Pricing Guide for Early-Stage Startups
Synthesizing Insights From the Data
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The Pricing Dilemma
Tech startups in their pre-product-market fit (PMF) phase often experiment with pricing to find a sustainable business model. In the US and Europe, early-stage SaaS and data-driven companies have tried a range of pricing strategies from giving the product away for free to charging premium prices upfront.
This episode examines common pricing models used before achieving PMF, the strategic risks and rewards of setting prices early, conversion data from early monetization experiments, and case studies illustrating key approaches. The goal is to provide a structured look at how young startups price their products when the primary objective is learning and user adoption rather than immediate profit.
Let’s jump in👇
Common Pre-PMF Pricing Models and Strategies
Before achieving PMF, startups favor simple or experimental pricing schemes that maximize learning or user adoption. Here’s a brief summary of the 8 most common approaches.
#1 Freemium Model
Offering a basic version that is free forever, with paid tiers for advanced features or higher usage. This is popular for startups focused on rapid user acquisition. For example, Dropbox and Evernote gained traction by letting users use core features at no cost, then converting a small percentage to paid plans (Userpilot, 2024).
Pros: Eliminates barriers to trial, fuels viral growth, and creates feedback loops.
Cons: Many free users never convert, and it can “devalue” the product’s core functionality if not balanced (Userpilot, 2024). (Slack is a standout case – its generous free tier drove massive adoption and an exceptional ~30% conversion of free teams to paid plans (Wolf, 2025)).
#2 Free Trials (Time-Limited)
Allowing full product access for a limited period (e.g., 14 or 30 days) before requiring payment. This strategy is common in B2B SaaS – e.g., Salesforce’s early free trials let teams experience the full CRM before buying (Chudoba, 2017).
Pros: Customers can evaluate value risk-free, yielding high-intent leads.
Cons: Puts a ticking clock on demonstrating value, and many users may drop off if the trial is too short or unstructured. A critical decision is whether to require a credit card upfront: opt-in trials (no card needed) get many signups but lower trial-to-paid conversion (~18% on average), whereas opt-out trials (card required) have far fewer takers but a much higher conversion (often ~50%) (Litt, 2024).
Conversion rate benchmarks, according to Litt (2024)
#3 Usage-Based or “Pay as You Go” Pricing
Charging based on consumption (API calls, data volume, etc.). Startups often choose this to align price with value delivered. For instance, cloud platforms like AWS and Snowflake from their early days charged per usage unit.
Pros: Low friction entry (pay for what you use), scalable revenue as usage grows (Olivier, 2024).
Cons: Can confuse customers if usage metrics are unclear; unpredictable revenue for the startup until usage stabilizes.
#4 Pilot and Beta Pricing
Early B2B startups frequently run pilot programs with a few “design partner” customers. These pilots might be free or discounted in exchange for feedback, or paid pilots with nominal fees. The idea is to validate the product in a real environment.
Pros: Builds strong case studies and reference customers; pilots let you refine the solution with minimal commitment from clients.
Cons: If free, there’s a risk of “pilot purgatory”; endless trials with no conversion. Experienced founders warn that unpaid pilots are a waste of time 95% of the time, due to a lack of buy-in (Lemkin, 2024). To combat this, many startups charge a small pilot fee (often refundable or creditable toward a contract) to ensure commitment (Shah, 2024). Well-structured paid pilots can convert 60–90% of the time into full-paying contracts (Lemkin, 2024), whereas a free pilot with heavy deployment effort can result in nearly 0% conversion if the customer isn’t truly motivated (Lemkin, 2024).
#5 Founder-Led Sales & High-Touch Onboarding
In early stages, pricing is often bespoke. Founders might personally negotiate each deal, offering custom pricing or heavy discounts to land the first users. This often goes hand-in-hand with high-touch onboarding or consulting. For example, an enterprise SaaS founder might onboard the first Fortune 500 client with on-site support and a steep “friends and family” discount.
