Do free tiers still work for AI SaaS?

Last updated: September 3, 2026

Why are free tiers harder for AI SaaS now?

Free tiers are harder for AI SaaS now because every serious user can create a real compute bill before paying anything.

That changes one of the nicest features of old SaaS economics. Giving another person access to a project-management tool or CRM usually added very little cost. AI products keep spending money as people use them. Every model call, generated video, voice minute, research run or coding-agent task consumes infrastructure.

The latest Stanford AI Index makes the tension unusually clear. Generative AI adoption reached 53% in just three years, and Stanford estimates that the value consumers get from these tools rose 54% in one year. It also notes that most generative AI tools remain free or close to free. So users have quickly learned to expect useful AI before they pay.

At the same time, Bessemer's latest State of AI benchmarks found roughly 60% gross margins among its healthier fast-growing "Shooting Star" AI startups. Its extreme-growth "Supernovas" averaged only about 25%, with some running close to zero or negative margins while chasing growth.

That combination is why this question has become much more important lately. Free access is helping AI spread incredibly fast, while the cost of providing that access is far harder to ignore.

Are leading AI SaaS products still giving users free access?

Yes. Leading AI SaaS products still give away meaningful usage today, although most put a clear ceiling on how much expensive work a free user can do.

We checked the current entry plans of 11 major AI products across chat, coding, app building, search, video, voice and music. Ten give users some form of useful free experience on their main product. Midjourney is the clear exception: its website and Discord product currently require a subscription, although its separate niji journey mobile app has a limited trial.

The more interesting finding is how these free plans are designed. Lovable gives five build credits per day but caps the monthly total at 30. Runway gives 125 credits once. HeyGen allows three videos per month. ElevenLabs resets 10,000 credits monthly. Suno gives 50 credits each day while reserving commercial rights and newer capabilities for paying users.

Free AI is still everywhere. Unlimited free AI is much harder to find.

Product	Free access as of now	Main limit
ChatGPT	Free core product and tools	Lower limits than paid plans on advanced usage
Perplexity	Free core search	5 Pro Searches per day
Cursor	Free Hobby plan	Limited Agent usage
Lovable	Free building	5 build credits daily, up to 30 monthly
Replit	Free Starter plan	Daily Agent credits and 1 live project
Runway	Free plan	125 one-time credits
HeyGen	Free plan	3 videos per month
ElevenLabs	Free plan	10,000 credits per month
Suno	Free plan	50 credits daily, no commercial use
Notion AI	Complimentary AI usage	Upgrade required after the allowance is exhausted
Midjourney	No main web/Discord free plan	Paid access starts at $10 per month

Has AI made free users too expensive to support?

For many AI SaaS products, free users are now expensive enough that freemium has become a serious unit-economics decision.

Bessemer's AI benchmarks show the size of the gap. Its healthier high-growth AI companies averaged around 60% gross margins, while the fastest-growing group averaged only about 25%. That leaves much less room for subsidizing large populations of non-paying users than a software company running at 80% or 90% gross margins.

Agentic products make the problem worse because one visible "request" tells us very little about the cost behind it. Cursor explained this directly when it changed its pricing model: a difficult coding-agent request can consume roughly ten times more tokens than a simple request using the same underlying model.

Video is even easier to understand. A free user asking for ten text answers may cost very little. A free user generating several high-end video clips can burn through GPU resources quickly.

AI founders increasingly have to treat free usage like customer-acquisition spend. If a company gives $4 of compute to 25 people to produce one $20 subscriber, it has already spent $100 before considering marketing, salaries, payment fees or support.

The free tier can still be worth that money. The company just needs to know why it is spending it.

Aren't AI models getting much cheaper to run?

Yes. AI inference keeps getting dramatically cheaper, and those falling costs are one reason free tiers still work at all.

Stanford's AI Index found that the cost of running a model at roughly GPT-3.5-level performance fell from around $20 per million tokens in late 2022 to $0.07 by late 2024. That was a decline of more than 280 times in roughly a year and a half. Depending on the task, Stanford found inference prices falling between ninefold and 900-fold per year.

There is a catch. AI products keep using that cheaper intelligence to do much more work.

A basic chatbot might produce a few hundred tokens. A research agent can search dozens of sources, reason across them and generate a long report. A coding agent can inspect an entire repository and modify several files. Video tools generate increasingly long, higher-quality clips. Reasoning models can spend far more compute on difficult problems than standard models.

The 2026 Stanford AI Index shows how wide that spread has become. For a medium-length prompt, estimated energy consumption among leading models released in 2025 ranged from roughly 5 Wh for some models to more than 20 Wh for the heaviest ones.

So cheaper inference keeps expanding what companies can afford to give away. Product ambition keeps expanding alongside it.

That race is why free AI has survived while truly unlimited usage keeps getting rarer.

