How to Price a B2B Product When No Competitors Exist

You have built something genuinely new. No competitor page to benchmark against. No Gartner Magic Quadrant for your category. No industry standard pricing to anchor to. This is the paradox of category creation: the very thing that makes your product defensible, its novelty, is what makes pricing it feel impossible.

Traditional pricing frameworks assume a market already exists. Yours does not. Yet. This guide is built on research across hundreds of SaaS companies, pricing studies from Price Intelligently (now SBI), OpenView Partners, Bessemer Venture Partners, and a16z, plus the real pricing journeys of Gong, Snowflake, Slack, Figma, and the latest wave of AI startups. It is designed for B2B founders who are creating categories, not entering them.

Every business school teaches three pricing approaches: cost-plus, competitor-based, and value-based. For category creators, two of these are immediately useless and the third requires reinvention.

Cost-plus pricing, where you take your cost and add a margin, is irrelevant for software. Your marginal cost to serve one more customer is near zero for traditional SaaS with 80-90% gross margins. Even for AI-first products, where margins sit at 50-65%, pricing off cost tells you nothing about the value you are delivering. GitHub Copilot initially lost approximately $20 per user per month despite charging $10/month. Cost-plus would have told them to charge $30, but the market signal they needed was about value, not cost.

Competitor-based pricing is impossible by definition when you have no direct competitors. And anchoring to adjacent categories is one of the most dangerous mistakes a category creator can make. Value-based pricing is the right direction, but the standard methodology assumes your buyers can articulate the value of something they have never seen before. When Gong started selling conversation intelligence in 2015, sales leaders were not sitting in meetings saying they wished someone would record and analyze their sales calls with AI. The category did not exist. The pain was real but unnamed.

Here is what makes pricing a category-creating product fundamentally different from pricing anything else: your buyers have no mental model for what your product should cost. Research on the anchoring effect, one of the most robust findings in behavioral economics, shows that the first price a buyer encounters heavily influences their perception of what something should cost, even when that first price is completely arbitrary.

Harvard Business School researchers have demonstrated that psychological pricing works because people rely heavily on the first piece of information they encounter when making decisions. For category creators, this is both a risk and an opportunity. You are not competing against another price. You are competing against the absence of a price. You get to set the anchor. But if you set it wrong, you are building your entire business on a distorted foundation.

According to Price Intelligently's research across thousands of SaaS companies, the average SaaS startup spends just six hours on their pricing strategy. Not six hours per week or per month, but six hours total to define, test, and optimize everything about how they charge. Compare that to the months spent on product development and the weeks spent on brand identity, and the underinvestment becomes stark.

This matters because a 1% improvement in pricing yields an 11-12.7% increase in profits, according to Price Intelligently, far outperforming equivalent improvements in customer acquisition or retention. Yet 43% of SaaS companies believe they are charging less than the market would bear, per OpenView Partners benchmark data. For category creators without competitive reference points, the problem is even more severe. Underpricing is 2x more common than overpricing among SaaS startups, and it is significantly harder to correct.

Gong is perhaps the purest example of pricing a product in a category that did not exist. When Amit Bendov and Eilon Reshef launched Gong in 2015, conversation intelligence was not a recognized software category. Sales leaders were not budgeting for it. No analyst had written about it. Their pricing discovery was remarkably pragmatic. In January 2016, Gong launched an alpha with 12 design partners who used the product without charge. As Reshef explained, in the early stage he would not worry about payment as a key thing because the users had not seen the value yet.

They focused on one thing: making sure the tool recorded all sales calls reliably. Not features. Not pricing tiers. Just making it work. Once the product was reliable, Bendov contacted the 12 design partners with a simple message: beta is over, time to buy. Eleven of twelve paid immediately. The twelfth eventually paid a year later when he joined a new company and brought Gong with him. Bendov refused to give discounts at the beginning, which forced the team to sell on value rather than price concession.

The result: Gong hit $100K ARR from those initial design partners, $2M ARR within the first year of selling, and $9M ARR the following year. Today, Gong charges a $5,000-$50,000 annual platform fee plus per-user licensing, with consistent 15-20% annual price increases. They created the category, named it, and set the price ceiling. The lesson is clear: your first customers are not paying for a product. They are paying to solve a pain they could not solve before. Price for that.

