Vector DB choice is the most over-engineered decision in RAG. Teams spend weeks choosing between databases when the cheapest managed option would carry them to Series A.
The vector DB decision is usually made too early and too elaborately. The first-principles question is what you actually need — and for most teams, the answer is "the cheapest thing that works until the bill forces a change."
First Principles: What You Actually Need
Strip it down: a vector DB stores embeddings and returns approximate nearest neighbors. The questions are how many vectors, how many queries per second, what latency, and what filter complexity. Most teams at the start have few of each — and the cheapest option carries all of them.
- How many vectors (thousands, not billions, at the start).
- How many queries per second (low, at the start).
- What filter complexity (simple metadata filters, at the start).
- What latency budget (almost anything works at the start).
The Cashflow Lens
The decision is a runway decision. A managed vector DB is cheap and ops-free; a self-hosted cluster is expensive and ops-heavy. The time you'd spend operating a cluster is time not spent on the product. Choose the managed option until the bill forces the self-hosted one.
What to Refuse on Principle
- A self-hosted vector cluster before the managed bill is a problem.
- A "scalable" database before you have the scale to need it.
- A feature comparison that ignores ops cost.
- Choosing for hypothetical traffic you don't have.
The Upgrade Path
The right path is staged: managed and cheap → managed and larger → self-hosted when the bill forces it. The database you'd want at Series B is a cost you pay at seed — and the cost is paid in runway, not just dollars.
Conclusion
Vector DB choice is over-engineered because teams choose for scale they don't have. The first-principles answer is the cheapest managed thing that works until the bill forces a change — and the runway you save is the win.
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
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