Pros: Maximizes learning and ensures early customers are successful (increasing the chance of testimonials and retention).
Cons: Doesn’t scale, and pricing lacks consistency. Each customer may be paying a different amount, and it’s hard to know your true pricing power when every deal is unique.
#6 “Contact Us” / Request-a-Quote Pricing
Many B2B startups hide their pricing on the website in favor of a request a quote approach. This is common when the solution is custom-tailored or the startup is still figuring out willingness-to-pay.
Pros: Gives flexibility to price on a per-customer basis and adjust as the product evolves. It avoids anchoring the market to a possibly wrong price early on.
Cons: Adds friction for customers who prefer transparency, and can reduce inbound signups (some prospects won’t bother calling). Startups often use this when targeting enterprise clients who expect to talk to sales anyway.
#7 Flat or Simple Tier Pricing
To avoid over-complicating things pre-PMF, startups often start with a very simple pricing structure like a single flat monthly rate or a basic per-seat charge. Many seed-stage SaaS companies “rarely place much emphasis” on detailed pricing models at first (Hickie, 2022). A one-dimensional model like “$X per user per month” is extremely common initially (Hickie, 2022).
Pros: Clarity and ease of understanding – it removes any impediment to speedy customer adoption (Hickie, 2022). It’s also easy to change later.
Cons: A simplistic model might undercharge power users or overcharge small users. It can also attract non-ideal customers. A rock-bottom per-seat price might draw bargain-hunters who churn quickly or aren’t the target segment (Hickie, 2022).
#8 Outcome-Based Pricing (as Emerging in AI Startups)
AI-native startups are increasingly moving toward outcome-based pricing models that align charges directly with customer results (per-conversation, per-resolution, or percentage-of-uplift models), rather than traditional per-seat or usage-based metrics. Examples include AI customer service platforms charging per resolved ticket or conversation (rather than per agent seat), or AI-driven sales tools charging a percentage of revenue uplift.
Pros: Strong alignment between vendor and customer value, increasing buyer confidence. It helps remove upfront risk for the customer, making sales cycles faster. Ultimately, the hope is that it can unlock premium pricing when tied to high-stakes outcomes like cost savings or revenue growth (a16z, 2024).
Cons: Requires robust tracking and clear agreement on what counts as a delivered outcome. Additionally, revenue predictability becomes difficult for the startup, especially at scale (a16z, 2024).
✈️ KEY INSIGHTS
Before product-market fit, startups test pricing models like freemium, free trials, usage-based, or pilot programs to drive adoption and learn fast. Each has pros and cons, but all aim to validate value and pricing before scaling.
Anchoring Pricing Before Validation: Strategic Implications
Setting a price early on (“anchoring” your product’s value) is a strategic decision that carries both risks and potential rewards. Founders must balance monetization goals with the need to learn and iterate.
Premature Monetization vs. User Adoption
Charging too early can create a barrier that slows down user growth. The first danger of charging pre-PMF is obvious: It “limits your user pool,” meaning far fewer people will even try the product (Sequoia Capital, 2024). This in turn limits feedback and learning; with a smaller beta user base, finding product-market fit can take longer (Sequoia Capital, 2024).
Thus, many successful startups follow this approach: They focus on engagement and retention metrics first, then layer on pricing once value is proven. The flip side is that not charging at all carries its own risks: You may attract a flood of free users who will never pay, creating noise in your feedback. Furthermore, serious business customers might avoid a product that’s completely free, either questioning its maturity or assuming it won’t be around long-term (First Round Capital, 2024).
In short, premature monetization can stunt early adoption, but waiting too long can yield false signals. Many experts recommend a middle ground – offer a free tier or trial to drive adoption, but start testing willingness to pay on your most engaged users. Even a nominal price in pilot programs can ensure you’re building for a truly paying problem (First Round Capital, 2024).
