Agents loop. The meter runs on every turn.

An AI agent solves a task in steps, and because the cloud does not remember the previous step, it charges you to re-read the whole conversation at every one of them, even after caching discounts. Call it the memory tax. This model compares three ways to pay for the same work over three years: the standard cloud plan most companies have, the optimized cloud plan a smart AI team would build, and a hybrid plan that moves work onto devices you already own.

Every figure on this page is computed by the engine from your scenario at request time. Nothing is hand-typed. The ways this model deliberately favors the cloud baselines are published in full below.

The memory tax, in plain terms

An AI agent solves a task in steps: plan, act, check, repeat. The cloud does not remember the previous step, so the agent sends the entire conversation back up on every step, and the cloud charges to re-read it every time. Most of what you pay for is not the answer. It is the agent re-reading its own notes. Caching discounts that re-reading; it does not eliminate it.

For the architects, the precise mechanics we model: on the first turn the full context is written to the provider's cache at the write-premium rate. On every later turn the accumulated history is billed at the cache-read rate, and only the new tail (this turn's tool result plus the prior turn's output) is written at the premium rate. Output tokens are billed at the output rate on every turn. That is the entire cost mechanism; nothing else is modeled.

What this model prices, and what it does not

People are billed by the seat because people are slow and predictable. Agents are billed by the token because they are neither. Your seat subscriptions (Copilot and its peers) cover the first kind of work; this model does not price them and does not argue against them. Keep your seats, keep your laptops, both are table stakes. The decision priced here is where the agents run.

Where this model favors the cloud

This model is biased against its own conclusion.

The cloud baselines are granted perfect caching: every turn is assumed to hit the cache, and no cache entry ever expires between turns. Real deployments miss caches and pay re-write costs. The cloud baselines also receive percent annual price deflation, applied in full and on schedule, even though the current memory squeeze is pressing provider costs in the other direction. The hybrid line is charged its full hardware bill up front, in Year One, with no residual value credited. The cloud lines on this page are floors, not estimates.

What the strategy is charged for

Only hardware the strategy requires is billed to it. The Copilot+ NPU refresh is priced at zero by default because fleets are getting that tier regardless of any AI decision; the slider in Advanced settings charges it back if you disagree. The RTX Spark tier, the hardware this page actually prices, is charged in full in Year One.

What the hardware actually is

What is an assumption, and whose

The local absorption rates for Years Two and Three are scenario assumptions under your control, not predictions. Your current scenario routes percent of task volume to the routine tier, absorbs percent of the complex tier locally in Year Two and percent in Year Three. Set the absorption sliders to zero and the hybrid strategy loses; the model will say so.

Rate cards

What one task costs in the cloud, Year One

Generated from the engine's unit costs at these rate cards, so you can see where the money concentrates before any scale is applied.

Trace it yourself

Every figure above is a field in this response. If you find one that is not, the build is broken and we want to know.