Data Infrastructure · sub-niche
Vector database engines.
Vector search engines optimized for specific workloads — high-dimensional, hybrid, or local.
Why now
RAG is now everywhere. The vector DB seat is being decided. Switching costs are forming fast.
What the signal looks like
Repos with benchmarks against Pinecone / Weaviate / Qdrant, SDK adapters in TS + Python, and recall/latency tradeoff dashboards.
Public examples
We name publicprojects + categories only — never founders we track inside the paid product. The buyer’s edge stays inside the product.
- Qdrant / Weaviate forks
- TurboPuffer-style serverless vector DBs
- pgvector + pgvectorscale combos
What this displaces
An overprovisioned Pinecone bill and 200ms p95.
Our build-vs-invest call
Hard to differentiate. The wedge is a specific workload (multi-tenant, low-latency, edge). Fund with prior search infra background only.
Common questions about this niche
- Buyer?
- AI app developers + ML platform teams.
- Pricing?
- $100-10k/mo SaaS or self-hosted.
- Moat?
- Performance + ecosystem + cost.
Five breakout startups, every Sunday — before the round gets crowded
The free Acceleration Watch: five venture-backed teams accelerating on the engineering signal, translated into plain English — 21 to 47 days before the deck circulates. No code-reading, no card.
More inside Data Infrastructure
- Real-time feature stores — Feature stores with sub-second freshness for online ML.
- Postgres extension marketplaces — Postgres is now the AI database. The extension ecosystem is the next platform.
- Columnar warehouse alternatives — Snowflake / BigQuery alternatives optimized for a specific shape — cheap, fast, or open.
- Change data capture tools — CDC pipelines that don't require a Kafka cluster.