Does freemium still create more paying customers than free trials?

Yes, surprisingly often. Current B2B data shows that freemium can produce more paying customers from the same website traffic even though each signup converts worse.

ChartMogul and ProductLed studied 200 software products in January 2026. Regular freemium converted about 3% to 5% of free users at a "good" level, while a normal free trial was around 4% to 6%. AI-native and AI-plus-SaaS products performed somewhat better, with 6% to 8% considered good and 15% to 20% great.

Looking only at conversion rates makes freemium appear weaker. The traffic funnel changes the answer.

In the study, roughly 9% of website visitors entered a freemium product, compared with around 4.5% starting a normal free trial. ChartMogul's modeled funnel therefore produced about five paying customers per 1,000 visitors for freemium and roughly four for a regular free trial.

Credit-card trials were completely different. Only around 35 of 1,000 visitors started one, but close to 30% converted, producing roughly 11 paying customers.

So there is no universal winner. Freemium wins by letting many more people start. A credit-card trial wins by filtering aggressively for people who are already serious.

For AI SaaS, we would judge the funnel on paying customers and gross profit per 1,000 qualified visitors. The signup-to-paid percentage alone hides too much.

Entry model	Starts per 1,000 visitors	Approx. free-to-paid conversion	Approx. paying customers
Regular freemium	90	5.5%	5
Regular free trial	45	8%	4
Ungated freemium	70	8%	6
Credit-card trial	35	30%	11

Do hard paywalls work better for consumer AI apps?

Yes. For consumer subscription apps, the latest large-scale data makes a much stronger case for charging early.

RevenueCat's 2026 subscription report covers more than 115,000 apps, over $16 billion in revenue and more than one billion transactions. Apps using hard paywalls converted 10.7% of downloads into paying customers within 35 days. Freemium apps converted 2.1%.

That is roughly a fivefold gap.

RevenueCat also found that hard-paywall apps generated close to twice as much revenue as freemium apps in its access-model comparison. Yet year-one retention ended up almost identical: 28% for annual freemium subscriptions and 27% for annual hard-paywall subscriptions.

So the paywall seems very good at selecting buyers. It does little to make those buyers stay.

That gets especially relevant for consumer AI utilities acquired through paid ads. Imagine paying Meta or TikTok to acquire someone, paying an AI provider every time that person uses the product, and then collecting no revenue from 98% of those downloads. That can get ugly fast.

Freemium becomes easier to defend when those free users create distribution through sharing, word of mouth, collaboration or public content.

For a standalone consumer AI utility with weak organic distribution, we would test a hard paywall much more aggressively these days than we would for a collaborative B2B product.

Is retention a bigger problem than free-to-paid conversion for AI SaaS?

Yes. AI products are often very good at getting people excited enough to pay and much worse at keeping them.

RevenueCat found that AI-powered subscription apps generated 41% more realized value per payer after one year than non-AI apps. They also churned around 30% faster.

ChartMogul found a similar pattern in a completely separate dataset covering roughly 200 AI-native companies above $250,000 ARR. Median gross revenue retention was only 40%, while median net revenue retention was 48%. Median B2B SaaS NRR was 82%.

The picture improves when AI products move upmarket. ChartMogul found much healthier retention among AI companies charging more than $250 per month, with gross revenue retention around 70% and net revenue retention around 85%.

That suggests many low-priced AI products still attract "tourists": people who want to experience the new capability, pay briefly and leave once the novelty wears off or another tool becomes good enough.

A free tier can make this worse if the product optimizes for the biggest possible signup number without proving a recurring need. It can also help by letting curiosity burn off before payment.

For now, we would worry more about whether customers still want the product six months later than whether the landing page converts 6% or 8% of free users.

Will people really pay for several AI tools at once?

Usually no. Consumer spending remains heavily concentrated in one main AI subscription.

Andreessen Horowitz analyzed Yipit spending data across ChatGPT, Gemini, Claude and Cursor and found that only about 9% of consumers paying for one of those products were paying for more than one.

Usage was concentrated too. For much of the period studied, fewer than 10% of weekly ChatGPT users even visited another major model provider.

That creates a difficult starting point for a new horizontal AI SaaS product. A prospective customer can already use powerful versions of ChatGPT, Perplexity and other assistants for free. Some are also paying $20 or more for one preferred AI service.

A new writing assistant, research tool or general chatbot therefore needs to earn another place in the user's monthly budget.

This is where free access still has a real job. It lets the product prove why it deserves to exist alongside tools the customer already has.

The strongest specialists make that argument easy. Cursor works inside a developer's codebase. ElevenLabs handles professional voice workflows. Lovable turns prompts into working applications. Their value is easier to understand once the user actually tries the workflow.

