Pyyan / Compare / Vertex AI Vector Search vs pgvector vs Qdrant vs Pinecone
Vertex AI Vector Search vs pgvector vs Qdrant vs Pinecone
4 of 5Vector Databases · verified 13 Aug 2026
| Specification | Vertex AI Vector Search | pgvector | Qdrant | Pinecone |
|---|---|---|---|---|
| Summary | Google's own, built on ScaNN. | Vectors inside the database you already run. | The fastest of the purpose-built stores. | Managed, and the smoothest to operate. |
| Index | ScaNN | HNSW, IVFFlat | HNSW | Proprietary |
| Hybrid search | Yes | Yes, with SQL | Yes | Yes |
| Hosting | Managed | Self-host or any managed Postgres | Self-host and managed | Managed only |
| Licence | Proprietary | PostgreSQL | Apache 2.0 | Proprietary |
| p50 latency | ~15ms | ~15ms | 4ms | <10ms |
| Category | Vector Databases | Vector Databases | Vector Databases | Vector Databases |
| Official | Google ↗ | PostgreSQL ↗ | Qdrant ↗ | Pinecone ↗ |
Highlighted rows are where these differ.
Vertex AI Vector Search
- The research behind it powers Google's own retrieval
- Tied to the platform
Best for workloads already on Google Cloud.
pgvector
- One database for vectors, rows and joins
- The honest default: reach for something else only when this stops working
Best for almost everyone, until scale says otherwise.
Qdrant
- Written in Rust; 10 to 25% faster than Weaviate or Milvus on common workloads
- Strong filtering alongside vector search
- Self-host or managed, same engine
Best for latency-sensitive retrieval.
Pinecone
- Sub-10ms p50 with nothing to maintain
- Costs draw scrutiny at scale; Notion moved away and cut spend ~60%
Best for teams who do not want to run a database.