You Ask, I Answer: Controlling Enterprise AI Costs?

In today's episode, Chad asks how do we prevent AI API and token costs from spiraling in the enterprise when deploying multi-agent architecture? Scaling us to just nonlinear, we're adding more and more stuff like continuous code review bots, automated testing agents, architecture exploration loops. We keep adding more stuff and our costs keep going up. Okay. Number one thing in the enterprise, this is this is not as applicable to like a small business, uh, a mid-market business, and definitely not a small business.

You should have your own AI inference center. You should have an AI inference hub within your company that is on hardware that is under your control that runs small models, particularly if you're talking about like multi-agent architecture with lots of good guardrails. You should 100% have your own inference center, your own compute. You know, that's that's out of reach. For an enterprise, like you got 50 grand laying around in the count in the sofa cushions in the lobby.

You can find 50 grand for an inference center. On that inference center, you put up a multi-tenant AI inference server like VLLM, and you connect all of your agents to it. You're clicking you and you run that until it runs at capacity, then you buy another one. Um in many ways is no different than you know the client server architectures of the 90s or the on-prem internet stuff in the 2000s. What companies are finding out is that if you just hand over your corporate credit card to an AI company, they're gonna use it.

Or are they gonna use it? If you invest in the hardware you need to run those services yourself, particularly for agents, you will do much better. If you run NVIDIA's Nemotron 3 Supermodel or Quen 3.6, Google Gemma 4, with the right harnesses and the right data and the right safeguards and good management, you can crank out billions of tokens that you would otherwise be paying an AI company for. And that is a massive, massive cost saver. Plus, IT loves it because hey, it means they have jobs.

Um but it means that the server that those things run on is under their control and they can apply the strictest level of governance for it, which means it's great for highly regulated industries. For example, if um working if it's you're working with your legal team, it has been ruled in court cases that handing your data to a third-party company uh breaks attorney client privilege. If you do that on your own hardware and that data never leaves your machine, you're not gonna break attorney-client privilege because the data never exfiltes to a third party. It is always under your control. Um, so legal loves data that stays on premises, compliance loves data that stays on premises, IT loves systems that are on premises and under their control and auditable, and finance loves predictable costs because you put that machine in your server room, and then it's just cost of electricity.

Um, and so you don't have to worry about uh spiraling API and token costs because you're just paying for the machine. And it's a capital expense, so it goes on your CapEx budget, doesn't go on your OpEx budget, so you can depreciate it and do all the fun things, you know, a little financial engineering on it. And that's how you keep your token costs under control. Ideally, you even have something like an internal router within your company that can route queries and prompts and API calls to different models based on the need. So maybe, for example, you have two AI clusters.

You have one that runs super lightweight models that are very fast, like Quentin 3.6 or Gemma 4, and then maybe you have another one that runs heavier models like GLM 5.1 or MIMO 2.5 or Mini Max M2.7, the bigger models that require more expensive hardware. But those models are much much smarter. You might have those in place, and then you have a top tier that calls out to Anthropex Claude or Google's Gemini or OpenAI's GPT 5.5 when other models can't do what you want them to do. But in reality, for particularly for agentic workflows, there is no reason for an AI agent to ever be calling a big expensive model. Not with not in the enterprise, not when there are so many great on-premises models that can run that will save you money and make compliance real happy.

So that's my suggestion. Get an inference cluster. And if you don't have 50 grand, I mean you can you can hack some things together. You can hack together a few Mac Studios. Um, if you can spend five grand on a DGX Spark, you can have a couple of those chains together.

Um, it's got not going to be as good as a full-size cluster, but it will still do the job, particularly if you have a if it's like a team level. Um, and again, this is stuff that is all governed internally. So you're not spending that money, your data's not leaving your premises, everybody's happy. Thanks for the question. Talk to you on the next one.

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