At seed stage, your AI stack is a runway instrument. Every choice — model, vector DB, framework, hosting — is a bet against the clock, not just a technical preference.
The temptation at seed is to build the stack you'd want at Series B. The discipline is to build the stack that gets you to Series A without running out of money proving you could build infrastructure.
First Principles: What Does Seed Actually Need
Strip the stack to what the business needs to ship: a model (API, not your own), retrieval (the cheapest thing that works), an agent loop (a framework, not a platform), and a place for logs. Anything beyond that is infrastructure you're building instead of the product.
- Model: a frontier API, BYO keys, no training your own.
- Retrieval: managed vector store until you outgrow it.
- Framework: the lightest one that fits, not the most "scalable."
- Logs: structured, cheap, and somewhere you can query.
The Runway-Aware Plan
Plan the stack in stages. Ship on managed everything. Move to self-hosted only when the bill forces it. Never build the platform before the product — that's the classic seed-stage error, and it's paid for in runway.
What to Refuse on Principle
- A custom vector database at seed. No.
- A multi-agent orchestration platform before you have one agent in production. No.
- Fine-tuning before you've exhausted prompting and retrieval. No.
- Self-hosted models before the API bill is a real problem. No.
How FACTA Frames It
FACTA's startups solution ships strategy plus a working system on a startup timeline — runway-aware by construction. The stack is the smallest one that produces a defensible system, and the upgrade path is staged so you don't pay for Series B infrastructure with seed money.
Conclusion
The seed-stage AI stack is a runway decision. Choose the cheapest thing that ships a system, and refuse every piece of infrastructure that doesn't pay for itself before the next raise.
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.
Show us your planned AI stack and your runway.
We'll tell you which pieces you're over-building and what to ship instead. Related: the seed-stage AI stack's cousin, build-vs-buy.
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