For a generic AI wrapper with only a slightly different chat interface, a free tier may simply expose how replaceable the product is.

Can a new AI SaaS really charge before showing any value?

Some can, but most new self-serve AI products have a tougher time asking for money before users see a useful result.

The reason is simple: the reference price for trying AI is currently zero.

ChatGPT gives free users its core product and a broad set of tools. Perplexity can be used without an account and gives registered free users five Pro Searches per day. Cursor's Hobby plan lets developers try Agent, Chat and completions. Replit gives free users daily Agent usage.

A new product landing in that environment needs an unusually strong reason to demand payment first.

Midjourney shows the other side. Its main website and Discord experience currently require a paid subscription starting at $10 per month. Midjourney has a recognizable output style, a strong brand and years of category awareness, so users often arrive already knowing what they want.

Enterprise AI can also charge earlier because the buying process looks different. A company evaluating legal AI, compliance software or a private data agent may use a demo or controlled pilot rather than a public free account.

For a new self-serve product, though, we would usually rather show one impressive outcome before asking for a card. The product itself can often sell the subscription better than another polished landing page.

How much free AI usage is actually enough?

A good AI free tier gives users enough room to reach one convincing result and then starts getting uncomfortable for anyone trying to use the product seriously.

Current pricing pages show this pattern across very different categories.

Runway gives new free users 125 credits once. HeyGen gives three complete videos per month. Lovable grants five build credits a day but stops at 30 for the month. ElevenLabs gives 10,000 monthly credits. Suno gives 50 credits each day but keeps commercial use behind its paid plans.

These limits are large enough to answer "does this thing actually work for me?" They are much less suitable for production.

That distinction is important with generative AI because one attempt often tells us very little. The first video can be bad. The first prompt can be poorly written. An app-building user usually needs several iterations before seeing why the tool is useful.

One free generation is therefore often too stingy. Unlimited generation can become a large subsidy.

We would design the allowance around the smallest amount of usage that gives a serious user a fair chance to experience the core loop.

Product	Current free allowance	What the user can realistically test
Lovable	5 build credits daily, max 30 monthly	Build and iterate on a small app
Runway	125 one-time credits	Test real image/video generation
HeyGen	3 videos per month	Produce several complete avatar videos
ElevenLabs	10,000 credits monthly	Test real voice and audio workflows
Suno	50 credits daily	Generate about 10 songs per day for non-commercial use

Should free users get the best AI model?

Free users should get a product good enough to impress them, while the most expensive capabilities can stay behind the paywall.

A weak free experience creates an obvious problem. The user tries the product, gets a mediocre result and leaves believing the paid version probably will not be much better.

Giving away the highest-cost workload without meaningful limits creates the opposite problem.

Current products solve this in several ways. ChatGPT lets free users access a strong core experience while putting tighter limits on heavier tools. Suno lets free users create with v4.5-all while its newer v5.5 model sits on paid plans. Cursor's free Hobby tier supports its core coding experience but gives paying users broader model access and much larger Agent limits.

Notion uses an even tighter version. Anyone can try Notion AI through complimentary responses, but continued AI usage requires a Business or Enterprise plan once that allowance runs out.

We like that basic logic. The free version should be strong enough to prove the product. Payment can unlock more intelligence, more volume, faster execution, commercial rights, larger context windows or advanced workflows.

Users need to understand why the AI is useful before they care about buying more of it.

Are credits replacing unlimited AI SaaS plans?

Yes. One of the clearest pricing shifts right now is the move from vague "unlimited" promises toward measurable usage pools.

Runway is a particularly strong recent example. The company introduced its Max plan with 9,500 monthly credits and is phasing out its old Unlimited plan. Existing eligible Unlimited customers can keep it temporarily, but Runway says they will eventually move to Max.

HeyGen has already made a similar move. Its old Unlimited plans were decommissioned for new purchases earlier this year. Current paid plans allocate credits, and users can buy more when they exhaust them.

Cursor has been moving in the same direction from another angle. It changed agent pricing because fixed request counts stopped making sense once one difficult coding task could consume roughly ten times more tokens than a simple one. Its latest Teams structure separates included usage into pools and gives administrators controls around additional spend.

As seen above, Runway, HeyGen and Cursor sell very different products. Yet all three ran into the same pricing problem: "one request" or "unlimited use" tells the company very little about the resources a customer may consume.

Credits are appealing because they let a company keep a simple subscription while connecting expensive usage to a finite allowance.

Classic SaaS sold access. AI SaaS is increasingly selling access plus a measured amount of work.

Where do free tiers work best in AI SaaS?

Free tiers work best when the product can deliver a visible win quickly, free usage is cheap enough to cap, and successful users naturally want more.

AI coding fits that pattern well. A developer can try Cursor on a real codebase within minutes. App builders such as Lovable and Replit can turn a prompt into something tangible during the first session. Once those users start building repeatedly, the need for more Agent capacity appears naturally.

