Strategic Pricing Models

"Is $20/month too much for our product?" Instead of guessing, we used the Van Westendorp method to find our pricing sweet spot. 4 questions revealed exactly what users would pay (and we haven't touched our pricing since). Here's the framework any founder can steal: 1. Send a survey to actual users, not prospects We surveyed people already using Gamma. They understood the real value of our product, not hypothetical value. Too many founders survey their waitlist or randomly select people who have never used their product. That's like asking someone who's never driven about car prices. 2. Ask these 4 specific questions - At what price would this be too expensive for you to consider it? - At what price is it expensive but still delivering value? - At what price does it feel like a bargain? - At what price is it so cheap you'd question if it's reliable? These create bookends for perceived value. You're mapping the entire spectrum of price psychology, not just asking "what would you pay?" 3. Plot the responses and find where the lines intersect Graph responses from lots of users. Where "too expensive" and "too cheap" lines cross: that's your acceptable range. Where "expensive but fair" meets "bargain": this is your optimal price point. 4. Test within the range, don't just pick the middle The intersection gives you a range, not a number. We ran pricing experiments within that range to see actual conversion rates. A survey shows willingness to pay; testing reveals actual behavior. 5. Lean towards generous (especially for product-led growth) We chose to be more generous with AI usage than our "optimal" price suggested. Word-of-mouth growth matters more than maximizing initial revenue. Not everything shows up in the numbers. 6. Lock it in and stop tinkering Once you find the sweet spot through data, stick with it. We haven't changed pricing in 2 years. Every month debating pricing is a month not improving product. Remember: pricing is a signal, not just a number (Image: First Principles)

Salesforce just fired the starting gun on a seismic shift in how we pay for software. At Salesforce #Agentforce, they announced they’re moving away from the traditional per-seat SaaS model to a consumption-based pricing for their AI agents. This is huge. Why? Because it signals the end of paying just to have access to technology. Instead, we’re moving toward paying for outcomes—the actual value delivered. Think about it. In a world where AI agents can perform the job functions of entire departments, does it make sense to charge per seat? Probably not. Here’s what’s changing: - From access to outcomes: Companies will pay for what the AI actually accomplishes. - From subscriptions to value: Pricing adjusts based on usage and results. - From Software-as-a-Service to Agent-as-a-Service: Technology that collaborates with you as a partner This isn’t just a tweak in pricing—it’s a radical upending of commercial models for large SaaS companies. What does this mean for businesses? - Budgeting will evolve: Costs align directly with value received. - ROI becomes clearer: Easier to measure the direct impact of technology investments. - Greater flexibility: Scale usage up or down based on needs without worrying about seat counts. It’s an exciting time, but also a challenging one. Is every SaaS company ready to embrace a model where companies pay directly for the value they receive? At Uniti AI, we’ve been thinking along these lines. We price our AI agents based on the amount of work they do, not on how many seats a company has. I believe this is the future. What do you think? Is the per-seat model on its way out?

The question I hear most from founders during Sequoia Capital's Arc program is about #pricing. Pricing is one of the most underutilized levers for startups. Why does it matter so much? It has the most direct impact on revenue, and the moment you establish your pricing, you determine your TAM. Getting the pricing metric right is, by far, the most important one. The key is to imagine the future: when you are a large and successful company, how have you changed the world, and what metric correlates best with your success? Hitch your financial wagon to that metric! If you are Figma, success is all designers using the app; therefore, the pricing metrics is per designer seat. If you are VMware, success is all workloads run in virtual machines; therefore, the right pricing metric would have been a virtual machine. A pricing metric is like the genie in a bottle: once you get it out, it is tough to rein it back or change it. The pricing model is about when and how frequently you charge. Recurrent subscriptions are the predominant model for SaaS apps, and usage-based pricing is the model for infrastructure solutions. Usage-based pricing creates a beautiful alignment of incentives but is less predictable. Upfront credit purchases and commitments are efforts to make usage-based practice more aligned with the rigid corporate budgeting processes. You can be the premium solution or the affordable one. Both are legitimate approaches. But your pricing needs to be consistent with the rest of your strategy: with your product and distribution channels.  You can’t have an affordable solution distributed through an expensive enterprise sales force. In this case, you need to sell either online or through inside sales—the product better be simple and the sales cycle quick. Many technical founders are shy about asking for a lot of money for their product. Don’t be. If customers like the product and it delivers value, they will gladly pay for it. Unless you hear customer complaints that you are expensive, then for sure you are underpricing. Calculate the ROI of your product, and take 20% of that value as your price point. How much it costs you to build the solution should not guide your pricing. But you should do a sanity check that you have a decent gross margin. Most companies start by selling a single package. Over time, they realize that different customer segments have different maturity levels and willingness to pay. To price discriminate between these segments, you need to introduce multiple packages.  Start by creating a customer maturity curve to inform your decisions on how many packages you need. The trick is to have the smallest number of packages to cover the broadest range of customer needs. Your packages will change and evolve quickly as your product matures. 