Brand Positioning and Price Perception
Your early pricing sends a strong signal about your brand’s positioning in the market. A low introductory price (or free) can position the startup as accessible and gain broad traction, but it might also peg the product as a low-end solution.
Conversely, a high price point can cultivate an aura of premium value, albeit at the cost of a smaller initial user base. For instance, the email startup Superhuman deliberately launched with a single plan at $30/month, far above typical email apps (Litterst, 2025). This premium pricing was a conscious strategy: They identified $30 as a “premium position” through competitor and persona analysis, finding that target users perceived $30 as a fair high-end price for the value (Litterst, 2025).
In Superhuman’s case, anchoring high reinforced its brand as an exclusive, high-quality product, and attracted users willing to invest time to fully onboard. The risk on the other side: Price too high before you’ve built enough value, and you may scare away would-be early adopters or come off as arrogant. Early-stage founders often opt to “price for volume” (as Atlassian did) – keeping prices low enough that the product “gets out of the customer’s way” and into as many hands as possible (Atlassian, 2025). This builds a large customer base that can be monetized more deeply later (land-and-expand).
Anchoring Effect and Future Pricing Flexibility
Once you set a price (even a placeholder), it becomes an anchor in the minds of customers and your team. This can constrain future pricing moves. Early adopters often expect to be “grandfathered” at initial prices, so radical changes can create legacy tiers that complicate your pricing structure long-term.
That said, anchoring doesn’t always mean you can’t change! Many companies do periodic price resets post-PMF. In one study of tech firms that delayed pricing changes, a SaaS company kept its early price static for ~6 years through growth, then executed a one-time 30% price increase on its main tier once it had scale (Bessemer Venture Partners, 2020).
Such adjustments are easier once you’ve proven value, but the earlier the anchor, the more customers you’ll eventually have to migrate to new pricing. Founders should thus treat early pricing as an experiment (communicate it as a beta or limited-time pricing if possible) to give themselves leeway to adjust.
Also consider what you anchor on: Not just price level, but pricing model (freemium vs paid, per-user vs usage). Changing the fundamental model later is difficult because it impacts product design, billing, and customer expectations (Bessemer Venture Partners, 2020).
Revenue Early vs. Later (Runway and Investor Signaling)
On the reward side, successfully charging even a modest price pre-PMF can yield valuable proof points. It answers the critical question: “Will anyone actually pay for this?”. That’s something that pure usage metrics can’t fully answer (Bessemer Venture Partners, 2020).
Early revenue, even if small, can extend runway and demonstrate traction to investors in enterprise-focused startups. It also forces the team to focus on delivering enough value that users want to pay, which can accelerate learning about which features matter most.
However, founders should be cautious not to become prematurely revenue-driven at the expense of usage growth. It’s a strategic trade-off: Growth-first startups treat revenue as a later optimization, whereas enterprise/B2B startups often prove the model with revenue from the get-go (since in B2B, if no one will pay, you likely don’t have a viable product). The narrative of “grow now, monetize later” has become more difficult in the past few years, however, as we have written about many times.
✈️ KEY INSIGHTS
Anchoring pricing early shapes how customers perceive your product but carries trade-offs: Charge too soon and you limit growth, wait too long and you risk weak signals or attracting only free users. Successful startups balance by experimenting with free tiers or trials while testing willingness to pay, knowing early pricing sets expectations that can be hard to shift later.
Conversion Rates from Pricing Experiments
Empirical data on conversion rates can shed light on how effective different early-stage pricing strategies are. While every product differs, industry benchmarks provide a reference for what startups can expect when offering free or trial options:
Freemium Free-to-Paid Conversion
In SaaS, typical freemium models convert about 1% to 10% of free users to paying, with 2–5% being the range most companies fall into (Korczynska, 2025). In other words, for every 100 users on the free plan, only a handful will upgrade. Ergo, the free user base must be large for this model to work. Many developer tools, productivity apps, and B2C services see conversion around 3% (Korczynska, 2025).
