In my view, some of the most exciting AI products recently haven’t gotten their monetisation right. Its early days and the trade-offs are complex. Take Manus AI’s for instance. It’s free product caught significant attention, overloading servers. Trying it uninterrupted wasn’t possible. Good news? I don’t think so.

Within a week or so of launching, Manus AI paused all their free invites entirely. They released paid tiers in a few weeks, with very limited free tier offerings. It broke momentum. Competitors have pledged to launch rival offerings soon. They’ll probably never get back that kind of momentum. Yet, it would have been very expense to sustain.

One of the few critical choices today in building and monetising an AI product is: should we offer a free tier (freemium) or not. Allow for a limited time/usage free trial instead? This isn’t an easy decision. So I’ve created one and will keep evolving it.

Free Tier (Freemium) vs. Free Trial: Key Differences

Let’s start by defining what each stands for.

Freemium means offering a limited-access version of the product for free, indefinitely. Users can sign up with no time pressure and use basic features or limited capacity forever, which lowers friction and encourages broad adoption. The goal is to hook users with core value and later entice a subset to upgrade for more features or capacity. In contrast, a free trial is all-access but time-limited. There’s full (or near-full) access to the product’s premium features for a short time (e.g. 7, 14, or 30 days). After the trial period, the user must pay to continue using the product at all. This creates urgency. We, as users, experience the product’s full value, then face a deadline that pushes them to convert if they want to keep that value.

Typically, B2C and prosumer apps favour freemium. It removes sign-up friction (no credit card up front). This means, as a user, I can reach a “wow moment” at my own pace. In contrast, B2B or high-value tools commonly use free trials. Yet, like we saw with Manus AI, this isn’t the general rule. First, lets analyse it across two axes – business trade-offs and user experience. Then we’ll consolidate this into a simple decision framework.

Business Trade-offs

I see seven business trade-offs to weight to make a good choice on free tier vs. free trial:

1. Customer Acquisition Cost (CAC):

Freemium is a powerful customer acquisition strategy. OpenAI’s ChatGPT free plan exemplifies this, reaching ~100 million users in two months^[3]. Widespread adoption was a clear priority over immediate revenue. This massive user base gave OpenAI a dominant mindshare and training feedback loop. Only later did they introduce paid plans (ChatGPT Plus) for power users. The free tier is clearly about maximizing reach. By eliminating price barriers, freemium can dramatically increase sign-ups – potentially reducing CAC by up to 50% according to research^[2]. A wide free user base also builds brand awareness and provides valuable user feedback for early-stage products.

In contrast, free trials, while still lowering CAC compared to “pay upfront” models have a different objective. The time limit deters casual explorers. Opt-out free trials, where users provide payment information upfront and must cancel to avoid charges, have demonstrated notably high conversion rates^[6]. . The broad, skeptical audience needed free access to even consider using the product. Jasper’s is positioned against this. Their bet is that businesses with serious content needs will trial the full product for a week (7 days) and then pay $49/month, rather than catering to millions of casual users.

2. Conversion vs Funnel Volume

Freemium typically converts a small percentage of users to paid. Even wildly popular AI apps reflect this: OpenAI’s ChatGPT, with an indefinite free tier, sees about a 5% conversion of users to its paid $20/month ChatGPT Plus in the U.S^[2]. Duolingo operates between 6-7% free to paid conversion. Most products range much lower. By contrast, in time-limited free trials averages of 10–25% conversion are common^[1].

Take Cursor, the AI code-editor as an example of the opposite. It starts off with a 14-day free trial of its Pro plan, allowing users to explore advanced features. During the trial, users gain access to: Unlimited completions: Generate as much code as needed with smaller models or these with slower response time along with 500 premium requests per month (access to premium models). This trial is ideal for developers who want to evaluate Cursor’s full potential before committing to a paid plan. The do have a “hobby” free tier, but that’s not targeted at developers at all (more on this later).

In short, freemium favours maximising top-of-funnel reach (and thus can lower CAC per user at scale), whereas trials focus on efficiently converting higher-intent users (potentially lowering CAC per paying customer).

