"No Way Jose. I Am Not Running My Own Models."
That was the blunt text I received from a talented founder minutes after reading my last post. His frustration was palpable:
"I am busy building a business. I don't have time to manage infrastructure. I need speed, not a server farm."
This is a natural response.
In this post, I argued that AI is not SaaS. In software, scale solves everything. In AI, scale exposes everything. If you are unit-negative at 1,000 users, you are dead at 1,000,000.
In this post, I warned of the Infrastructure Trap. Relying exclusively on "renting" the biggest models locks you into a cost structure you can’t control.
The Founder’s Dilemma
So, we are at an impasse. You can't rely on Big APIs (kills margins), but you "can't" run your own models (kills focus).
How do we solve this? The answer isn't to build a data center. It is to stop using a chainsaw to cut butter.
We default to massive Foundation Models (GPT-4, Claude Opus) because it is easy. But using a Trillion-Parameter model for "Arithmetic" is economic malpractice.
The math is brutal: Even with price drops, a high-end model costs ~$5.00 per million tokens. A self-hosted 7B model on a rented GPU costs roughly $0.20 per million tokens. That is a 25x difference. E.g., Monthly token costs of $50,000 vs $2000. That capital should be hiring engineers, not paying Microsoft’s electric bill.
To satisfy "No Way Jose" (simplicity) and "Margin Health" (survival), you must treat your Agent as a General Contractor.
An efficient Contractor doesn't lay every brick. He hires a Master Architect for the design, but masons for the execution. Your AI Architecture must do the same:
The Router (The Foreman): A tiny classifier (100M parameters, costs pennies) acting as traffic cop.
The Specialist (The Laborer): For the 80% of tasks that are "Arithmetic" (extraction, classification), the Router sends work to a purpose-built Small Language Model (SLM).
The Generalist (The Architect): Only when the task is a "Differential Equation" (complex reasoning) does the Router call the big API.
Own the "Small" Stack
This is the middle ground. You don't need to train a Trillion-parameter model. But you must develop the expertise to run small, purpose-built models.
Efficiency: A 7B model on a cheap chip burns a fraction of the energy.
Accuracy: A small model trained on your data often outperforms a giant model trained on the internet for your specific task. Specificity beats generalization.
Margins: You cannot control OpenAI’s price. You can control the cost of a 7B model on rented compute.
Yes, this adds complexity. But ask yourself: Is that complexity harder than running your biz with 20% gross margins?
The Bottom Line
The hyperscalers are selling rocket fuel. It’s powerful, but expensive. Don’t use it to drive to the grocery store.
Decompose the task. Route the work. Own the margin.
That was the blunt text I received from a talented founder minutes after reading my last post. His frustration was palpable:
"I am busy building a business. I don't have time to manage infrastructure. I need speed, not a server farm."
This is a natural response.
In this post, I argued that AI is not SaaS. In software, scale solves everything. In AI, scale exposes everything. If you are unit-negative at 1,000 users, you are dead at 1,000,000.
In this post, I warned of the Infrastructure Trap. Relying exclusively on "renting" the biggest models locks you into a cost structure you can’t control.
The Founder’s Dilemma
So, we are at an impasse. You can't rely on Big APIs (kills margins), but you "can't" run your own models (kills focus).
How do we solve this? The answer isn't to build a data center. It is to stop using a chainsaw to cut butter.
We default to massive Foundation Models (GPT-4, Claude Opus) because it is easy. But using a Trillion-Parameter model for "Arithmetic" is economic malpractice.
The math is brutal: Even with price drops, a high-end model costs ~$5.00 per million tokens. A self-hosted 7B model on a rented GPU costs roughly $0.20 per million tokens. That is a 25x difference. E.g., Monthly token costs of $50,000 vs $2000. That capital should be hiring engineers, not paying Microsoft’s electric bill.
To satisfy "No Way Jose" (simplicity) and "Margin Health" (survival), you must treat your Agent as a General Contractor.
An efficient Contractor doesn't lay every brick. He hires a Master Architect for the design, but masons for the execution. Your AI Architecture must do the same:
The Router (The Foreman): A tiny classifier (100M parameters, costs pennies) acting as traffic cop.
The Specialist (The Laborer): For the 80% of tasks that are "Arithmetic" (extraction, classification), the Router sends work to a purpose-built Small Language Model (SLM).
The Generalist (The Architect): Only when the task is a "Differential Equation" (complex reasoning) does the Router call the big API.
Own the "Small" Stack
This is the middle ground. You don't need to train a Trillion-parameter model. But you must develop the expertise to run small, purpose-built models.
Efficiency: A 7B model on a cheap chip burns a fraction of the energy.
Accuracy: A small model trained on your data often outperforms a giant model trained on the internet for your specific task. Specificity beats generalization.
Margins: You cannot control OpenAI’s price. You can control the cost of a 7B model on rented compute.
Yes, this adds complexity. But ask yourself: Is that complexity harder than running your biz with 20% gross margins?
The Bottom Line
The hyperscalers are selling rocket fuel. It’s powerful, but expensive. Don’t use it to drive to the grocery store.
Decompose the task. Route the work. Own the margin.