Postgres compatibility drives adoption

Postgres compatibility matters most because Postgres is usually the first place teams prototype retrieval, keep source data, and attach messy application metadata. If a vector system can ingest and emit something close to Postgres rows plus JSONB fields, migration gets much simpler. Engineers avoid writing custom transforms for nested metadata, reduce drift between systems, and make it easier to compare results during evals and cutovers.

• Elasticsearch matters for teams that need exact keyword behavior and rich field mappings, but in this workflow it is less the baseline than the specialized search layer. The interview points to Elasticsearch when queries need custom syntax and stronger sparse retrieval, not as the default source schema for prototyping.
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• Postgres stands out because it natively handles nested JSONB documents, and the interview describes that as a good fit for dense plus sparse retrieval at smaller scale. That makes Postgres shaped import and export the cleanest way to preserve metadata structure without rewriting every pipeline around Turbopuffer's object storage flow.
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• Pinecone payloads are useful to support, but they are structurally narrower. Pinecone records center on an ID, vectors, and flat metadata for filtering, while Postgres JSONB can carry deeper nested application state. That means Postgres interoperability reduces more migration pain for real production schemas.
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The next competitive step for retrieval databases is looking less like faster vector search and more like becoming easy drop in companions to the data stores teams already run. The vendors that win production migrations will be the ones that can read Postgres shaped data cleanly, preserve nested metadata faithfully, and let teams replay the same corpus across systems without schema drift.