Cloud LLM vs. Own Infrastructure: The Honest Cost Calculation

Note: All figures in this article are illustrative calculation-model placeholders: they are not price quotes and not a price promise. Insert your own current numbers.

The Decision in One Sentence

Cloud LLMs win while your volume is low and variable; owned infrastructure wins once a workload runs sustained or data may not leave the building. The honest calculation tells you exactly where that tipping point sits for your company.

Most cost comparisons on this topic are dishonest because they count only one side: cloud advocates ignore volume, hardware advocates ignore operations and utilization. This article costs both sides fully and gives you a model to fill in yourself.

The Two Cost Models Side by Side

Dimension	Cloud LLM (API)	Own infrastructure (self-hosted)
Cost structure	Variable: per token / request	Fixed: hardware + operations, volume-independent
Entry cost	Near zero, instant	High: purchase or dedicated GPU rental
Cost per token (sustained)	Constant, rises linearly with use	Falls as utilization rises
Scale up	Trivial: provider scales	Bounded by your capacity
Scale down	Trivial: pay for what you use	Fixed cost runs even when idle
Data residency	At the provider (often outside DE/EU)	Fully under your control
Vendor dependency	High: price, rate limits, model lifecycle	Low: model and operations in your hands
Operating effort	Minimal: provider runs it	Real: maintenance, monitoring, updates, security
Cost predictability	Fluctuates with use	High: fixed, predictable line item

The Break-Even Model

The core of any honest decision is one comparison: monthly cloud cost at your volume vs. monthly full cost of your own machine. Four inputs you must know or measure: (1) your monthly token volume in sustained operation, not the pilot (multiplying pilot figures by 3–10× is more realistic than it sounds once a tool enters daily work); (2) the cloud price per 1M tokens (insert today’s list price); (3) the monthly full cost of your machine, not just the GPU, but depreciation/rental + power + hosting + operations (zeroing out operations is self-deception); (4) realistic utilization, where a machine at 15% has the same fixed cost as one at 80% but five times the unit price.

Monthly cloud cost   = (volume in M tokens) × (price per M tokens)
Monthly own cost     = hardware/rental + power + hosting + operations   [fixed]
Break-even volume    = monthly own cost ÷ cloud price per M tokens

Above break-even, owned wins; below it, cloud wins. The honesty is in not flattering the inputs.

Illustrative example (placeholder figures, not a real price): if the machine fully costed is €1,500/month and cloud is €0.50 per M tokens, break-even is 3,000M tokens/month. Replace with your own numbers.

What the Calculation Misses but Still Counts

Data-residency risk: for sensitive workloads the most expensive event is a breach, not a GPU line item.
Vendor dependency: a price hike, an ill-timed rate limit, or a model deprecation hits without warning; own infrastructure is insurance, and the premium is operating effort.
Predictability: a fixed monthly line is more valuable for CFO planning than a variable one that grows precisely when your rollout succeeds.

When to Choose Cloud / When to Choose Own / Hybrid

Cloud: low/occasional/spiky volume, no sensitive data, pilot phase, or need for the absolute reasoning frontier. Own: sustained above break-even, data must stay in-house, you want to cut vendor dependency and make cost predictable, and you have operating capacity (or a partner). Hybrid (most common): sensitive and high-volume workloads in-house, the rest in the cloud. A single GPU plus staging carries the in-house side surprisingly far. We run exactly that setup ourselves, deliberately small, and know its limits first-hand. See Our Decision Tree: Self-Host or Cloud?.

FAQ

Is cloud or self-hosting cheaper? No blanket answer. There’s a break-even set by your volume, the cloud price, and your full cost. The model above gives your exact point.

How big must the hardware be? For many Mittelstand workloads a single modern GPU suffices; we run on exactly one node plus staging. Utilization and operations are the real constraints, not size.

What if my volume is uncertain? Then a cloud pilot is right, until you’ve measured enough to set break-even seriously. Measure first, decide second.

Next Step

We fill the break-even model with your real numbers and tell you honestly where your tipping point is and which architecture pays off.

Request the cost calculation →