Snowflake entered a market dominated by legacy players with traditional per-node or per-license pricing. They did not just create a new product; they created a new pricing architecture that became inseparable from their competitive advantage. Mike Scarpelli, Snowflake's CFO, explained the reasoning: they now have the ability to really track compute resources per query that any customer is running. Previously, on-premise software relied on license and per-seat models because hardware was pre-purchased regardless of actual usage. Snowflake's cloud-native architecture made it possible to charge based on what customers actually consumed. The results: 168% net revenue retention rate described as hands-down best in class, 106% year-over-year growth before hitting $1 billion ARR, the largest software IPO in history, and 90% of revenue generated from consumption, not subscriptions.

Slack launched in 2013, entering a space that technically had incumbents like email, HipChat, and IRC, but was creating a fundamentally new category of team collaboration. Their answer was a freemium model with strategically designed constraints: a free tier with genuine utility including the last 10,000 messages, 10 integrations, and 5GB storage, and a standard tier at $6.67 per user per month billed annually. The critical innovation was that Slack only charged for users who were active within the billing period. This fair billing approach delivered an 8.6% free-to-paid conversion rate, significantly above the industry average of 2-5% for SaaS applications, along with 132% net dollar retention in 2020. Salesforce acquired Slack for $27.7 billion.

Figma entered a market dominated by Adobe but created a new category of collaborative, browser-based design. Their pricing innovation was the editor-based model: only active content creators pay while viewers collaborate for free. In design workflows, there are typically many more stakeholders reviewing designs than designers creating them. By charging only editors, Figma removed the single biggest barrier to cross-functional adoption. The results: 77% UI design market share by 2022, over 4 million designers on the platform by 2021, and as of Q1 2025, 76% of customers use two or more Figma products, up from 64% a year earlier. As Madhavan Ramanujam of Simon-Kucher puts it: how you charge is more important than how much.

Before you set a single price, you need to understand three things. First, what is the buyer currently spending to solve this problem badly? Even if no direct competitor exists, your buyer is spending money on something, whether spreadsheets, consultants, manual processes, or a patchwork of tools. That is your true competitive benchmark. Second, what is the measurable outcome your product delivers? Revenue generated, time saved, risk reduced, cost eliminated. Quantify it. Third, what is that outcome worth to different buyer segments? A mid-market SaaS company and an enterprise pharmaceutical company may get the same feature set but derive wildly different value.

Madhavan Ramanujam, whose research at Simon-Kucher found that 72% of innovations fail to meet their financial targets, argues that founders must have the willingness-to-pay conversation with customers before the product is even built. His core principle: design the product around the price, not the price around the product.

The biggest mistake founders make in pricing research is asking what would you pay for this. Directly asking customers about willingness to pay yields misleading answers because it is context-specific and shaped by how the offer is framed. The Van Westendorp Price Sensitivity Meter is a better approach. It uses four questions: At what price would this be so cheap you would question its quality? At what price would this be a bargain, a great buy for the money? At what price would this start to get expensive, but you would still consider it? At what price would this be too expensive to consider? Plot the cumulative responses and you get an acceptable price range. For B2B products with no competitors, this works particularly well because it surfaces psychological thresholds rather than competitive benchmarks.

Another powerful technique is the ask them as market experts approach. Instead of asking prospects what they would pay, ask them how much another company in their position would pay for this solution. This third-person framing reduces anchoring bias and yields more honest answers. You can also use the cost of the problem approach: ask how much this problem is costing them right now in salary, tools, time, and missed opportunities, then price as a fraction of that cost. If your product saves a VP of Sales 10 hours per week of pipeline review, and that VP costs $300K per year fully loaded, you are saving them roughly $75K per year in time alone. Pricing at $20K per year gives you a 3.75x ROI story.

Conjoint analysis is the most powerful quantitative pricing research method for B2B products, especially those creating new categories. It works by presenting respondents with different product configurations at different prices and asking them to choose, mimicking real purchase decisions. For category creators, conjoint is valuable because it answers which features drive willingness to pay and which are expected table stakes. A practical conjoint study for an early-stage B2B product requires as few as 30-50 respondents to yield directional insights, though 200+ gives statistical significance.