Here is what we must decide as founders- do we prefer a huge user base with low yield (freemium) or a smaller, highly engaged user base with high conversion (free trial). To help make that decision, we needed the next trade-off axis.

3. Cost-to-Serve

A crucial factor for AI products is cost of serving free users. Traditional software has near-zero marginal cost per user, making generous free tiers feasible. AI changes this: every prompt or API call might incur non-trivial compute cost^[2]. If the product has high cost-of-goods (COGS) per user (e.g. expensive GPU inference, API calls to large models, or substantial support needs), a freemium model can quickly become financially unsustainable^[4]. For instance, Midjourney, a generative image tool, initially let anyone create 25 images free. But as millions of users flooded in, the GPU costs and server load skyrocketed. In April 2023 Midjourney’s CEO halted the free trial program. For Manus AI, a general purpose assistant, this transition happened within a few days. This illustrates why many generative AI startups lean towards time-bound trials or very limited free quotas. In principle, for data- and compute-intensive software (like the new wave of generative AI apps) favour free trials often win over freemium for this reason.

Bottom line: evaluate your margin structure. High COGS per user tilts the decision toward free trials (or very limited free tiers), whereas low-cost-per-user products can more freely use freemium to boost growth.

4. Lifetime Value (LTV)

Freemium and free trial models can lead to different LTV dynamics. With freemium, because only a small fraction convert, you rely on that minority of power-users or enterprise upgrades to generate enough lifetime value to cover the cost of all the free users. If those who convert tend to be lower-spending or churn quickly, the model will fail. Free trials usually attract users with a clear intent to find value, so if they convert, they’re more likely to stick around and pay over time (higher LTV per paying customer). However, one could argue that freemium users who eventually convert might be very loyal (since they’ve used the product extensively in free mode before committing). However, this arguement is not true in practice today – given the sheer pace of product improvements that’s keeping customer’s in constant evaluation mode. This debate on Cursor vs Windsurf is an excellent example of it.

In essence, these are key questions to evaluate LTV trade-offs:

• How much more will a converted user pay over time? How to ensure stickiness?
• Can freemium users be upsold to higher tiers later (increasing LTV), or will they stick to the lowest paid tier?
• Does the product naturally has expansion revenue (seats, usage)? If so a freemium user who eventually converts an entire team can yield huge LTV – making the freemium investment worthwhile. If not, a faster trial conversion might be preferable to start revenue flow sooner.

5. Competition

In AI today, highly funded competitors can skew our analysis entirely. Instead, let’s first consider a more traditional example. Slack’s freemium plan proved “sticky,” keeping teams using it for free for years until they outgrew the limitations. This gives Slack a large future upsell pool. However, these free users contribute $0 until they convert. Cost to serve is relatively low, so this is not a significant drain on company resources. In sharp contrast to this was Atlassian’s Stride. Stride offered only a 30-day free trial and cheap pricing, aiming for quick paid conversions. Slack, even with a costlier paid plan but a generous free tier, amassed far more users. In the end, Stride shut down, as customers overwhelmingly chose Slack’s free, unlimited-use offering over a time-limited trial of a slightly cheaper product^[4].

In AI, Google Cloud AutoML is one such example. Targeted at developers and enterprises with advanced machine learning tools, AutoML offered limited-time free trials, with ~$300 in credit. Users had to commit financially to continue using the platform. While this attracted enterprise users, it failed to capture broader market share among developers and smaller companies who preferred free-tier alternatives like AWS SageMaker (~250 hour per month of free tier for model training and hosting) or open-source options like Hugging face. The lack of an accessible freemium model may have hindered its growth.

Thus, consider your competitive context: In short, freemium prioritizes user base size, while trials prioritize near-term revenue (with a smaller, but paying, user base). However, freemium is almost necessary if others in your space are freemium; if nobody else is, you have the choice to be the accessible free option (to undercut them) or to position more exclusively with trials.

