// COMPARISON
GalaxDB vs the alternatives
Most AI applications need SQL, vector search, local embeddings, and training exports. GalaxDB is the only database that covers all four in a single binary.
Feature matrix
| Feature | GalaxDB | PG + pgvector | Pinecone | Qdrant | LanceDB | ChromaDB | Milvus | DuckDB | Notes |
|---|---|---|---|---|---|---|---|---|---|
| Full SQL | LanceDB OSS uses DuckDB bridge, not native SQL | ||||||||
| Vector search (HNSW) | |||||||||
| HNSW recall@10 (SIFT-1M) | GalaxDB: 0.990 at ef=200. pgvector: ~0.95. Pinecone: not published. | ||||||||
| Local embeddings (no API) | Qdrant has FastEmbed (lightweight, optional) | ||||||||
| Time-travel (AT VERSION) | |||||||||
| Training export (Lance) | |||||||||
| Near-dedup (MinHash LSH) | |||||||||
| Embedded mode (no server) | |||||||||
| PostgreSQL wire protocol | |||||||||
| Self-hosted | |||||||||
| Encryption at rest | pgvector relies on OS-level encryption | ||||||||
| MVCC / snapshots | |||||||||
| Single binary | Milvus requires etcd + MinIO + multiple services | ||||||||
| Open source |
Performance
Measured on AWS c6id.4xlarge (Intel Xeon Platinum 8375C, 16 vCPU, 32 GiB RAM, 884 GB NVMe), release build. The write-path numbers are an apples-to-apples run against PostgreSQL 16 on the same NVMe. See BENCHMARKS.md for full results and reproduction commands.
| Metric | GalaxDB | PG + pgvector | Pinecone | Qdrant | Notes |
|---|---|---|---|---|---|
| HNSW recall@10 (SIFT-1M, ef=200) | 0.990 | ~0.95 | not published | ~0.99* | *Qdrant recall from their own benchmarks on their hardware |
| COPY bulk load | 190,287 rows/s | N/A | N/A | N/A | Same instance-store NVMe as the PostgreSQL comparison below |
| Concurrent INSERT, 1 client (durable) | 10,450 rows/s | 11,891 rows/s* | N/A | N/A | *PostgreSQL 16.14, same NVMe, fsync=on, prepared statements |
| Concurrent INSERT, 16 clients (durable) | 37,448 rows/s | 84,747 rows/s* | N/A | N/A | PostgreSQL's process-per-connection model scales better past 8 clients today |
When to choose what
Choose GalaxDB when
- You need SQL + vector search in one query
- You want local embeddings (no API cost)
- You need training data export to PyTorch
- You want time-travel for reproducibility
- You want a single binary with no external deps
- Your existing psycopg2/SQLAlchemy code should work unchanged
Choose something else when
- Existing Postgres + basic vector search: stay with pgvector
- Zero-ops managed cloud, no SQL needed: Pinecone
- Pure vector search, self-hosted, high perf: Qdrant
- ML pipeline, notebook-first, Lance format: LanceDB
- Quick RAG prototype: ChromaDB
- Billion-scale vectors, dedicated infra team: Milvus
- Pure analytics, no vector search: DuckDB
GalaxDB is not a drop-in PostgreSQL replacement
PRIMARY KEY uniqueness is enforced. NOT NULL, CHECK, UNIQUE (on non-PK columns), and FOREIGN KEY constraints are parsed but not yet enforced. There is no support for triggers, PL/pgSQL, views, sequences, or multi-column indexes. If your application depends on any of these, test against GalaxDB before migrating production traffic.
Pricing reality
GalaxDB is Apache 2.0 open source. You pay only for the infrastructure you run it on.
GalaxDB (self-hosted)
Server cost only
e.g. $100/mo for a c6id.4xlarge
Pinecone (10M vectors)
$200-400/mo
Enterprise: $500/mo minimum
Milvus (Zilliz Cloud, 10M)
~$500/mo
Plus operational overhead
Pinecone pricing from pinecone.io/pricing. Milvus from zilliz.com/pricing. Content rephrased for compliance.