Your first 10 customers are not a revenue source. They are a pricing laboratory. Customers 1-3 are the anchor-setting cohort. Price higher than feels comfortable. Mark Cranney, a16z's former head of go-to-market, has observed that startups routinely leave money on the table: founders say their product saves hundreds of thousands of dollars yet price it as if it only saves thousands. If the first three prospects all say yes without negotiation, you are probably underpriced.

Customers 4-6 are the calibration cohort. Vary your pricing by 20-30% across these customers. Test different metrics such as per seat versus flat rate versus usage-based. Track not just whether they buy, but how long the sales cycle takes and what objections arise.

Customers 7-10 are the signal-reading cohort. By now you should see patterns. A win rate above 80% means you are almost certainly underpriced. A win rate between 30-60% indicates healthy pricing according to Price Intelligently. A win rate below 20% means you are either overpriced or facing a product-market fit issue. Also track deal velocity. If your average sales cycle is 67 days, the B2B SaaS median, and your deals are closing in 20, pricing friction may be too low. If deals are stalling in the negotiation stage, you may have a price-value communication problem rather than a pricing problem.

For category creators, the model decision matters more than the price point. Choose per-seat pricing when your value scales with team size, individual users derive value independently, and CFOs need predictable budgets. But beware: seat-based pricing dropped from 21% to 15% of companies in 12 months as AI made this metric dangerous. If your software uses AI to help one person do the work of ten, and you charge per seat, your revenue shrinks by 90% while the value you deliver increases 10x.

Choose usage-based pricing when your value scales with output, not headcount, your costs are material and scale with usage like AI inference, and customers use the product intermittently. Companies using usage-based pricing see 10% higher NRR, 22% lower churn, and 2x faster growth according to OpenView research. Choose flat-rate pricing when network effects are important and unlimited users drives adoption, you have a narrow product with a single buyer persona, or simplicity is a competitive advantage like Basecamp's famous $99 per month for unlimited users.

Choose hybrid pricing when you want revenue predictability with upside capture, and your product has both a platform component and variable-value features. Companies using hybrid models report the highest median growth rate at 21%, outperforming both pure subscription and pure usage-based models. The 2025-2026 trend is decisively toward hybrid. 92% of AI software companies now use mixed pricing models. The formula that is emerging for early-stage companies: a platform fee at 2x your delivery costs plus included credits for baseline usage, plus overage pricing at declining per-unit cost.

Intercom's Fin solved the seat-based pricing death spiral by abandoning per-seat pricing at $39 per agent for per-resolution pricing at $0.99 per resolved conversation when they launched their AI support agent in 2023. Within six months, they saw 40% higher adoption rates, and one enterprise customer cut support costs by 60% while handling 3x more tickets. Replit moved from subscription to usage-based plans and saw revenue rocket from approximately $2M ARR to $144M ARR in a single year by 2025, while improving gross margins from single digits into the 20-30% range. Cursor IDE transitioned from request-based limits to a compute credit pool system: $20 per month includes $20 of frontier model usage at API pricing, with unlimited access to baseline features.

Mistake one: anchoring to adjacent category prices. When your product is genuinely novel, the temptation is to look at the closest existing category and price relative to it. A new AI sales tool might price against Salesforce seat costs. A new data platform might price against legacy BI tools. This is almost always wrong. Adjacent categories have years of commoditization pressure baked into their prices. By anchoring to their price, you inherit their margin structure without inheriting their scale. Instead, anchor to the value of the outcome your product delivers.

Mistake two: racing to the bottom in a market of one. Low prices create a self-fulfilling prophecy. Research from Clio, the legal practice management platform, illustrates this clearly: when Clio priced low early on, they attracted customers who treated the solution as disposable, leading to high churn, frequent complaints, and constant discount requests. When the founder raised prices by 40%, higher-paying clients churned less because they had invested enough to actually use the product. As a category creator, you have no price competition. Racing to a low price signals either desperation or low value.

Mistake three: not segmenting early enough. Ramanujam's research is clear: companies should build segments based on differences in customers' willingness to pay for a new product. A startup serving both SMBs and enterprises with a single price is leaving money on the table with enterprises and potentially pricing out SMBs. Even with your first 10 customers, you can start identifying segments based on company size and revenue, intensity of the problem you solve, decision-maker seniority, and industry vertical.