6. Total Addressable Market (TAM)

Freemium generally requires a large TAM to work well. Since only a small percentage convert, you need a big pool of users to get enough revenue. If your product could potentially serve millions (e.g. a horizontal productivity AI tool or developer API), freemium can help capture a big slice of that market. But if your market is finite or niche. In a small market, you can’t afford a scenario where 95% of users stay free forever – you’d run out of prospects to convert. A free trial is often more sustainable.

Canva exemplifies this approach:

• It targets a broad audience, including individuals, small businesses, educators, students, and large enterprises.
• It’s generous free plan offers access to thousands of templates, basic design tools, and limited stock images. These features are sufficient for casual users and beginners, making Canva accessible without upfront costs.
• Canva Pro ($12.99/month) and Canva for Teams ($29.99/month for five users) unlock advanced features like AI-powered design tools, premium stock libraries, brand kits, and collaboration tools.
• By showcasing premium features alongside free ones (e.g., background remover or Magic Resize), Canva entices free users to upgrade by highlighting what they are missing.

In short: a mass-market AI app (like Canva) can leverage freemium to become a category leader, whereas a specialized AI B2B tool (e.g. AI for biotech research) might use time-limited trials or demos to focus on converting a high percentage of a small audience.

7. Price Point & Sales Model:

Freemium tends to align with lower-price, self-serve products – typically in the tens of dollars per month range (ChatGTP’s pro at subscription at $200/ month is at the higher end of the range). Copy.ai ($49/month) targets solopreneurs and marketers with a free tier with upgrade paths for more. At that price, individual users or team leads can swipe a credit card to upgrade if they like the free version. Higher-priced software or services, like ScaleAI, Gong (priced at hundreds or thousands per month/user) usually involves a sales cycle or at least careful consideration, which maps better to a structured trial or proof-of-concept.

In AI, we see this divide: a $20/mo AI writing tool might be freemium or low-friction trial, but a $1000/mo AI analytics platform will likely only do trials or custom demos rather than an open free tier. The customer’s expected buying journey (self-service vs. high-touch) should align with the free trial/tier strategy.

Product and UX Factors

Beyond pure economics, I see four product and user experience factors determine which model is appropriate:

1. Time to Value:

Consider how quickly users realize the core value or “aha!” moment of your AI product. If time-to-value is short – the product delivers a clear benefit in the first session or day – then a free trial can work well. Users will experience the full value quickly within the trial window, which builds desire to continue. For example, an AI writing assistant or image generator can wow a user with results in minutes. A 7-day or 14-day trial of full features might be sufficient for them to decide to pay. In contrast, if time-to-value is long or usage needs to build over time, a brief trial may end before the user gets hooked. A free tier is better in those cases, allowing users to use the product gradually until they hit a natural upgrade trigger. Collaboration and habit-forming tools often fall in this category. Slack’s value grows as a team archives more messages; a 30-day trial might not be long enough for a small team to fully adopt it, whereas a free tier lets them use Slack until they need premium features (searchable history, etc.).

Use freemium if users need more time to reach value, and use a time-bound trial if value is evident quickly (or can be made evident through a guided experience).

2. Product & Onboarding Complexity

Simple, self-explanatory products lend themselves to freemium. Users can immediately understand and derive value from a stripped-down free version. For example, Calendly (while not AI, a simple scheduling tool) grew via freemium – the basic scheduling link feature is free and easy to use, which spread it widely. If an AI product similarly has a straightforward core use-case (say an AI that removes photo background with one click), a free tier can attract huge numbers of users quickly, and a percentage will pay for advanced features (higher resolution, bulk processing, etc.). Conversely, complex products with rich feature sets may need a trial to let users explore advanced functionality. Users might not see the value if they’re stuck on a limited free plan that doesn’t showcase key features. This is why some enterprise SaaS do “gated freemium” – e.g. offering basic functionality free but requiring a trial or demo for the full product. AI startup founders should ask: “Can a user get the gist of our value with a limited free version, or do they really need the full product features (even briefly) to be sold on it?”

