Most trading tools price per seat. Staxis prices per swarm — because in a system of 250 self-learning LLM-assisted agent swarms, the swarm is what produces the move, and the swarm is the unit of value.
The pricing unit reveals what a product thinks its value is. Per-seat pricing says the value is the person. Per-swarm pricing says the value is the swarm — the system that surfaces the trade, not the human reading it.
The Business Model of a Swarm
Canvas it: the customer is the trader, the product is the move surfaced by a swarm, and the unit of value is the swarm. The trader keeps custody and acts on their own exchange; the swarm surfaces what to trade, when, where. The swarm is the asset; the seat is just access.
- 250 self-learning LLM-assisted agent swarms.
- The swarm surfaces the move; you keep custody and act.
- The swarm is the unit of value — so it's the unit of price.
The Cashflow Frame
Per-seat pricing scales against the team. Per-swarm pricing scales with the value produced. A trader running one swarm pays for one swarm; a trader running all swarms pays for all swarms. The pricing matches the value captured, not the headcount.
What the Architecture Refuses to Relax
Four properties the platform refuses to relax, each inspectable: permissions scope, property test, governance log, intelligence feed. The non-custodial guarantee, the circuit breaker, and the hash-chained audit log are the same across every surface — switch surfaces without migrating data.
The Plans
Free paper mode → Pro (one live swarm) → Power (all swarms) → Performance (qualified clients) / Enterprise (family offices + RIAs). The progression is the unit of value growing — from one simulated swarm to a portfolio of live ones — and the price grows with it.
Conclusion
The swarm is the unit of value because the swarm is what produces the move. Pricing per swarm aligns cost with value — and the architecture (non-custodial, circuit breaker, audit log) is what makes the swarm inspectable, not a black box.
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:
AI leadership that builds. Not just advises.
See what pricing per swarm looks like at the Staxis product page.
Related: the audit log as the product and circuit breaker design.
Explore AI Automation