Vector databases (DBs), once specialist research instruments, have become widely used infrastructure in just a few years. They power today’s semantic search, recommendation engines, anti-fraud measures and gen AI applications across industries. There are a deluge of options: PostgreSQL with pgvector, MySQL HeatWave, DuckDB VSS, SQLite VSS, Pinecone, Weaviate, Milvus and several others.The riches of choices sound like a boon to companies. But just beneath, a growing problem looms: Stack instability. New vector DBs appear each quarter, with disparate APIs, indexing schemes and performance trade-offs. Today’s ideal choice may look dated or limiting tomorrow.To business AI teams, volatility translates into lock-in risks and migration hell. Most projects begin life with lightweight engines like DuckDB or SQLite for prototyping, then move to Postgres, MySQL or a cloud-native service in production. Each switch involves rewriting queries, reshaping pipelines, and slowing down deployments.This re-engineering merry-go-round undermines the very speed and agility that AI adoption is supposed to bring.Why portability matters nowCompanies have a tricky balancing act:Experiment quickly with minimal overhead, in hopes of trying and getting early value; Scale safely on stable, production-quality infrastructure without months of refactoring;Be nimble in a world where new and better backends arrive nearly every month. Without portability, organizations stagnate. They have technical debt from recursive code paths, are hesitant to adopt new technology and cannot move prototypes to production at pace. In effect, the database is a bottleneck rather than an accelerator.Portability, or the ability to move underlying infrastructure without re-encoding the application, is ever more a strategic requirement for enterprises rolling out AI at scale.Abstraction as infrastructureThe solution is n …