Within this, Onboarding complexity is another factor. Products that require significant onboarding, setup, or learning curve often benefit from a free tier or at least a longer trial. If your AI tool is plug-and-play (e.g. a simple AI image app or a browser plugin) then users can start getting value immediately, and a short trial is fine. But if it’s a complex B2B integration or developer tool that takes time to implement, a short trial might not be sufficient for a fair evaluation. One strategy is to offer a free trial with strong onboarding support to ensure users see success quickly. For instance, Zendesk (a customer support SaaS) uses a 30-day free trial but pairs it with in-app guidance and sales assistance to help companies get set up within that window^[4]. This creates urgency but also helps the user overcome complexity in time. Alternatively, a free tier might lower the pressure on onboarding – users can tinker and integrate at their own pace. Many developer-focused AI platforms provide free API credits or a limited free tier precisely so developers can take their time prototyping. For example, In it’s early days OpenAI’s API offered new users $5 in free credits (even $18 at one point) to experiment, rather than a strict n-day limit. It allowed developers to run a few tests at their own pace. They’d tinkered enough to come back later to build full applications with paid upgrades.

Summary: If the value lies in advanced, nuanced features, or if onboarding is lengthy or requires data/setup, lean towards a free tier or a generous trial with user assistance to hit milestones before the trial ends. If the core value is accessible in a lightweight form, freemium can work by teasing users with more if they upgrade. If your product onboarding is quick and self-service, a trial can suffice. If onboarding is lengthy or requires data/setup, lean towards a free tier or a generous trial with user assistance to hit milestones before the trial ends.

3. User Intent:

The intended audience’s mindset is critical. Ask: are users actively seeking a solution with intent to buy, or are they casually exploring a new technology? If it’s the former (common in B2B scenarios or mission-critical tools), a free trial works well – serious buyers are willing to test and then pay if it meets their needs. For example, Autodesk targets professional designers who expect to pay for high-end software; offering a 15-day trial of Autodesk’s AI-powered design tools is enough for these users to evaluate it in a real project and then purchase the expensive subscription. Autodesk explicitly avoided freemium in favor of a trial because their niche professional users were few but willing to pay a high price once convinced. On the other hand, if users are in discovery mode or skeptical about AI, freemium lowers the barrier so they can play around without commitment. This has been crucial for many creative AI tools appealing to consumers/hobbyists. For instance, ChatGPT attracted over 100 million users in part by offering an unlimited free tier for casual use – many people were just curious about AI and wouldn’t have paid upfront. Once hooked by the free tier’s utility, a subset decided to upgrade to the paid version for more power. The is one of the reasons why Jasper AI, a marketing AI content platform, which only has paid subscriptions lost significant momentum with launch of ChatGPT and other free AI products^[5]. The broad, skeptical audience needed free access to even consider using the product.

Use freemium when you need to win over a cautious or mass-market audience who may not have immediate intent to buy. Use trials when targeting evaluators or business users who have a defined problem and budget, and just need short-term access to confirm your solution works.

4. Buyer-User Relationship

Freemium succeeds best when the free user directly controls or strongly influences purchasing.

High Fit (User = Buyer): Consumer and prosumer AI tools (e.g., ChatGPT, Claude, Cursor, Perplexity, Descript, ElevenLabs) align closely because users can instantly upgrade without approvals. Indie devs, SMB founders, or creative teams experience value firsthand, making free-to-paid conversions straightforward.

Low Fit (User ≠ Buyer): In enterprise contexts, users often aren’t budget holders, complicating monetization. For instance, analysts using Claude or designers using Runway ML may love the product but must convince executives or procurement to buy. Freemium generates internal advocates here, but converting usage into sales typically requires Product-Led Sales strategies or direct marketing.

AI tools initially focus on audiences where user and buyer overlap for simpler growth. However, as they scale to enterprises (e.g., ChatGPT Enterprise, Synthesia), bridging this gap becomes crucial. Thus far, successful AI startups have deliberately minimized the user-buyer disconnect to optimize early conversions.

In short, ask if the end users also the decision makers who can pay? Then go freemium. Else a free trial or a PoC maybe better.

