6 Proven Pricing Models for AI SaaS (and How to Build Them)

In the AI SaaS world, pricing is more than just numbers—it’s product strategy. The most successful AI companies match revenue with actual value delivered, whether that’s GPU time, token usage, or team-based productivity.

This guide breaks down six pricing models that are working right now for leading AI SaaS companies—and shows how you can implement them. Whether you’re just getting started or evolving beyond flat subscriptions, these models offer the flexibility your business (and your customers) need.

All of these models can be built using Lago, the open-source billing engine that gives product and engineering teams full control without the overhead of legacy tools.

1. Flat-Rate Subscription

Flat-rate pricing charges users a simple, recurring fee for access to a set of features or resources. It’s easy to understand, budget for, and manage—especially for individuals or smaller teams testing your product.

Example: Midjourney charges $10/month for 200 GPU minutes, renewing automatically unless canceled.

To build it, you’ll define a fixed monthly or yearly charge and track usage in the background (if desired). This model is easy to launch with Lago, which handles recurring billing and can display non-billable usage to users for transparency.

2. Pay-As-You-Go (Usage-Based Pricing)

Pure usage-based pricing means customers pay only for what they use—tokens, images, inference calls, etc. It removes friction from onboarding and scales revenue directly with value delivered.

Example: OpenAI charges based on token consumption, with no fixed subscription fee.

You’ll need to define your pricing unit (e.g., token or API call), assign a rate, and track consumption in real time. Lago makes this easy with event-based metering and live cost projections, helping customers avoid billing surprises.

3. Tiered or Volume-Based Usage

In tiered pricing, unit costs decrease as usage grows—either gradually (graduated pricing) or all at once (volume pricing). This rewards high-volume customers and encourages deeper engagement.

Example: Hugging Face offers GPU inference pricing that decreases for higher hourly usage.

To build this, define usage bands with specific rates for each range. Decide if pricing applies per band or across the entire volume. Lago supports both models and allows product teams to manage them without writing custom billing logic.

4. Hybrid Subscription + Overage

A hybrid model bundles a base allowance in a subscription, with additional usage billed on top. It combines predictable spend with flexibility—great for AI workloads that vary over time.

Example: Perplexity Pro offers daily query limits as part of a subscription, with pay-as-you-go pricing once the quota is exceeded.

To implement it, define a base plan that includes usage credits, then set up overage pricing for anything beyond the limit. Lago supports bundled credits and alerts when usage is high, so customers stay informed and you stay in control.

5. Seat-Based Pricing

Seat-based pricing charges based on the number of human users accessing your product—rather than compute or tokens. It’s a natural fit for AI productivity and collaboration tools.

Example: GitHub Copilot Business charges $19 per developer per month, independent of usage volume.

To build it, track the number of active users on an account and apply your per-seat rate. Lago supports seat-based billing by tracking user count via events and supports enterprise-grade setups like minimum seats or annual invoicing.

6. Prepaid Credits / Wallets

With prepaid credit models, customers fund a wallet up front and spend credits as they use your service. This offers budget control and lets you collect revenue before usage occurs.

Example: Midjourney allows users to purchase GPU credits that are burned down as needed.

To build this model, offer credit packs (e.g., 10 GPU hours), set usage rules to reduce balances, and notify users as balances run low. Lago supports native wallets, conversion rules, auto-topups, and liability tracking—ideal for usage-heavy AI tools.

Final Takeaways for AI Product Builders

Modern AI products need modern monetization. These pricing models aren’t just billing options—they’re growth levers. The key is flexibility and control. Start with usage to lower onboarding friction. Shift toward hybrid or subscription models as usage stabilizes. Show customers real-time spend to build trust. Offer commitment options like prepaid bundles or seat-based pricing to grow revenue predictably.

Lago gives you the flexibility to support all of these models, without locking you into a rigid structure or over-engineering your stack. It’s built for product teams who want to ship pricing fast—and get back to building.

FAQ

Q: What’s the best pricing model for an early-stage AI SaaS?

A: Start with usage-based or prepaid credits to reduce friction, then evolve toward hybrid or subscription models as engagement increases.

Q: Can I support multiple models at once?

A: Yes. Many successful companies combine subscriptions, usage overages, and prepaid options to maximize flexibility. Lago lets you do this without writing custom logic.

Q: How does Lago help reduce billing complexity?

A: Lago offers real-time metering, flexible pricing logic, native wallets, and built-in versioning—making it easy to adapt your pricing over time without developer bottlenecks.

Build a Billing System That Grows With You

If you’re building an AI-native SaaS, your pricing model will evolve—and your billing engine needs to keep up. Lago gives you full control, real-time transparency, and open-source flexibility.

Ready to align revenue with customer value? Book a demo with Lago or try it on your own terms.

Let me know if you’d like this adapted into a PDF, a Notion doc, or a talk track for customer onboarding.

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