Part 2: How to Design and Operationalize Subscription + Usage Models for AI Applications

What is the best way to monetize AI? That is a core question for both operators, founders and investors these days. The answer to that question has changed over the past few years. Initially most companies focused on pure subscription models but now we are seeing a change to hybrid and usage-based pricing modes. 

Last week we had a look at the design of AI pricing and in this breakdown we are focused on the hard part - operationalizing a usage-based or hybrid pricing strategy.

Key Takeaways:

1. Instrumentation is non-negotiable - log unaggregated usage data for auditability and flexibility 
2. Billing systems must unify - calculate subscription + usage together to avoid chaos.
3. Integrate across tools - connect CRM → billing → invoicing → product → analytics.
4. Clarity drives trust - make pricing transparent, visual, and easy to understand.
5. Educate customers on what drives cost and how to optimize spend.
6. Support teams need visibility into usage to explain charges and upsell confidently.
7. Iterate relentlessly - experiment with thresholds, gather feedback, and monitor usage-based metrics like ARPU and churn.

Building the Infrastructure for Instrumentation, Metering, and Billing

There are three things you need to do if you move from simple subscriptions to hybrid:

1. Usage Data Processing: Before you can bill, you need to capture, enrich, and store usage data—this is your instrumentation and metering layer.

A key principle: Store usage data in raw, unaggregated form. This enables:

• Full data lineage for auditability and transparency.
• Flexible re-billing if pricing changes or errors occur.
• Pricing model experimentation on historical usage to optimize future strategy.

Platforms like m3ter emphasize this unaggregated approach to give you long-term flexibility and control over billing.

2. Complex Bill Calculation: Accurate billing requires three sequential steps - Aggregation, billing and pricing log.

Accurate billing requires three sequential steps:

1. Aggregation Logic – Roll up raw usage data into billable metrics (e.g. SUM, MAX, or custom rules).
2. Pricing Logic – Apply your pricing model (e.g. volume tiers, stair step pricing, overages).
3. Billing Logic – Allocate the final amount to invoices, considering billing hierarchies (e.g. parent/child), prepayments, committed spend, and credits.

3. System integration & Data Flow

To enable accurate billing, order data must flow seamlessly from the CRM to your billing engine, and finalized bill calculations should integrate with your invoicing and finance systems (e.g. for revenue recognition).

This requires automated data translation between different system models—and the more automation, the better.

Avoid siloing usage data in the billing system. It should also be available in:

• The product (for customer visibility)
• The BI stack (for analytics)
• Sales & Customer Success tools (to inform renewals, upsells, and support)

A connected, transparent data flow ensures smoother operations and better customer experiences.

Generally these tasks will be beyond the capabilities of your CRM, ERP, and iPaaS solutions, so you’re going to need something ‘off-platform’. There are two broad approaches:

1. A dedicated middle office billing platform - expensive and also disruptive, because various workflows will need to move on to this platform
2. A ‘invisible infrastructure’ approach, where these tasks are performed in the background and the emphasis is on ‘automagically’ enabling existing workflows.  You could build this yourself, or use a vendor solution like m3ter.

Communicating Pricing to Customers

Once pricing is tied to usage, it becomes part of the product experience. Customers don’t just want to know what they’re paying - they want to understand why.

1. Clarity is King: Great pricing pages, invoices and customer-facing billing dashboards are transparent, visual, and easy to navigate. See our review of the Top 20 AI pricing pages here.
2. Proactive Education Reduces Churn: Help users optimize their usage by educating them on: What drives their costs, How to stay within limits, When to upgrade or purchase more capacity. This builds trust and reduces surprise billing.
3. Equip Support to Handle Billing Conversations: Your support and success teams should have real-time visibility into customer usage and billing metrics. This enables them to explain charges, flag anomalies, and guide users confidently.

Monitoring and Iterating

Pricing isn’t a set-it-and-forget-it function—especially in AI. As usage patterns, infrastructure costs, and customer expectations evolve, your pricing model needs to adapt.

1. Run Pricing Experiments

Use A/B tests to experiment with:

• Usage thresholds
• Tier breakpoints
• New monetization units (e.g. per image, per request)

Instrument your product and billing system to track how changes impact adoption and revenue.

2. Build Feedback Loops

Talk to customers regularly. Understand:

• Where they see value
• Where pricing feels unfair or confusing
• What might trigger churn or expansion

Feedback helps spot friction early and informs model changes.

3. Monitor the Right Metrics

Track performance by usage cohort and pricing structure:

• CAC payback period
• ARPU (Average Revenue per User)
• NRR (a measure of expansion)
• Churn
• Gross margin by usage level

These metrics help you evaluate whether pricing is driving sustainable growth.

4. Embrace Change with a Flexible System

Expect pricing changes to be ongoing. Choose systems and processes that make it easy to:

• Update pricing logic
• Rebill or simulate past data
• Roll out changes gradually across customers

Operationalizing pricing is where strategy meets reality. In Part 1, we explored how to design AI pricing; here, we covered how to bring it to life. With the right systems, transparency, and iteration, hybrid models can drive sustainable growth and better customer experiences.

To learn more feel free to contact us.