Set and forget is not a pricing strategy ! Price--> Design--> Build We know that's what everyone says, but thats an oversimplification of what the entire process should look like. The assumption your pricing was correct in the pre-design phase and doesn't need change is dangerous, dangerous, dangerous !! I have seen too many physical and software products change drastically between initial design to final delivery. Product owners will typically assume that pricing still holds. You have to change that philosophy. In the real world we need a lot more iteration in price: Step 1: Initial Price: This stage you quantify the value and set an initial target price. This is a combination of internal/external research, some value quantification and pricing knowledge. Step 2: Design: With that price info, the product team designs a product that hits product and profitability targets. This is also where you need to keep track of the product margins. Often product will go design a better product at the expense of higher cost, and margins suffer before launch. Step 3: Reprice: Now that we know the new design constraints that impact the profitability, this stage gives you the opportunity to reprice the product based on the design. If substantial value has been added, price should go up. Do not fall into the 'lets over deliver on value and keep price same' trap. Step 4: Build: Now with that new price info and product roadmap the product goes through the build stage. Step 5: Pre launch reprice : Now significant time may have passed since last price review. The market for the product, the economy etc may have changed. This stage can assist in making last changes before product goes out. Good time to also establish guardrails for price performance, discount strategy, or sales strategy. Step 6: Launch: Goes without saying the product is out in the real world. Great way to capture feedback. Also a stage where performance is measured against the price guardrails. Step 7: Reprice 3: Based on sales feedback, you start charting next steps. Selling too slow, you may need discount or reprice. Selling too fast, it may be overdelivering on price vs value. Pricing metric may need change. Fx may have changed. This is the price adjustment stage, should be annual or semi annual. You can incorporate these steps into new product introduction framework or annual or semi annual pricing strategy process, either ways it will help establish good pricing principles in the org. I know of many products that once designed were never repriced years into its life.. Surely things must have changed all those years... Think of Pricing as a lifecycle !! -------------------------- We are in #Pricingtribe.

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$7,225 for one day of coding. And Cursor isn't even the worst example. Replit's margins went negative. Anthropic throttles its best users. I mapped pricing across 50 AI startups. Six distinct patterns emerged. The core tension: traditional SaaS has near-zero marginal cost per user. AI products pay for compute on every interaction. A casual Claude user costs pennies. A developer running Claude Code all day costs tens of thousands per month. Your best users are your most expensive users. That tension is breaking every pricing model in the market. Cursor charged a flat 500 requests/month. Worked fine until users leaned into multi-step agent workflows. They switched to credit pools. One developer burned 500 requests in a single day. The plan description changed from "Unlimited" to "Extended" twelve days after launch. Replit grew 15x in ten months ($16M to $252M ARR). But they were buying revenue with compute. When they launched a more autonomous agent, margins crashed to negative 14%. They had to invent "effort-based pricing" mid-flight. Anthropic played it differently. Their $17/$100/$200 tiers map to genuinely different user personas, not volume bands. A casual user and a Claude Code developer are different products with different willingness to pay. The lesson across all 50 companies: before you set any price, pull the cost distribution. What does your P10 user cost? P50? P90? If the ratio exceeds 10x, flat pricing will break. In AI products, it almost always exceeds 10x. Full guide with all 6 models, 4 case studies, and a decision tree: https://lnkd.in/gdKaQSMk

