Why a Vector Database Alone Won't Cut It (Chroma vs. Our Approach)
Vector databases like Chroma have exploded in popularity. They solve a very specific problem: finding similar pieces of information fast. But if you mistake a vector DB for a full knowledge substrate, you're going to hit hard limits.
Anthony Rawlins
CEO & Founder, CHORUS Services
The Chroma Value Proposition Chroma is excellent at what it does: store embeddings and return the nearest neighbors. It’s simple, efficient, and useful as a retrieval backend.
The Limits But a database is not a knowledge system. With Chroma, you get:
- Embeddings without meaning — no structured way to represent “where” knowledge lives.
- No sense of time — history is overwritten or bolted on manually.
- No reasoning trail — results come back as raw chunks, not justifications.
- No distributed context — each deployment is its own silo.
What We’re Doing Differently Our stack (Chorus + BZZZ + UCXL) doesn’t replace a vector DB; it sits above it.
- We define a protocol for addressing and navigating knowledge, like URLs for context.
- We make time a native dimension, so you can query across versions and histories.
- We attach provenance to every piece of retrieved information.
- And we enable agents — not just apps — to share and evolve context across systems.
Conclusion Chroma is a great building block. But it’s still just a block. If you want to build something more than a single tower — a city of agents that can collaborate, exchange knowledge, and evolve together — you need infrastructure that understands time, structure, and justification. That’s the gap we’re closing.