Mistake four: treating price as a launch decision, not a living strategy. Patrick Campbell, founder of Price Intelligently which was sold to Paddle for $200M, recommends reviewing pricing every quarter at minimum. His reasoning: your product is theoretically going to be constantly improving, and price is the measure of exchange representing created value. If the value changes but the price does not, you have a growing gap. Y Combinator-backed companies change pricing at least twice in two years. Among mature SaaS companies, 98% have made updates to pricing or packaging since September 2022, with 43.8% updating both pricing and packaging.

Mistake five: overcomplicating the model at launch. Bessemer's playbook advises: resist pricing complexity. Identify one model that scales from 10 to 1,000 customers. Custom deals that spiral into nine different approaches become operational nightmares. Launch with a single paid plan. Add tiers when you have enough customers to segment meaningfully, typically at 50-100 customers, not five. Mistake six: pricing for margin before product-market fit. Early-stage AI companies often see gross margins of 25% or lower. GitHub Copilot lost money on every user for its first year. This is acceptable when you are creating a category and learning what buyers value. Bessemer's advice: deliver quality over margin initially.

A 1% pricing improvement yields an 11-12.7% profit increase according to Price Intelligently. A 5% price increase for Fortune 500 companies produces a 22% operating profit jump according to McKinsey. Usage-based pricing delivers a 10% higher NRR advantage and 22% lower churn according to OpenView. Hybrid model growth rate is 21% median, the highest of all models. Companies abandoning seat-based pricing dropped from 21% to 15% in 12 months.

For pricing review frequency: pre-PMF companies under $1M ARR should review every 6-9 months with each cohort of 5-10 customers. Post-PMF companies between $1M and $10M ARR should conduct quarterly reviews with major changes 1-2 times per year. Growth stage companies above $10M ARR should do quarterly reviews with strategic overhauls annually.

Win rate serves as a pricing diagnostic. Above 80% means almost certainly underpriced. Between 60-80% means likely underpriced and you should test raising 15-20%. Between 30-60% is the healthy pricing range. Between 20-30% may indicate overpricing or value communication gaps. Below 20% suggests overpricing, poor fit, or inadequate positioning.

Sales cycle benchmarks by segment: SMB deals under $10K ACV typically close in 14-57 days with 28-35% win rates. Mid-market deals between $10K and $50K ACV take 77-95 days with 20-28% win rates. Enterprise deals above $50K ACV take 90-180+ days with 15-25% win rates. If your deals are closing significantly faster than these benchmarks with high win rates, price is likely too low. If deals are stalling in negotiation with long cycles, you may have a pricing-positioning misalignment.

In weeks one and two, focus on value discovery. Interview 15-20 target buyers using the Van Westendorp four-question method. Map the cost of the status quo for each buyer segment. Identify 2-3 distinct buyer segments based on willingness to pay.

In weeks three and four, design your model. Choose your charge metric based on where value accrues, whether that is seats, usage, or outcomes. Design a single paid tier and resist the urge for good/better/best at launch. Set your price at the upper end of the Van Westendorp acceptable range. Build in metering infrastructure from day one, even if you are not charging on usage yet.

In months two and three, begin live testing. Price your first 3 customers 20% above where you think the ceiling is. Track win rate, deal velocity, and objection patterns. If all 3 close easily, raise the next 3 by another 20%.

In months four through six, calibrate and segment. With 10+ paying customers, analyze willingness to pay by segment. Introduce a second tier if segment differences are greater than 2x in willingness to pay. Establish your pricing review cadence at quarterly minimum. Every quarter, check win rates, deal velocity, and NRR against benchmarks. Survey new customers on price perception at onboarding. Adjust pricing for new customers and grandfather existing ones.

When you are creating a new category, pricing is not a math problem. It is a market-creation problem. The founders who get this right, from Gong's Amit Bendov refusing to discount, to Snowflake's Mike Scarpelli reforecasting revenue daily, to Figma's Dylan Field linking pricing architecture to product mission, treat pricing as a strategic weapon, not an administrative task. You do not need competitors to set a price. You need three things: a deep understanding of the pain you eliminate, honest conversations with buyers about what that elimination is worth, and the courage to charge for it. Price higher than feels comfortable. Review more often than feels necessary. And remember: you are not setting a price for a product. You are setting the price for a category.