Some exceptional cases achieve much higher rates: Slack and Spotify convert ~30% of free users, thanks to intense engagement and smart limits that push power users to pay (Korczynska, 2025). In B2B freemium (targeting small teams), conversion tends to be on the higher end (6–10%) because the free tier often serves as a funnel for teams who have budget if they see value (Korczynska, 2025).
The activation and retention of free users is crucial. Those who don’t find value will never convert. Techniques like usage limits and feature paywalls are used to achieve that ~2-5% benchmark without giving too much away for free (Korczynska, 2025).
Free Trial Conversion
For time-limited trials (often 14 or 30 days of full access), conversion rates are significantly higher than freemium, since trial users have declared some interest. Across SaaS businesses, 15% trial-to-paid conversion is considered a decent benchmark, around 25% is the B2B average, and 30%+ is excellent (Korczynska, 2025).
These percentages usually measure the share of trial users who end up becoming paying customers. Notably, requiring a credit card for the trial dramatically filters who signs up but boosts the eventual conversion: industry data shows ~49–50% of users in credit-card (“opt-out”) trials convert to paid, compared to ~18% in no-credit-card (“opt-in”) trials (TrialPro, 2025). In other words, almost half of trial users will stay and be charged if they had to input a card (many simply continue unless they opt out), whereas when trials don’t require a card, many more people try the product but only ~1 in 5 actually purchases after the trial expires.
Startups must weigh this trade-off: An opt-in trial maximizes the top of funnel (more signups, more data, good for PMF learning), whereas an opt-out trial maximizes immediate conversion (good for revenue and showing traction, but fewer users will experience the product). By industry, trials convert at different rates. For example, a recent analysis found RegTech had the highest trial conversion (5.8% of site visitors went on to become paid), whereas EdTech was among the lowest (2.6% of visitors to paid) (TrialPro, 2025).
Pilot-to-Paid Conversion
In B2B contexts where a pilot project is used instead of a self-serve trial, the conversion rates can be high if the pilot is paid and closely managed. As noted, good teams aim for 70%+ conversion of pilots into full contracts (Lemkin, 2024). Anecdotally, enterprise startups often report 60–90% of customers who engage in a structured, paid pilot will end up signing a longer-term deal (Lemkin, 2024).
Conversely, a free pilot (especially one requiring a lot of integration work) might convert at a very low rate – often the effort isn’t justified unless the customer has skin in the game (Lemkin, 2024). No surprise, then, that many enterprise startups insist on a nominal fee for pilots or an “opt-out” clause on a full contract (where the default is the customer signs on, with an option to cancel after 60 days if unhappy). The latter effectively treats the pilot as the start of an annual deal with a get-out clause, and tends to yield lower churn than true opt-in pilots (Lemkin, 2024).
✈️ KEY INSIGHTS
Early pricing experiments show freemium models typically convert just 2–5% of free users, while time-limited free trials average 15–25% conversion, rising to ~50% if a credit card is required. Paid B2B pilots perform best, often converting 60–90% into long-term deals, highlighting the importance of user commitment in boosting conversion.
Case Studies Illustrating Pricing Approaches
To bring these concepts to life, here are a few brief case studies of companies (US and Europe) and their early-stage pricing strategies:
Slack (Freemium & Product-Led Growth)
Slack launched in 2013 with a freemium model, offering teams a free tier with limited message history. This low-friction entry strategy fueled rapid adoption across companies. By the time Slack started charging, teams were deeply engaged.
Slack’s focus was on making the free version so valuable that teams felt almost compelled to upgrade once they hit the limits. The approach worked extraordinarily well: Slack boasts a 30%+ conversion rate of free workspaces into paid subscriptions, far above industry averages.
Key to this success was a generous free offering (enough to hook users) combined with usage triggers that naturally led growing teams to pay (e.g. needing full search and archive of messages) (Korczynska, 2025).
