pgvector specialists for your dedicated team.

Where pgvector fits in a long-term product team.

The architectural argument is strong for most products. Vectors beside the relational data means one backup, one set of transactional guarantees and joins between embeddings and business data in a single query, with no synchronisation, no consistency window and no second system to run.

Index choice is the substantive decision. HNSW gives better query performance and recall at the cost of build time and memory; IVFFlat builds faster and uses less memory with lower recall. Both are approximate, and the accuracy-versus-speed setting is a deliberate choice.

What your dedicated team does with pgvector.

The practical ceiling is higher than most teams expect. Millions of vectors with appropriate indexing perform well, and a great many products that adopted a dedicated vector database never needed one.

Finding where that line sits for a specific workload takes measurement, and it is part of the architectural work when you hire AI engineers through us.

Common pgvector use cases.

• Vectors and business data in one query Similarity joined to relational filters without crossing a system boundary.
• One thing to back up and operate No synchronisation path and no second consistency model.
• Index chosen deliberately HNSW or IVFFlat selected against measured recall and latency requirements.

Adding pgvector skills to your team.

Vector search in Postgres is assigned across AI and data capacity on one agreement, since the decision spans both.

pgvector specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.