Looking for a quick comparison between Chroma and PGvector for a mid sized startup project we are deploying this week.
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Looking for a quick comparison between Chroma and PGvector for a mid sized startup project we are deploying this week.
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
Chroma documentation production deployment collections filtering persistence distributed deployment 2026 Google ↗
pgvector official documentation HNSW IVFFlat filtering iterative scans PostgreSQL 2026 Google ↗
- Architecture Overview · docs.trychroma.com
- Introduction · docs.trychroma.com
- Distributed Architecture · docs.trychroma.com
- GitHub - pgvector/pgvector: Open-source vector similarity search for Postgres · github.com
- EDB Docs · enterprisedb.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Chroma documentation production deployment collections filtering persistence distributed deployment 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
pgvector official documentation HNSW IVFFlat filtering iterative scans PostgreSQL 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 |