Ask an engineering team "cloud or on-prem?" and you'll get a religious debate. Ask a finance team the same question and you'll get a spreadsheet. Both are asking the wrong question. The useful question is narrower and far more answerable: at what usage volume does owning the hardware become cheaper than renting tokens? Below that crossover point, cloud APIs are the obvious choice. Above it, every additional token you push through a rented endpoint is money you could have stopped spending months ago. This piece lays out both cost models honestly, shows you how to find your own break-even, and names the cases where each side genuinely wins.
The cloud API cost model
Cloud inference is metered. You pay per token — input and output priced separately — and you pay nothing when you're idle. There is no capital expenditure, no procurement cycle, and no GPU sitting in a rack depreciating while your product-market fit is still unproven. Commercial APIs today sit broadly in the range of tens of dollars per million tokens, and that number is the whole story: your bill is a straight line through the origin, its slope set by your usage.
That linearity is a gift at low or spiky volume and a liability at scale. The same property that lets you spend almost nothing during a quiet week means a viral launch, a batch-processing job, or a chatty agent loop can multiply your bill without warning. Cost compounds precisely because usage compounds — and mature LLM products tend to grow usage relentlessly as they add features, users, and retrieval context.
Two line items rarely make it into the first estimate. Data egress — moving your data to and from the provider — carries both a bandwidth cost and a governance cost, because your prompts and outputs leave your perimeter. And vendor lock-in is a real, if soft, expense: prompts tuned to one model's quirks, output parsers built around one provider's formatting, and pricing you don't control all raise the cost of ever leaving.
The on-prem cost model
On-premises deployment inverts the shape of the bill. You pay a large amount up front — GPU capex — and then a comparatively small, roughly fixed amount every month for power, cooling, rack space, and the operational effort to keep it healthy. Critically, that monthly figure barely moves with token volume. Once the hardware is deployed, inference is effectively unlimited: the marginal cost of one more million tokens trends toward the price of the electricity it took to compute them.
So where cloud is a straight line rising from zero, on-prem is a high starting step followed by a nearly flat line. At low volume the flat line sits far above the cloud line and on-prem looks absurd. Push enough tokens through, and the two lines cross.
Finding your break-even
You don't need a vendor's marketing math to find the crossover — you need four numbers and a division. The method:
- Estimate tokens per month. Take real traffic: average tokens per request times requests per day times 30. Include the input side — retrieval context and system prompts often dwarf the user's actual question.
- Compute monthly cloud spend. Multiply that token volume by your provider's blended per-token rate. This is the recurring number on-prem has to beat.
- Amortize the hardware. Take the GPU box's capital cost and spread it over a realistic service life (three years is a common assumption), giving a monthly amortized figure.
- Add on-prem opex. Power, cooling, and a slice of an engineer's time. Sum it with the amortized hardware to get the true monthly cost of owning.
Compare the two monthly totals. If cloud spend already exceeds owned cost, you're past break-even today and paying a premium to rent. If not, divide the hardware capex by your monthly savings (cloud spend minus on-prem opex) to see how many months of use it takes to pay the box off.
The qualitative pattern is consistent: light, occasional workloads may never break even, while steady high-volume workloads cross over quickly. For heavy, continuous usage the LLMDeploy figures put break-even in roughly two to four months — after which the hardware is effectively free inference for the rest of its service life. Run the arithmetic with your token volume before trusting any generic claim, including that one.
The single biggest lever is token volume, and it's the number teams estimate worst. Instrument your real traffic for a week before modeling anything — a factor-of-three error in tokens/month moves the break-even more than any hardware or electricity assumption you'll argue about.
Hidden costs on both sides
The sticker models above are clean; reality adds friction to each.
On the cloud side
- Rate limits can throttle a launch or a batch job precisely when throughput matters most.
- Egress and bandwidth add up quietly when you're shipping large contexts back and forth all day.
- Fine-tuning constraints — limited options, extra per-token surcharges, or no access to model weights at all — cap how far you can customize.
- Compliance overhead grows every time you have to prove where regulated data went and who could see it.
On the on-prem side
- Procurement lead time for GPUs can stretch into weeks or months — hardware you can't buy is hardware you can't amortize.
- Ops and SRE effort is a real, recurring salary line, not a rounding error.
- Utilization risk is the killer: a box that sits idle still costs its full amortized amount, so a fixed cost divided over low usage produces a terrible per-token rate. On-prem economics assume you actually keep the hardware busy.
When cloud still wins
Cloud is the right answer more often than hardware vendors like to admit. Choose it when volume is low or unpredictable, when you're still experimenting and don't yet know which model or use case will stick, or when you simply have no ops team and adding one would cost more than the tokens ever will. Paying per token to avoid owning a problem is a legitimate, often optimal, engineering decision.
When on-prem wins
On-prem earns its keep when the workload is steady and high-volume enough to clear break-even, when sensitive data or compliance makes keeping inference inside your perimeter non-negotiable, when latency matters and a local GPU beats a round trip to someone else's data center, and when you want cost control at scale — a fixed, predictable monthly number instead of a bill that grows with your success.
The takeaway
| Dimension | Cloud API | On-Prem |
|---|---|---|
| Cost shape | Linear per-token, zero capex | Upfront capex + flat opex |
| Marginal cost per token | Fixed vendor rate | Trends toward electricity |
| Best at | Low / spiky volume | Steady high volume |
| Scaling behavior | Cost compounds with usage | Cost stays flat; usage is "free" |
| Data & compliance | Data leaves your perimeter | Data stays in-house |
| Ops burden | None — provider runs it | Requires ops / SRE effort |
| Main risk | Bill grows with success; lock-in | Idle hardware; procurement lead time |
| Time to value | Immediate | Days once hardware is available |
There is no universal winner — only a crossover point specific to your token volume, your data sensitivity, and your appetite for running infrastructure. Model it honestly with your own numbers, and the answer usually stops being a debate and becomes a calculation.
If that calculation points on-prem, that's the side LLMDeploy handles. We stand up open-weight models on your hardware, inside your perimeter, deployed in as little as 72 hours — and for heavy, steady workloads the break-even lands in months, after which your inference cost is effectively just the power bill. Run your numbers first; if you land past the crossover, we'll get you there.