本地部署AI應用貨幣化：積分、路由同使用限制

當客戶控制嘅部署可以通過批准嘅連接路徑發送選定嘅AI請求時，本地AI應用程式嘅貨幣化變得實際可行。應用程式可以保持安裝喺客戶嘅環境中，而其可變推理使用量會被單獨測量同定價。.

呢個區別好重要。一個隔離網絡嘅安裝無法使用連接嘅推理路徑。一個連接嘅本地產品可以，但只限於客戶批准嘅請求、數據、模型同環境。.

對於軟件供應商嚟講，商業問題係簡單嘅：永久許可證、年度合同或者座位價格係可預測嘅，而AI使用量唔係。一個部署可能每星期生成幾個摘要。另一個可能每日運行數千個文檔、支持、搜索或者代理任務。.

答案唔係將產品移出客戶控制範圍，而係為合資格嘅AI功能創建一個清晰嘅使用層。.

點解本地AI應用程式貨幣化需要一個連接邊界

“本地”描述咗產品運行嘅地方。佢唔係自動意味住每個AI請求都必須喺本地處理，亦唔係意味住每個部署都可以將請求發送到其環境外面。.

喺定價之前，將部署分為兩條路徑：

• 隔離網絡或者完全本地： AI處理保持喺客戶嘅環境內。ShareAI路由嘅貨幣化唔適用於呢啲流量。.
• 連接或者選擇性連接： 批准嘅AI請求可以使用外部路徑。呢啲請求可以被標記、計量、限制，並作為單獨嘅使用流定價。.

喺架構文檔、訂單表格、產品設置同面向客戶嘅使用語言中明確呢個邊界。唔好將連接使用模型當作係離線功能嚟賣。.

將軟件許可證同可變AI使用分開

一個本地許可證通常支付產品訪問、部署權利、支持、維護或者約定用戶數量嘅費用。AI推理會產生另一條成本曲線。.

官方模型文檔顯示咗原因：模型API通常區分輸入同輸出使用量，而費率會因模型同功能而異。睇下 OpenAI 模型目錄 同埋 Claude定價文檔 用於當前例子。.

嘗試將嗰個變量使用隱藏喺一個無限軟件費用入面會造成兩個可避免嘅問題：

• 輕量客戶可能會補貼重量客戶。.
• 當請求量、上下文大小、輸出長度或者模型選擇改變時，供應商承擔利潤風險。.

一份更清晰嘅合同將耐用軟件權利同可選嘅連接AI消耗分開。客戶可以理解牌照涵蓋咩同咩會產生額外使用。.

喺設計信用之前選擇一個使用單位

信用最好係映射到客戶已經理解嘅單位。由產品行動開始，然後計算背後嘅推理成本。.

AI 功能	面向客戶嘅單位	成本驅動因素監控	有用嘅控制
文件提取	頁面、文件或者完成嘅工作	輸入大小、模型、輸出架構、重試次數	文件同每月工作上限
支援助手	草稿、對話或者解決嘅案例	上下文長度、回應長度、工具調用	每工作空間預算
RAG搜索	查詢或有根據嘅答案	檢索、重新排序、提示大小、輸出	每日查詢限制
AI代理	運行、步驟或完成嘅工作流程	模型調用次數、工具、重試次數	最大步驟同支出

面向客戶嘅單位應該穩定到足夠用於預算。內部計量應該保持足夠詳細去解釋成本、診斷異常同改善路由。.

將信用視為包裝，而唔係真相來源

信用係一個方便嘅產品抽象。佢唔應該取代準確嘅使用記錄。.

喺推出之前定義呢啲規則：

1. 每個AI功能嘅一個信用代表咩。.
2. 唔同模型或操作是否以唔同速度消耗信用。.
3. 邊個津貼係包含喺軟件協議入面嘅。.
4. 當津貼快用晒嘅時候會發生咩事。.
5. 客戶可唔可以批准加額、提高上限、轉換模式或者停止連接AI使用。.

避免每個工作流程都用單一不透明嘅信用價格。一個簡短嘅摘要請求同一個多步驟嘅代理運行可以有好唔同嘅成本概況。.

用部署層面嘅上下文路由合資格嘅請求。

Connected on-prem monetization depends on attribution. Every routed request should identify the commercial context without exposing unnecessary customer data.

Useful routing and reporting fields include:

• customer or account identifier;
• deployment identifier;
• workspace, department, or tenant identifier;
• feature and usage-event type;
• environment, such as production or test;
• selected model or routing policy;
• request identifier for retry and duplicate handling.

The application remains outside ShareAI. For eligible connected usage, the product sends approved inference traffic through ShareAI. The team can review the ShareAI文檔 while planning the integration boundary.

