In the software industry, pricing models in the B2B sector face fresh challenges as traditional, user-based structures struggle to adapt to the pressures of AI and heightened profitability demands.

In a recent episode of the TSIA TECHtonic podcast, host Thomas Lah spoke with Dan Balcauski, founder of Product Tranquility, about practical insights that businesses can use to rethink their pricing strategies. This blog will cover the actionable insights from their discussion, empowering you to make necessary changes.

For complete insights on B2B software pricing today, listen to this podcast episode in the TSIA Portal.

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Shifting From User-Based Models: Why It Matters

Historically, software companies have relied on a per-user pricing model, often charging customers based on the number of users they support. However, as companies deploy AI, which can impact headcount in their client organizations, reliance on per-user models becomes limiting—with fewer or flatlining user counts and growth stalls for companies still operating on legacy models. For businesses, this is a call to revisit their pricing metrics.

When considering new pricing models, companies should carefully weigh several factors to ensure they align with customer needs and business goals:

• Cost vs. value: Ensure your pricing model reflects the true value delivered, not just a metric that once made sense.
• Profitability push: AI advancements mean clients are also more focused on profitability. Pricing models should align with their evolving needs.
• Customization flexibility: In the AI-enabled environment, customers may need customizable pricing structures that better mirror their growth and cost structures.

Related: How to Navigate The Future of B2B Software Pricing

“SaaS companies are under immense pressure to be more profitable. So pricing is a key lever there, and they're trying to rethink, what am I giving away? What am I not giving away?”—Thomas Lah, Executive Director and Executive Vice President, TSIA

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What Are the Options Beyond Per-User Pricing?

During their conversation, Balcauski offered insight into several alternative pricing models that could better align with the evolving demands of B2B clients. These models, focusing on revenue retention and outcome-oriented strategies, can significantly boost your business's profitability and customer satisfaction.

Value-Based Pricing Models

Rather than tying charges to the number of users, value-based models use metrics that track how much value the customer gains from the product. One example Balcauski shared was usage-based pricing for CRM tools, which could instead focus on revenue under management rather than the number of users.

Platform Extension Models

Companies like Microsoft are embracing platform extension models, charging for added value AI modules such as Microsoft Copilot, which works across the Office suite. Customers pay a premium for additional AI capabilities, distinguishing it as a net new value add. This approach allows the base product to remain accessible while enabling users to opt-in for enhanced functionality.

Outcome-Based Pricing

Although uncommon, outcome-based pricing offers a promising approach for aligning charges with specific customer outcomes. AI tools like Intercom’s agent-based features allow companies to charge per successful transaction or closed case, offering clarity and justifying costs. Companies can foster greater customer transparency and trust by attaching pricing directly to measurable results.

Related: From Cost-Plus to Value-Based: The Pricing Evolution in Technology Services

Practical Steps to a Future-Proofed Pricing Strategy

Balcauski emphasized that navigating these pricing shifts is an exercise in understanding internal cost structures and client perspectives. Companies can future-proof their pricing by integrating the following strategies:

• Conduct a pricing metric review: Regularly assess whether your current metric—users, transactions, or features—still aligns with how customers perceive value. Avoid overcomplicating your pricing structure with extraneous metrics, as this can add unnecessary friction to the sales process.
• Rethink free upgrades: When companies attempt to monetize AI, sometimes the most strategic move is offering it as a cost-saving measure rather than a premium service. For example, using AI-driven knowledge bases can reduce support needs and improve efficiency without direct charges to the customer.
• Build a scalable pricing model: Companies in high-growth tech sectors should ensure pricing scales with customer growth. Balcauski notes that while user counts might not rise, metrics such as revenue or usage volume better represent how the customer scales.

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Avoiding Pitfalls in AI-Driven Pricing Structures

A key takeaway from Balcauski’s insights is the importance of maintaining customer understanding and simplicity in pricing models. He advises against incorporating AI-specific costs directly into client pricing through technical terms, such as “tokens” or “credits.” Customers are interested in paying for value—not the infrastructure behind it.

Moreover, as many AI capabilities are still in a transitional phase, companies may need time to clarify the direct value of these tools before passing costs onto the customer. Ensure pricing remains intuitive and value-focused.

Related: Deploying AI to Optimize Customer Outcomes and Financial Results

Moving Forward in a New Era of Pricing Strategy

With the rise of AI, the way companies price B2B software is rapidly changing. While the per-user model will likely remain relevant in some cases, businesses need to evaluate if it continues to align with the value customers receive. Companies can better capture AI's advantages by adopting value-based, platform extension, or outcome-based pricing, while providing a pricing structure that reflects tangible customer outcomes.

For companies focused on sustainable profitability, the question is: Is our pricing model equipped to keep pace with technological advancements and customer expectations? Now is the time to explore alternative models, engage in value literacy, and position your business to thrive in a dynamic, AI-influenced world.

Related: The Future of B2B Customer Engagement: How AI Is Transforming the Buyer-Seller Relationship

Your Key Takeaways

• Reevaluate pricing models as AI disrupts per-user standards: Traditional per-user pricing models may no longer align with the value companies deliver, especially as AI impacts headcount and profitability demands an increase. Shift from user counts to more value-based or usage-oriented metrics that reflect your product’s contribution to customer outcomes.
• Explore alternative pricing structures for lasting value: Consider models beyond per-user pricing, like value-based, platform extension, or outcome-based approaches. Each offers flexibility to capture AI's advantages, enabling companies to price based on the measurable benefits they deliver rather than legacy metrics.‍
• Focus on simplicity and value in pricing communication: Complex pricing metrics like “credits” or “tokens” can alienate customers. Instead, keep pricing straightforward, linking it to clear value drivers. Remember, customers are interested in the outcomes they receive, not the infrastructure or technical elements behind the product.