TMI Community // AI & automation

Choosing a stack for AI products in 2025

The stack decision is rarely about the newest tool. This decision tree focuses on foundations that keep options open as usage, risk, and product clarity change.

MP

Mina Patel

Staff software engineer

Nov 18, 2025//8 min read

A promising AI product can outgrow its first architecture quickly, but novelty is not the only source of risk. The harder problem is choosing which parts should remain replaceable and which parts deserve the discipline of a long-lived platform.

We now begin with four questions: what must be deterministic, what can be probabilistic, what data may leave the system, and which workflow needs a human decision. Those answers usually narrow the stack more effectively than a feature comparison.

The result is a deliberately boring core: clear interfaces, observable jobs, provider-agnostic prompts, and a small evaluation set that runs before every meaningful release. The interesting work belongs at the product edge.

“Choose foundations for the decisions you expect to revisit, not the demos you want to show once.”

Discussion (31 Replies)

SA

Sara Ahmed

Design Technologist

Apr 02, 2026

The idea of making the review ritual small enough to repeat is the part I am taking away. Security guidance is only useful when it survives a busy development sprint.

LM

Leo Martins

Cloud Architect

Apr 03, 2026

Would love to see the threat-model template you used. We have been trying to keep it close to the pull request without making the PR unreadable.

AK

Ayesha Khan

Product Engineer

Apr 04, 2026

The key is to record the architectural decision and the residual risk clearly, rather than producing a 30-page static specification.