// 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 |
Yes Partial No
Performance
GalaxDB numbers measured on AWS c6id.4xlarge (Intel Xeon Platinum 8375C, 16 vCPU, 32 GiB RAM, 884 GB NVMe), release build. See BENCHMARKS.md for reproduction commands.
| Metric | GalaxDB | PG + pgvector | Pinecone | Qdrant | Notes |
|---|---|---|---|---|---|
| Write TPS (16 threads, 1M rows) | 258,555 | ~3,200 | N/A | N/A | GalaxDB measured on AWS c6id.4xlarge, release build |
| Read p50 (warm cache) | 3 µs | ~95 µs | N/A | N/A | |
| Read p99 (warm cache) | 47 µs | ~300 µs | N/A | N/A | |
| Scan throughput | 4.49 GB/s | ~0.9 GB/s | N/A | N/A | |
| HNSW recall@10 (SIFT-1M, ef=200) | 0.990 | ~0.95 | not published | ~0.99* | *Qdrant recall from their own benchmarks on their hardware |
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
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.