides stronger technical guarantees for multi-tenant data isolation, which simplifies the 'appropriate technical measures' argument. MongoDB's application-level filtering also satisfies GDPR but requires more thorough testing to demonstrate the same assurance.