Compare · Cost

Local AI vs cloud AI: the real cost is per token

Cloud AI is priced by the token, so the bill grows with every query, document, and retry. NodePlus runs on local models for $0 per token on a flat subscription.

The comparison below is not just price. It is data location, predictability, lock-in, and quality, the things that decide the true cost of running AI on real work.

The cost model, side by side
$0
per token, flat recurring subscription
Fixed
monthly cost, does not scale with usage
66.6%
SWE-bench Pro, so cheaper is not weaker
§ IThe comparison

What each model actually costs you

Sticker price is only part of it. The full cost of AI on real work includes where your data goes, how predictable the bill is, and whether you are locked to one provider.

Pricing model
NodePlusFlat subscription, $0 per token
Metered cloud AIMetered per token, billed on every request
Cost at scale
NodePlusA heavy month costs the same as a light one
Metered cloud AIRises with every query, document, and retry
Predictability
NodePlusFixed and easy to budget
Metered cloud AIVariable, hard to forecast month to month
Where your data goes
NodePlusRead in place on hardware you control
Metered cloud AISent to a third-party model on every call
Quality
NodePlus66.6% on SWE-bench Pro, on local models
Metered cloud AIFrontier quality, at frontier per-token prices
Lock-in
NodePlusYours to run, no provider dependency
Metered cloud AITied to one vendor and its pricing changes
§ IIWhy local wins on cost

The cost does not scale with how much you use it

A metered model punishes adoption. A flat local model rewards it. That difference compounds the more your team relies on AI.

Marginal cost of zero

Once it is running, one more query, one more document, or one more report costs nothing extra. Usage is free at the margin.

A bill you can forecast

Fixed monthly cost instead of a metered one that spikes with a busy close or a large analysis job.

No data-egress cost or risk

Data is read in place, so there is no third-party model to send it to and no residency exposure priced into the trade.

Frontier-class, not budget-class

66.6% on SWE-bench Pro and 93.8% on LongMemEval, on local models. You do not give up quality to escape the meter.

Hardware is fixed, not metered

The hardware cost is part of the deployment and does not grow with use, unlike per-token inference.

Adoption without rationing

Teams stop rationing AI to control spend, so the highest-value uses actually get run.

§ QCommon questions

Why is per-token pricing a problem?

Because it charges you more precisely when the tool is most useful. Every document read, every query, and every retry adds cost, so heavy real-world use becomes an unpredictable bill. Teams end up rationing the tool to control spend.

How does NodePlus charge if there are no tokens?

A flat recurring subscription. The engine runs on local models with no per-token bills and no metered cloud inference, so a heavy month costs the same as a light one, and analyzing a full history costs the same as a single lookup.

Does cheaper mean lower quality?

No. The same local engine scores 66.6% on SWE-bench Pro and 93.8% on LongMemEval. Every system that scores higher on the public coding leaderboard is a closed, paid, cloud model. You are not trading quality for cost here.

What about the hardware cost of running locally?

It is included in the deployment, not a surprise line item, and it is a fixed cost rather than a metered one that grows with usage. Past a modest volume, local is the cheaper model precisely because it does not scale with use.

Is our data part of the cost equation?

Yes. Cloud AI sends your data to a third-party model on every request. NodePlus reads it in place on hardware you control, so there is no data-egress or residency exposure priced into the trade.

Run AI on real work without watching a meter

Book a briefing and we will scope a deployment on local models, priced as a flat subscription with no per-token bills.