pgvector vs Marqo(2026)
pgvector is better for teams that need no new infrastructure. Marqo is the stronger choice if no external embedding model needed. pgvector is open-source (from $0) and Marqo is open-source (from $0).
Full feature breakdown, pricing details, and pros & cons below.
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pgvector
pgvector adds vector similarity search directly to PostgreSQL — store embeddings alongside your relational data and run nearest-neighbor searches with standard SQL.
Starting at $0
Visit pgvectorMarqo
Marqo is an end-to-end tensor search engine that generates, stores, and retrieves embeddings automatically — send text or images and Marqo handles the ML pipeline for you.
Starting at $0
Visit MarqoHow Do pgvector and Marqo Compare on Features?
| Feature | pgvector | Marqo |
|---|---|---|
| Pricing model | open-source | open-source |
| Starting price | $0 | $0 |
| PostgreSQL extension | ✓ | — |
| L2/inner product/cosine distance | ✓ | — |
| IVFFLAT indexing | ✓ | — |
| HNSW indexing | ✓ | — |
| Standard SQL | ✓ | — |
| Works with Supabase/RDS | ✓ | — |
| Exact + ANN search | ✓ | — |
| Auto-vectorization | — | ✓ |
| Open source | — | ✓ |
| Multimodal (text + image) | — | ✓ |
| Managed cloud | — | ✓ |
| Lexical + tensor hybrid | — | ✓ |
| REST API | — | ✓ |
| Python SDK | — | ✓ |
pgvector Pros and Cons vs Marqo
pgvector
Marqo
Should You Use pgvector or Marqo?
Choose pgvector if…
- •No new infrastructure
- •Works with existing Postgres
- •SQL interface
Choose Marqo if…
- •No external embedding model needed
- •Multimodal out of the box
- •Simple API