Data Infrastructure · sub-niche
Real-time feature stores.
Feature stores with sub-second freshness for online ML.
Team-sized buildTrickle — one deal per quarter
Why now
Real-time fraud, personalization, and ranking all need real-time features. Most teams build it badly.
What the signal looks like
Repos with streaming-ingest adapters (Kafka, Kinesis), point-in-time correctness libraries, and serving APIs with caching.
Public examples
We name publicprojects + categories only — never founders we track inside the paid product. The buyer’s edge stays inside the product.
- Tecton shape
- Hopsworks
- Feast + custom serving
What this displaces
A Lambda + DynamoDB + crossed fingers.
Our build-vs-invest call
Heavy build. Fund only with prior ML-platform team. The wedge is one industry (fintech, e-commerce, ads).
Common questions about this niche
- Buyer?
- ML platform teams at ML-heavy companies.
- Pricing?
- $100k-1M+/year.
- Defensibility?
- Performance + correctness + integrations.
More inside Data Infrastructure
- Vector database engines — Vector search engines optimized for specific workloads — high-dimensional, hybrid, or local.
- 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.