Stop building your own vector index: Why managed services are eating the Postgres ecosystem

Adding semantic search capabilities to an internal document analysis tool once required a large Python script, a local HNSW index on an EC2 instance, and the risk of the index crashing when 500,000 embeddings were reached. Now, the same setup uses a managed vector service, with vectors pushed via API, near real-time index updates, and the elimination of "Search currently unavailable" errors.

This shift isn't just about moving to the cloud; it's about recognizing that a vector index is a specialized infrastructure piece, not just another table in a database. The shift to managed services is driven by the operational tax of running vector indexing within a monolithic Postgres instance. This includes managing shared_buffers, work_mem, and the vacuuming cycle, which can lead to CPU spikes and query latency issues when dealing with large numbers of vectors.

Managed services like Pinecone and Databricks Mosaic AI Vector Search abstract away the index management, memory pressure, and hardware provisioning, allowing teams to focus on API latency and cost. While pgvector offers a cost-effective solution for smaller datasets within the Postgres ecosystem, larger teams or those already utilizing platforms like Databricks may find value in enterprise-grade managed services. Ultimately, the choice depends on the specific use case, scale, and existing infrastructure.