The Iron Triangle of AI Engineering: Capability, Cost, and Speed

Most product teams I meet treat AI as a magic feature. You send an input, and you get a perfect output. But having built and scaled these systems, I can tell you that in reality, building AI native products is not about magic, its about managing a new set of volatile variables. Unlike traditional software, which is deterministic (Code A always equals Result B), AI is probabilistic. This introduces three competing constraints that every leader must balance.

To build a viable AI product, you cannot just optimize for quality. You must constantly trade off three variables: Capability (Efficiency), Cost, and Speed.

Your solution will never be perfect at all three. Your strategy is defined by which variable you choose to sacrifice. I have seen too many pilots fail because they tried to maximize all three simultaneously using a single model.

* The Use Case: Content Moderation or Chatbot Intent Classification. * The Trade off: You sacrifice Capability. * The Real World Stack: You do not use GPT 4 here. In my recent implementations, we used distilled models like Phi 3 or Claude 3 Haiku. * Why: If a user sends a message, you have 200ms to decide if it is safe. You cannot wait for a reasoning model to ponder the nuances of the speech. You need a good enough answer, instantly and cheaply. I have seen teams burn 50 percent of their margin by routing these simple checks to a flagship model.

* The Use Case: Legal Contract Analysis or Complex Medical Diagnosis. * The Trade off: You sacrifice Speed and Cost. * The Real World Stack: This is where you deploy the heavyweights. I typically reach for Claude 4.5 Sonnet (for coding/logic) or Gemini 3 Deep Think Mode (for long context). We often wrap these in a "Chain of Thought" workflow, which multiplies token cost and latency but ensures precision. * Why: If you are analyzing a merger agreement, no one cares if it takes 3 minutes or 3 Hours. But if you miss a clause, the liability is massive. Accuracy is the only metric that matters.

* The Use Case: Coding Assistants or Writing Aids. * The Trade off: A constant negotiation between all three. * The Strategy: The only way I have saved unit economics here is by using a Router. Simple requests go to a fast model; complex requests are escalated to a smart model. You are constantly dynamically adjusting the mix based on the user intent.

Building AI native is not a binary choice between API or Custom Model. It is a spectrum.

On one side, you have the Custom route where you own the weights and the infrastructure. I advise this only when latency or privacy is non negotiable. On the other side is the Off the Shelf route, where you move fast but are exposed to pricing volatility.

The job of an AI leader is not to pick the best model. It is to pick the right trade off for the specific node in your user journey. If you treat every problem as a nail, you will go bankrupt buying hammers.