# Model Execution & Token Metering

*Compute is metered, and usage is monetized.*

In ArtisanAI, AI model inference is not a passive backend operation — it is a token-incentivized, programmable economic activity. The protocol treats each model as a first-class on-chain actor, with usage governed by deterministic rules, resource tracking, and monetization logic.

#### Model Execution Workflow

When a user initiates a content generation request (e.g., by submitting a prompt), the following sequence occurs:

* Model Selection

The request is routed to either a public protocol-curated model or a user-deployed custom model.

* Inference Execution

The selected model processes the input and generates output on distributed or edge compute.

* Usage Logging

The model’s CID, user wallet, execution parameters, and timestamp are logged on-chain.

* Metering & Cost Calculation

Based on complexity and compute consumption, the execution is priced in $ART tokens.

* Settlement & Distribution

$ART tokens are programmatically distributed to the model creator, infrastructure operator, and protocol treasury.

<br>

#### Dynamic Token Metering

The metering algorithm adjusts execution cost based on:

* Model Type & Size
* Inference Latency
* Priority Tier
* Storage Access
* Concurrent Load

<br>

#### Monetization Rights for Model Owners

Model developers can configure monetization terms via smart contracts:

* Fixed or tiered pricing curves
* Access permissions
* Remix licensing splits
* Revenue streaming to DAO treasuries

<br>

#### Transparent Execution Trails

All inference events are:

* Recorded on-chain
* Linked to input & output assets
* Auditable & queryable
* Incentive-linked


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.artisanaiai.xyz/designing-the-creator-protocol-architecture-and-interfaces/model-execution-and-token-metering.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.