Framework: Six questions for Choosing the Right Model

Considering the above factors, here is a decision framework to guide founders or product leaders in choosing between a free tier and a free trial for an AI product:

1. How large is the market? Does it have viral potential?
   If both are high, then a freemium model is a clear winner. In niche markets or high-value users free trials will filter for serious users much faster.
   
2. What is your cost-to-serve for free users?
   If the marginal cost is low or zero (or your funded like OpenAI), freemium is best, else consider free trials.
   
3. Is the product simple & quick to show value?
   If user “get it” fast and can reach an “aha!” moment using just free features then freemium. If advanced features are necessary or users need to tinker at their own pace, then free trial is better. Complex integration or long sales cycles make freemium almost impossible.
   
4. What is your immediate priority revenue or rapid growth/network effects?
   Free trials are better at capturing revenue, freemium is necessary for rapidly capturing the market
   
5. What user expectations has competition set?
   If your competitor is offers a free version, you may need to match or exceed that to stay in the game
   
6. What is the buyer user dynamics?
   Freemium is best when User = Buyer or a key influencer. The father away the buyer is from the product experience, its harder to convert and needs a carefully designed proof-of-concept trials that engages the buyer.

Conclusion

To conclude, lets try and apply this framework to Manus AI’s dilemma:
(rating on a scale of 1 – 5, 5 being most suitable for freemium)

1. Market size and virality – 5/5. It’s General purpose tool, with high viral potential
2. Cost-to-serve free users – 1/5. Marginal computation cost today is clearly very high
3. Simple product, quick time to value – 5/5. Aha moment is within a few hours
4. Growth priority – 5/5 – Lots of competition will come in soon, so critical to gain momentum
5. Competition freemium – 4/5 – Most well funded competitors (like Claude, ChagGTP, Perplexity) have free tiers, with paid upgrades.
6. Buyer is the user – 5/5.

For Manus, the only barrier appears to be cost-to-serve. The classical dilemma in consumer AI today. Let’s assume they are capital constrained (compared to other competitors above), so they really can’t afford it. Instead of switching completely to a free-trial, I’d rather explore a hybrid approach. Let’s call it a “reverse trial”. The objective is to Upsell. Stated simply, users start on a full-feature trial and then drop to a limited feature free tier if they don’t convert. It’s a more nuanced funnel (trial -> free -> convert later), but has the potential to keep the large user base active. Another option is to build a desktop version and leverage local compute as much as possible, which reduces the cost-to-serve (like bolt.new). Github did the opposite. GitHub Copilot, an AI pair programmer for developers, initially offered a 60-day free trial upon launch and then required a subscription. This made sense given the clear value devs could see in a few weeks of coding with Copilot. Over time, as competition increased (e.g. Amazon CodeWhisperer, Replit’s Ghostwriter, etc.), GitHub introduced Copilot Free in late 2024 – allowing all GitHub users limited completions per month. There are ofcourse other strategies too like Clearbit (AI-powered marketing data). Clearbit is enterprise-y, but it launched free tools/widgets that showcase its data, acting as lead magnets that eventually channel users to the paid product. Yet, those aren’t really helpful in making the freemium vs free trial decision.

Ultimately, the right model is the one that aligns with users’ journey and business economics. The question earlier was deliberately framed as a yes or no. Freemium or no freemium? Yet, reality is more nuanced. You can pivot models as we’ve seen. The decision isn’t set in stone. Allow it to evolve as your product, it’s cost structure, and competitive landscape evolves. My intention is to re-apply this framework at different points as the product, it’s cost structure and competitive landscape evolves.

What else should I consider? Answer in comments or email me.

Inputs & inspiration from:

[1] Freemium vs Free Trial: Which is Right for You | ProdPad
[2] AI prices could kill freemium pricing (but won’t)
[3] ChatGPT sets record for fastest-growing user base – analyst note | Reuters
[4] Different Shades of PLG: Free-Trial or Freemium? – Battery Ventures
[5] Paid subscription Jasper AI losing momentum to freemium AI tools like ChatGPT – Earnest Analytics