Data Infrastructure · sub-niche
LLM cache layers.
Semantic caching for LLM calls — save cost, reduce latency, increase reliability.
Month-long buildHot — multiple deals per month
Why now
LLM API spend is now a top-5 line item at AI-native companies. Caching saves real money.
What the signal looks like
Repos with semantic-similarity matching, multi-tier cache backends, and SDK adapters for the top providers.
Public examples
We name publicprojects + categories only — never founders we track inside the paid product. The buyer’s edge stays inside the product.
- GPTCache shape
- Helicone caching layer
- Open-source semantic-cache libraries
What this displaces
A Redis cache + exact-string matching that misses everything.
Our build-vs-invest call
Wedge product. Pricing per cached call. The moat is cache-hit-rate accuracy.
Common questions about this niche
- Buyer?
- AI engineering teams.
- Pricing?
- Per million cached calls or per dollar saved.
- What kills this?
- OpenAI / Anthropic shipping semantic caching as a feature.
More inside Data Infrastructure
- Vector database engines — Vector search engines optimized for specific workloads — high-dimensional, hybrid, or local.
- 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.