Abstract
I already have 25 containers running on my own server. Portal, ERP, automation, vector database, credential manager — all self-hosted. And yet, when it came time to decide where to run the AI models, the answer was: on none of my servers. This node documents the real criteria behind that decision — not as an anti-self-hosting argument, but as the same criterion that makes the rest of the stack worth it.
The Decision That Wasn't Obvious
I had everything ready to do it. The server, the container architecture, the habit of solving things within my own infrastructure instead of relying on a third party. Adding Ollama to the list would have been, technically, the easiest step of all — one more container among 25.
I didn't do it. The cost-risk relationship of computing versus the cost of processing externally in a diversified way did not favor self-computing. Today, the infrastructure articulates eleven external models with a failover system — none run on my server.
That decision is what almost no self-hosting article documents, because almost all are written to sell the idea of self-hosting, not to point out when it's not appropriate.
The Threshold That Most Operations Never Cross
The economic break-even point between running a self-hosted model and paying for a cloud API appears around 500,000 tokens per day for mid-range models, and can reach 2 million for more demanding configurations (PromptCost.org, 2026; AI Cost Check, March 2026). Below that volume, self-owned hardware — amortized, with electricity and maintenance included — costs more than the API, not less.
For a single infrastructure administrator operation, crossing that volume every day is the exception. Most people reading a guide on "how to self-host your own LLM" never reach that point — and no guide tells them before they buy the hardware.
The Analogy That Organizes the Four Criteria
Analogy: it's the same decision as having your own car versus using transportation apps. If you drive long distances every day, owning a car pays for itself compared to the accumulated travel expenses — the exact equivalent of the volume that crosses the economic break-even point. But the car has maintenance that doesn't appear in the purchase price — insurance, tires, unexpected breakdowns — just as self-computing has maintenance that doesn't appear in the hardware price. A car solves nothing if you have nowhere to park it — just as a local model solves nothing if your architecture doesn't support it. And if what you really want is not to depend on a single means of transportation, the answer is not to buy a car — it's to have several transportation apps so you never get stranded by one that fails. Buying the car without having done those four calculations is the same decision as installing Ollama because container 26 was technically easy.
The Four Criteria That Matter, in Order
1. Real Volume vs. Break-Even Point. If your operation does not consistently cross the token/day threshold — not in an occasional spike, but as a pattern — self-computing does not pay for itself. This is not an opinion: it's the same math I used to decide not to run models locally.
2. The Maintenance Cost That Doesn't Appear on Any Bill. The complete stack of 25 containers requires between 2 and 4 hours of actual monthly maintenance — updates, logs, dependency conflicts. That time has a cost even if it doesn't show up as a charge on a card. Self-hosting an AI model adds a layer of specific maintenance (model versions, drivers, memory) that most underestimate before experiencing it.
3. Real Architecture Compatibility, Not What the Tutorial Assumes. I run on ARM64 (Oracle Cloud Ampere, free tier). Not every tool has a native build for that architecture — Hermes Desktop, for example, simply does not run there without emulation. Before planning to self-host something, verifying that it runs on your actual architecture avoids discovering it after committing time.
4. If External Diversification Already Solves the Problem You Think You Need to Solve by Self-Hosting. The most common reason for wanting a self-hosted model is not to depend on a single provider — resilience. But eleven external providers with a failover system solve exactly that same problem, without the capital cost or operational risk of maintaining self-computing. If your real motivation is "I don't want to depend on just one," the answer is not necessarily to self-host — it may be to diversify.
What This Does Not Mean
It does not mean that self-hosting is a bad idea. The rest of the stack — automation, database, credential management, monitoring — does cross those four criteria with margin, and that's why it's self-hosted. The decision not to run models locally is not a general stance against self-hosting; it's the same cost-risk criterion applied honestly to a specific case, rather than applying it only when it confirms what you already wanted to do.
What Is Still Not Resolved
I do not have my own figure for the exact point of volume or hardware price drop at which this decision would reverse — the analysis was done once, with the prices and volume of this moment, not as a permanent rule. Model and hardware prices drop every three to six months in this market; it's worth reviewing the calculation periodically instead of assuming that today's decision remains correct a year from now.
Closing
If you are evaluating self-hosting something, the useful question is not "Can I do it?" — almost always the technical answer is yes. It's "Do my volume, my real maintenance time, my architecture, and the problem I really want to solve justify it?" Sometimes the honest answer is no, even when you already have 25 containers that say you know how to do it.