Beyond the Universal Database: Why Modern Workloads Demand Specialization

As AI/ML workloads transition from experimental features to core application requirements, the limitations of stretching a single database across diverse use cases become operationally unsustainable. This narrative reveals a fundamental architectural shift: stacking extensions like pgvector, PostGIS, and TimescaleDB creates compounding complexity that, when repeated across multiple projects, makes polyglot persistence or purpose-built databases more pragmatic than maintaining a monolithic PostgreSQL ecosystem.