Usage‑Based AI Pricing Models: The Future of SaaS

Introduction

The rise of artificial intelligence (AI) is transforming how businesses operate, build products, and deliver value. With that transformation comes a shift in how SaaS (Software as a Service) companies price their offerings. Gone are the days when simple monthly subscriptions or flat licensing fees were enough. In 2025 and beyond, usage-based pricing (UBP) is becoming the new norm, especially for AI-powered services.

Why? Because AI applications are resource-intensive and operate differently from traditional SaaS tools. Charging per user or per month doesn’t reflect the true value — or cost — of services powered by large language models, image generators, or inference engines. Usage-based AI pricing aligns cost with consumption, giving both providers and customers better control and transparency.

At KanhaSoft, we recognize this shift and are helping companies adapt. Whether you’re building an AI-powered CRM, chatbot, analytics tool, or custom SaaS platform, usage-based models can unlock new revenue, reduce friction, and align with how modern businesses consume software.

What Is Usage-Based AI Pricing?

Usage-based pricing (also known as consumption-based pricing) is a model where customers pay based on how much they use a service, rather than a flat monthly fee. In the context of AI-powered SaaS, usage can be measured by:

• Number of API calls
• Compute time or GPU minutes
• Number of processed documents or images
• Tokens used (as seen with OpenAI models)
• Storage or data volume

This model stands in contrast to traditional seat-based or tiered subscription models, where companies often overpay for unused capacity or get locked into plans that don’t scale with actual usage.

For example, an AI text generation API might charge $0.002 per 1,000 tokens used. A customer running a lightweight chatbot could pay $20/month, while a large enterprise automating documentation could scale to $2,000/month.

Why Is This Model Becoming Popular Now?

There are three primary reasons usage-based AI pricing is gaining traction:

1. AI Infrastructure Costs Are Non-Trivial: Running AI models — especially large language models (LLMs) and vision transformers — is expensive. GPU compute, inference pipelines, and data processing consume real-time resources. Vendors need a pricing model that reflects those dynamic costs.
2. Customer Demand for Flexibility: Modern SaaS buyers, especially tech-savvy enterprises, don’t want to be boxed into inflexible contracts. They prefer models where they can start small, evaluate performance, and scale usage as ROI becomes clear.
3. Shift Toward Product-Led Growth (PLG): In a PLG strategy, users often try the product before committing. Usage-based pricing encourages initial experimentation while ensuring the vendor can monetize as usage grows.

A 2024 study by OpenView Partners found that more than 61% of new B2B SaaS products are exploring usage-based pricing models.

Benefits of Usage-Based AI Pricing

For Providers

• Revenue Grows with Usage: As customers use more AI services, revenue scales accordingly without requiring additional sales cycles.
• Lower Entry Barrier: Startups or small clients can begin at low usage, increasing conversion rates.
• Data-Driven Monetization: You can analyze customer behavior and optimize pricing for real-world usage patterns.

For Customers:

• Fair Pricing: Pay only for what is used, reducing overspend.
• Scalable ROI: As value is proven, spend scales up logically.
• Transparency: Clear usage metrics help teams manage budgets with cost alerts and dashboards.

Additionally, usage-based models encourage innovation. Developers and teams are more likely to experiment when pricing scales with usage, creating a win-win scenario for both parties.

Common AI Pricing Models in SaaS

There’s no one-size-fits-all model. Many AI-powered SaaS platforms blend usage-based pricing with traditional models to suit different user needs. Below are some popular pricing structures:

1. Pure Usage-Based (Pay-as-You-Go):

• Example: OpenAI charges per token used.
• Ideal for APIs and back-end services.

1. Tiered Consumption:

• Example: Hugging Face offers plans based on monthly token limits.
• Customers get predictable costs with volume discounts.

1. Hybrid (Base Subscription + Overage):

• Most popular structure in 2025.
• Users pay a monthly base fee with credits; overages are billed as needed.

1. Outcome-Based Pricing:

• Based on real-world performance (e.g., leads generated, conversions).
• Used in niche enterprise setups where business outcomes are measurable.

How to Implement Usage-Based Pricing

At KanhaSoft, we help clients implement AI SaaS products that are billing-aware and future-ready. Here’s a 5-step approach we use:

1. Choose the Right Usage Metric:

• Tokens, API calls, time-on-task, etc.
• Align with the customer’s perceived value.

1. Build Transparent Tracking:

• Real-time dashboards, usage alerts, and reports.
• Helps customers control spend.

1. Design Flexible Plans:

• Offer tiers with usage credits.
• Allow auto-scaling or rollover options.

1. Integrate FinOps Tools:

• Cost prediction and alerts make billing predictable.
• Prevents surprise overages.

1. Pilot & Optimize:

• Run pricing experiments.
• Iterate quarterly based on customer feedback.

Real-World Examples Leading This Shift

The industry is already seeing major adoption of UBP in AI contexts:

• OpenAI: Pricing by token usage; widely adopted by AI developers.
• AWS and Azure: Charge for compute, storage, and inference calls.
• Midjourney: Charges based on GPU minutes used for image generation.
• Google Vertex AI: Usage-based pricing for model training, deployment, and prediction.
• Indian Startups: Many SaaS startups in India are pivoting to token- and event-driven pricing models to support scale and flexibility.

KanhaSoft is actively consulting clients on building AI platforms with built-in usage metering and monetization layers. Our developers integrate advanced billing logic directly into custom dashboards and SaaS platforms.

Challenges of Usage-Based Pricing

While UBP is powerful, it’s not without challenges:

• Unpredictable Bills: Customers may hesitate if usage costs are not easily forecasted.
• Complex Infrastructure: Usage metering, billing engines, and dashboards require robust backend systems.
• Customer Education: You need to train users to monitor and optimize their usage.

However, with the right tools and guidance, these challenges are manageable. At KanhaSoft, we help SaaS companies design transparent billing systems with cost control mechanisms.

Why KanhaSoft Is the Right Partner

KanhaSoft is a leading custom software development company with deep expertise in:

• AI-powered SaaS platforms
• Usage tracking & metering APIs
• Billing system integration
• Dashboard development & FinOps alignment

We empower startups, enterprises, and agencies to build scalable, flexible, and monetizable platforms using modern pricing frameworks.

Whether you’re creating a new AI product or migrating from flat-rate billing, our team ensures that your pricing infrastructure supports growth and customer satisfaction.

Conclusion

The days of rigid pricing models are behind us. Usage-based AI pricing isn’t just a trend — it’s a response to how software is built, consumed, and scaled in the AI era. SaaS businesses that embrace this model can better align value with cost, reduce entry barriers, and boost customer satisfaction.

KanhaSoft is your go-to partner in this journey. From concept to deployment, we help you implement intelligent pricing mechanisms that scale with your growth.