Choosing Your Vector Database
Purpose-built vs. Postgres extensions — real benchmarks from production deployments.
The Question Every Enterprise Asks
When building an AI pipeline, one of the first architectural decisions is where to store your vector embeddings. The choice typically comes down to two options: a purpose-built vector database designed from the ground up for similarity search, or a vector extension on your existing Postgres database.
Both are valid choices. The right answer depends on your specific use case, team capacity, and scale requirements. We've deployed both approaches across different clients and have real production data to share.
Benchmark Setup
We tested across three production deployments with similar hardware:
- 50,000 – 200,000 documents per deployment
- 1536-dimension embeddings
- 10-50 concurrent queries per minute
- Mixed query patterns (keyword + semantic)
Results
| Metric | Purpose-Built | Postgres Extension |
|---|---|---|
| Query latency (p50) | 12ms | 45ms |
| Query latency (p99) | 38ms | 120ms |
| Indexing speed | ~2min/10K docs | ~8min/10K docs |
| Ops complexity | Additional service | Same as Postgres |
| Backup / HA | Separate config | Existing Postgres HA |
Our Recommendation
Choose a purpose-built vector database when: you have more than 100K documents, need sub-20ms query latency, have a DevOps team comfortable managing additional services, or plan to scale significantly.
Choose a Postgres extension when: you have fewer than 100K documents, want to minimize operational complexity, already run Postgres and want to leverage existing backup/HA infrastructure, or latency under 100ms is acceptable.
For most mid-size enterprise deployments (50K-100K documents), either approach works. We typically start with the Postgres extension for faster time-to-value and migrate to a dedicated vector database if query volume or dataset size demands it.