Do not treat request tags as a compliance claim. They are operational metadata for attribution, reporting, support, and usage controls. Each vendor and customer must still evaluate data handling, network, model, security, and contractual requirements for their environment.

Add usage limits that protect customers and the product

Good limits are visible before they become blockers. Use several layers:

• 包含配額： A defined amount of connected AI usage included with the commercial package.
• Soft alerts: Notifications at predictable budget or credit thresholds.
• Hard caps: A customer-controlled stop that prevents unapproved overage.
• Administrative approval: A clear path to add credits or raise a budget.
• Workflow limits: Maximum file size, context size, agent steps, retries, or output length.
• 後備行為： A defined product state when connected AI is unavailable or a cap is reached.

The product should show remaining allowance, recent usage, and the event that consumed it. Customers should not need to reverse-engineer a bill from token logs.

How ShareAI Builder handles the money flow

ShareAI is the routing, usage, billing, margin, and payout layer for eligible AI traffic. It is not the application builder or the on-prem deployment platform.

The flow is:

1. Your team builds and operates the application outside ShareAI.
2. Eligible connected AI requests route through ShareAI.
3. You configure a surcharge or margin for that application traffic.
4. 客戶為路由嘅AI使用向ShareAI付款。.
5. ShareAI routes the inference through its marketplace.
6. ShareAI 每月根據嗰啲流量產生嘅收入向Builder支付。.

Builder payouts are tied to traffic from the Builder’s application. They are separate from Provider rewards for contributing eligible compute capacity.

On-prem AI app monetization implementation checklist

• Classify each deployment as air-gapped, local-only, connected, or selectively connected.
• Identify the AI workflows allowed to use a connected route.
• Choose a customer-facing unit for each workflow.
• Record the model, request, deployment, workspace, feature, and environment context needed for attribution.
• Define included allowances, alerts, hard caps, and approval paths.
• Explain what the software license covers and what creates paid AI usage.
• Design product behavior for exhausted credits, network failure, routing failure, and model unavailability.
• Test retry and duplicate handling so one customer action is not counted twice.
• Give customers a clear usage view and support process.
• Review the architecture and data path with the customer’s technical and commercial stakeholders.

Frequently asked questions

Can on-prem software use ShareAI Builder?

Yes, when the on-prem application can route eligible AI requests through an approved connected path. The application remains built and deployed outside ShareAI.

Does ShareAI host the on-prem application?

No. ShareAI provides the routing, usage, customer-payment, margin, and monthly payout layer for AI traffic routed from the existing application.

Does this model work for air-gapped deployments?

Not for traffic that cannot leave the environment. Air-gapped AI needs a fully local processing and commercial model. ShareAI-routed monetization applies only to eligible connected requests.

What should an on-prem AI product meter?

Meter both the customer-visible event and its main cost drivers. Common fields include deployment, workspace, feature, model, input size, output size, tool calls, retries, and completed jobs.

Are credits better than token-based billing?

Credits are often easier for customers to understand, while tokens and model events remain useful behind the scenes. A good design maps credits to clear product actions and keeps the underlying usage auditable.

How should BYOK fit into the pricing model?

Treat BYOK as a separate route with explicit support boundaries. Decide which features allow customer keys, who handles provider billing and failures, and whether ShareAI-routed usage remains available as another option.

Can customers set deployment-level usage caps?

They should be able to. Deployment, workspace, and feature-level caps make budgets easier to control and reduce surprise overage.

How do customers pay for ShareAI-routed usage?

For the Builder flow, the customer pays ShareAI directly for routed AI usage. The Builder’s configured margin is attached to that application traffic.

How are Builder earnings paid?

ShareAI pays the Builder monthly based on generated earnings from eligible routed traffic. Earnings depend on actual usage and the configured margin; they are not guaranteed.

Is a Builder payout the same as a Provider reward?

No. A Builder earns from traffic generated by an application they own or maintain. A Provider earns through an approved program for contributing eligible compute capacity.

Does connected routing make an on-prem product compliant or private by default?

No. Deployment location alone does not establish compliance or privacy. The vendor and customer must evaluate the complete data path, model, provider, retention, security, and contractual requirements.

When is ShareAI a good fit for an on-prem AI product?

It is a strong fit when the product stays customer-controlled but some approved AI workflows can use connected inference, usage varies by deployment, and the vendor wants a routed billing and Builder-margin layer.

Start with one connected AI workflow

Choose one expensive or high-value AI action, define its unit, tag it by deployment, add a customer-controlled cap, and test the full payment and fallback experience.

打開 建設者控制台 to define the routed usage path and Builder margin for an application you already own or maintain.