SaaS and AI pricing for reduced churn and increased profitability.

Valpex supports SaaS and AI businesses that want to connect pricing strategy with practical implementation, from startups and scale-ups to mid-market and investor-backed companies. We work with founders, management and product, sales and finance teams. Three things characterize our approach:

• Specialized in SaaS and AI: Instead of generic frameworks, we bring experience with recurring revenue, value metrics and usage-based and hybrid pricing models.
• Fast start: First analyses usually begin within a week, not after a long preparation phase.
• Lean setup: You work directly with your consultant, who involves management and the relevant teams in decisions and implementation.

Valpex specializes in pricing for software companies and works in a lean setup directly with your team. For some projects, another provider is therefore the better choice:

• Large international programs: A more broadly positioned provider is better suited to extensive, cross-border transformations with many stakeholders.
• Pricing outside software: Valpex specializes in SaaS and AI, not in pricing physical products, e-commerce or retail.
• Concepts that stay on paper: Instead of extensive reports, Valpex delivers measures your team implements, on its own or together with us.

Working with Valpex aligns your pricing with the value of your product: more revenue, healthier margins and less churn, carried by a team that stands behind the new pricing. We work on three levers:

• Monetize new features: New products and AI features get a pricing model that reflects their value, such as usage-based or hybrid pricing, instead of being given away.
• More expansion, less churn: Packaging based on customer value and willingness to pay makes upgrades the natural next step, strengthens Net Revenue Retention (NRR) and lowers the risk of churn.
• Confidence in sales: Clear discount rules, pricing governance and training give your sales team confidence and spare it debates about exceptions.

Working with Valpex begins with a modular pricing audit. We analyze your starting point, shed light on current challenges and conduct targeted interviews with your team. On request, we also join customer interviews and sales calls. This is how we uncover weak spots and untapped potential. As a result, you receive a concrete action plan. You can then either implement it on your own or implement it together with us as your partner.

A pricing project with Valpex builds on the pricing audit. Initial strategic insights emerge within weeks; measurable revenue effects typically materialize after three to four months. We follow five phases that keep the effort for your team manageable:

• Analyze: We build a fact base from your data, customer interviews and the sales calls we join.
• Design: We develop the pricing model, value metric, packaging, price points and commercial rules.
• Test: We validate the new logic with customers, data and your team before it goes live.
• Implement: We launch the new pricing with clear ownership, a plan for existing customers and training for your team.
• Control Results: We measure the impact on revenue and conversion and feed the insights into the next cycle.

Yes, Valpex also supports implementation, because a pricing strategy only creates value if it can be implemented successfully. Depending on the engagement, we support price migration, packaging changes, customer communication, sales enablement, discounting rules, quoting processes and pricing governance. We can also help define how existing customers should transition to the new model and how exceptions should be handled. After launch, we define the metrics and review process needed to evaluate the new pricing and continue improving it based on real customer and market feedback.

Valpex derives prices from the value your customers receive and from your data, not from gut feeling or what competitors charge. That is exactly where do-it-yourself pricing often falls short and leaves revenue on the table. Three things make the difference:

• Evidence instead of gut feeling: We determine customer value and willingness to pay with customer interviews, commercial data and proven methods rather than internal estimates.
• Experience with typical pitfalls: We know value metrics, packaging and existing-customer migrations from consulting and from our own operational responsibility.
• The whole system in view: We align price points, packaging, sales and product, not just the price list on the website.

There is no single best SaaS pricing model. The right model depends on how customers receive value, how usage scales, how predictable pricing needs to be and how the product is sold. Common models include flat-rate, per-seat, usage-based, tiered and outcome-based pricing. In many cases, hybrid pricing works well because it combines a predictable base fee with usage-, feature- or value-based expansion. The objective is not to follow a pricing trend, but to choose a model that customers understand, reflects the value they receive and supports sustainable growth.

There is no reliable formula for calculating one optimal SaaS price. Value-based pricing starts with the economic value or benefit the product creates for the customer, but willingness to pay, competitive alternatives, customer segmentation and the economics of serving each segment also matter. We typically combine customer interviews, commercial data and willingness-to-pay research. Methods such as Van Westendorp can help establish a credible price range, but they should not be used in isolation. The final price needs to fit the broader pricing and packaging strategy as well as actual buying behavior.

A free trial is often better for complex B2B SaaS products that require explanation, onboarding or a defined evaluation period. It can also be preferable when infrastructure, support or AI inference costs per user are meaningful, because it limits the cost of maintaining a large base of unproductive free users. Freemium can work well when the product is easy to adopt independently, the free experience delivers meaningful value and there is a natural path from free usage to paid expansion. The decision should be based on customer behavior, conversion dynamics and unit economics rather than on what competitors are doing.

Continuously. That does not mean constantly changing prices. Pricing should be managed like any other core part of the business: reviewed, improved and adjusted as the company and its market evolve. A good pricing governance framework defines ownership, review cycles, relevant metrics and how pricing decisions are made. Changes in customer value, competition, product capabilities, costs, market conditions or go-to-market strategy can then be addressed systematically rather than reactively.

There is rarely one decisive indicator. ARR and MRR show recurring-revenue momentum, while ARPA and ACV reveal changes in account and contract value. NRR and GRR separate expansion from retained revenue, while logo churn and revenue churn show whether the business is losing customers or a disproportionate share of revenue. LTV:CAC, CAC payback, conversion rates, discount levels and expansion behavior can also be relevant. None of these metrics should be read in isolation. The business model, customer value, segmentation, competition, market dynamics and financial economics need to be understood together.

A SaaS pricing strategy project can range from answering one focused commercial question to redesigning the entire pricing and packaging model. Typical areas include customer and market research, willingness-to-pay analysis, customer segmentation, value metrics, packaging, price points, discounting, expansion logic, monetization and pricing governance. Depending on the situation, the work may also cover migration, sales processes and implementation. The objective is not simply to find a higher price, but to build a pricing system that aligns customer value, willingness to pay, product economics and commercial strategy.

Enough to make a well-supported decision, but not necessarily a large research program. We typically combine targeted customer interviews with commercial data, win/loss insights, product usage, internal expertise and existing market knowledge. The right amount of research depends on the importance and uncertainty of the decision. A major redesign of pricing and packaging usually requires more evidence than a focused change to one package or price point. The goal is to understand customer value, willingness to pay and meaningful differences between customers, not to conduct research for its own sake.

Yes, but the design and implementation of the price increase matter as much as the new price itself. The right approach depends on customer value, segmentation, contracts, migration paths and the size of the change for individual customers. Options can include grandfathering, phased migrations, differentiated treatment of customer groups or changes to packaging. Communication and sales preparation also matter, particularly when customers need to understand why the pricing is changing. Good pricing is not about charging every customer as much as possible. It creates a better fit between price, value and long-term customer economics.

There is no universal AI pricing model. Depending on customer value and the cost structure, subscriptions, usage-based pricing, token- or credit-based systems, AI add-ons, hybrid models and outcome-based pricing can all make sense. For AI agents, per-task and success-based pricing are also possible, while human-in-the-loop delivery can materially change the cost to serve. We assess the value metric, compute and inference costs, usage predictability, gross-margin architecture and time-to-value together before choosing a model. The challenge is to create pricing that captures value while remaining understandable and predictable for the customer.