Starting October 1, real-time drug pricing at the point of care is a regulatory requirement. --- HHS and CMS just finalized a new rule that will require providers to use certified health IT systems to: • Submit prior authorizations (PAs) electronically • Access real-time #DrugPricing and coverage information • Share electronic prescriptions with pharmacies and payers This matters because for the first time, millions of patients will be able to see what a drug will cost before they get to the #pharmacy. Plus doctors will be better equipped to choose covered, cost-effective options that don’t get denied later. --- Past research on real-time benefit tools suggests execution will be vital for this to succeed. However, if it does, it could improve adherence and lower patient and plan costs--more research on this tomorrow! For patients, this rule could reduce sticker shock, cut delays due to PAs, and lower the odds of treatment abandonment due to cost. For providers, it likely means fewer faxes, faster approvals, and more informed prescribing (if the technology works as intended). --- Will this lead to fewer denials, faster treatment starts, and less time wasted on appeals? Possibly. It will take full adoption of the tech stack across providers and pharmacies to get there, plus collaborating with payers. Any signs on the ground that these changes have begun to roll out?

You're afraid to raise your prices because you think you'll lose clients. Here's the counterintuitive truth: You might lose some clients, and that's actually strategic. I worked with a professional speaker who raised her minimum speaking fee. She lost 25% of her revenue initially. But here's what happened next. That same price increase saved her 40% of her time by eliminating lower-paying engagements below her new threshold. What did she do with those reclaimed hours? She wrote a book proposal. She developed a signature workshop series. She built relationships with higher-tier event planners. Within 18 months, her revenue was 30% higher than before the price increase. The best clients who truly value your work will stick with you. The ones who leave either can't afford your current level of expertise or weren't aligned with where you're heading anyway. Here's the practical strategy that makes this work: Give existing clients 6-12 months advance notice of your price increase. Grandfather them in at current rates until that date. Why this timeline works: Six months gives them enough time to budget for the change without feeling blindsided. It preserves your current relationship while you're building new work. And it positions the increase as inevitable growth, not a sudden cash grab. The real insight? This isn't just about raising prices. It's about strategically choosing which clients you keep as you level up your business. 🛟 Save this post if you're ready to get paid what you're actually worth. ➡️ Follow Dorie Clark for more strategies on building a business that values your expertise.

Our most underestimated pricing tool? AI. It’s easy to assume that pricing is all about intuition or guesswork, but AI is transforming how businesses approach price optimization. However, AI isn’t a one-size-fits-all solution—it’s a tool that, when used right, can drive smarter, data-backed decisions. Here’s why AI matters for your pricing strategy: → Dynamic Adjustments AI helps businesses adjust pricing in real-time, responding to shifts in demand, market conditions, and competitor activity. It ensures prices are always competitive and aligned with the market. → Data-Driven Insights By analyzing large sets of data—like past sales, customer behavior, and trends—AI helps identify the best price points to maximize profit without alienating customers. → Personalized Pricing AI enables businesses to tailor prices to individual customer segments, increasing both loyalty and conversion rates while optimizing profit margins. → Simulated Scenarios AI allows companies to simulate different pricing strategies and predict their outcomes. This way, businesses can test new approaches without taking unnecessary risks. So, how can you leverage AI in pricing? → Start Small Begin by integrating AI tools that align with your existing pricing strategies, and gradually scale as you learn. → Combine AI with Human Insight AI is a powerful tool, but it needs human judgment to adapt to the nuances of the market and customer sentiment. → Embrace Dynamic Pricing Implement AI-powered dynamic pricing models that adjust in real-time based on factors like demand and competitor actions. AI isn’t just a trend—it’s a game changer for smarter pricing strategies. It’s time to stop guessing and start optimizing. How are you using AI to optimize your pricing strategy? Let’s talk!

Had an interesting insight chatting to a marketer yesterday at SaaStock. By removing pricing from their website they increased the number of enterprise prospects. This resulted a substantial revenue increase. This bucks the trend of product led growth practitioners who often advocate for transparent pricing. Rationale for this: - Encouraging direct communication, which allows for a personalised sales experience - Enterprise needs are often complex and unique. Removing pricing suggests that solutions are tailored - Avoids price-based comparisons, sometimes the meaningful differentiators don’t come across on the website I reckon displaying pricing isn’t only segment specific in terms of your strategy mix, but likely industry & geo specific too (they were targeting US companies). Removing pricing is something you may want to test if you’re targeting enterprise; we still aren’t there yet with self serve.