Atlassian (Low-Cost, Viral Pricing in Enterprise)
Atlassian, an Australian enterprise software company (maker of Jira and Confluence), is an example from the early 2000s of a company that shunned a sales-driven approach in favor of low pricing and volume. Because they had little access to venture capital in 2002, Atlassian couldn’t afford a sales team and instead made their software self-serve and affordable. Jira was free to try and extremely easy to buy (pricing fully transparent online, self-service checkout) (Atlassian, 2025).
They set Jira’s price so low that any team’s manager could approve it without lengthy procurement (even a credit card expense). This was encapsulated in their principle “make it easy to afford” (Atlassian, 2025). Early on, they famously had a “$10 for 10 users” pricing for small teams, which was essentially a token price to get teams started. This unconventional strategy in enterprise software paid off: Atlassian gained thousands of customers with almost no salespeople, using pricing as a weapon to remove friction.
Over time, they expanded pricing as customers grew (land-and-expand), but those early low-price users became the seed for Atlassian’s massive enterprise penetration. The Atlassian case underscores how pricing can be used to build a bottom-up adoption flywheel in B2B:
By pricing for volume (even at the expense of short-term revenue), they turned their user base into a growth engine (Atlassian, 2025). It also shows a pivot in pricing model – years later, Atlassian made the first 10 users completely free (instead of $10) once they saw the benefit of even more frictionless signups (Atlassian, 2025). They were willing to change their monetization level as data indicated it would boost overall growth.
Zendesk’s Outcome-Based Pricing for AI Agents
In September 2024, Zendesk introduced an outcome-based pricing model for its AI-powered customer service agents. Under this model, clients are billed solely for successful resolutions achieved autonomously by AI agents, rather than traditional metrics like per-seat or per-interaction fees. This approach directly ties costs to the value delivered, aligning vendor revenue with customer success. The model includes transparent tracking of AI-driven resolutions and offers scalability as businesses increasingly integrate AI into their operations. Zendesk’s shift reflects a broader industry trend toward value-based pricing in AI services (Zendesk, 2024).
✈️ KEY INSIGHTS
Slack used a freemium model to drive massive adoption, converting over 30% of free teams to paid by offering real value before monetizing. Atlassian relied on ultra-low, self-serve pricing to build viral B2B adoption, later expanding pricing as customers grew. Zendesk recently embraced outcome-based pricing, charging only for AI-driven resolutions to align revenue directly with delivered value.
Conclusion
Pricing in the pre-PMF stage is as much art as science. Many startups default to guessing their pricing! 67% of founders admit their early pricing is essentially a guess (SBI, 2024). This is understandable given limited data, but as we’ve seen, those early pricing choices have far-reaching effects on user acquisition, brand perception, and the company’s ability to iterate or pivot.
The best approach for a young SaaS or data product is to align pricing strategy with learning goals. If you need widespread usage and feedback, err on the side of free (with a plan for how to convert or upsell later). If you need deep validation in a complex B2B environment, use pilots or founder-driven sales to get a few paying reference customers even at a low price.
Always gather data: run small pricing experiments (e.g. A/B test a $0 vs $10 starter plan if you can, or test different trial lengths) and talk to users about their willingness-to-pay. Remember that pricing is not “set and forget”! It should evolve with your product. Many companies re-price once they have product-market fit and a better sense of value metrics.
In summary, early-stage pricing is a strategic lever to be pulled carefully: it can accelerate adoption and signal value, but it must remain flexible. The startups that ultimately win are those that either didn’t let price get in the way of finding passionate users, or those that managed to get paid while still learning from every customer.
By understanding common models, being aware of the pitfalls of anchoring too early, and watching the data on what converts, founders can navigate the pre-PMF phase with a pricing approach that supports the quest for product-market fit.
Thanks to Jérôme Jaggi for his help with this post.
Stay driven,
Andre
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