Creative AI also has an excellent demonstration loop. People instantly understand the value of a good song, voice clone, image or video. The problem is cost, which explains why these products tend to use explicit quotas.

General assistants can support much larger free populations because text has become cheaper and daily habit has strategic value. Stanford's latest AI Index estimates that generative AI has already reached 53% adoption in only three years. Being the tool millions of people use for free can create enormous distribution into paid consumer and enterprise products.

Free works much worse when the first useful result already requires expensive implementation, private company data, weeks of onboarding or substantial human support.

In those businesses, a sales-assisted pilot often gives the buyer enough proof without opening the infrastructure to everyone on the internet.

Does a permanent free tier still make sense for B2B AI SaaS?

Yes, especially when one employee can adopt the AI product alone and later pull the rest of the company into it.

ChartMogul's 2026 conversion study found that 70% of freemium products introduced human sales touchpoints when enterprise users came in through self-service. For free-trial products, the figure reached 80%.

That is a useful clue. Product-led growth and sales are increasingly working together.

A developer can start using an AI coding tool personally. A marketer can discover an AI video product. A salesperson can test an AI research assistant. If the tool spreads inside the company, security, administration, procurement and enterprise pricing arrive later.

Free usage becomes part of the sales funnel.

The approach becomes less useful when individuals cannot activate the product themselves. An AI platform that needs access to the customer's warehouse, months of implementation and legal approval will learn little from thousands of anonymous free accounts.

The economics improve further at higher contract values. ChartMogul found AI-native products charging above $250 per month retaining revenue far better than low-priced AI products.

So we would keep permanent free access for B2B AI where bottom-up adoption is genuinely possible. Where adoption begins with an organization rather than an individual, a controlled trial usually makes more sense.

Can free AI users make money without ever subscribing?

Yes, but this becomes a serious business model only at very large scale.

ChatGPT has just given us the clearest example.

In late August 2026, OpenAI said ChatGPT Ads had already passed a $1 billion annualized revenue run rate less than 200 days after launch. Tens of thousands of advertisers were using the system. OpenAI also said ChatGPT had more than one billion weekly active users.

That changes the economics of its free tier. A free user can now generate advertising revenue without ever buying Plus, Pro or another subscription.

The broader free audience can also feed other parts of the business. Employees bring ChatGPT into companies. Developers move into API usage. Heavy consumers can upgrade to Go, Plus or Pro.

This model is hard to copy at startup scale. A company with 30,000 free users cannot simply add advertisements and expect meaningful economics. Advertiser demand, targeting, measurement and sufficient audience scale all have to exist first.

Still, the OpenAI example expands what "free conversion" can eventually mean. A free user can be monetized through subscriptions, advertising, transactions, enterprise expansion or developer usage.

For most AI SaaS startups today, subscription conversion should still carry the economics. The extra models become interesting once the free audience is large enough to matter.

When should an AI SaaS skip the free tier entirely?

An AI SaaS should skip permanent free access when free users consume expensive resources and contribute very little distribution, learning or future revenue.

Midjourney shows that a strong product can live behind a paywall. Its main service currently starts at $10 per month, and users arriving there generally understand what Midjourney does before subscribing.

A high-value enterprise AI product can make the same choice for different reasons. If the customer needs a security review, private data integration and a custom deployment before seeing value, a public free tier adds little. A paid pilot or tightly controlled evaluation is cleaner.

High-compute products also need to be careful. Autonomous agents, large research jobs and premium video generation can consume several dollars of resources quickly. If only a tiny percentage of those users ever pay, the free tier becomes a very expensive advertising channel.

We would ask one simple question: what does the company get back from a free user?

If the answer includes referrals, public content, collaboration invites, useful product data, habit formation, bottom-up enterprise adoption or a strong chance of later conversion, the subsidy can make sense.

If the answer is mostly "more signups," we would probably stop giving the product away.

Do free tiers still work for AI SaaS?

Yes. Free tiers still work for AI SaaS today, but the winning version looks much more like a controlled sample of the product than old-school unlimited freemium.

The apparent contradiction mostly disappears once we separate the businesses.

ChartMogul's current B2B benchmarks show that freemium can generate roughly as many or more paying customers per 1,000 website visitors than a standard free trial because twice as many visitors are willing to start. AI and AI-plus-SaaS products in that study also posted slightly stronger conversion benchmarks than traditional SaaS.

RevenueCat's consumer-app data gives us the opposite lesson. Hard paywalls convert downloads into customers about five times better than freemium. For consumer AI utilities with expensive usage and little organic distribution, giving the product away can easily become a bad trade.

The pricing changes happening across AI companies tell us how the market is resolving that tension. Runway is replacing its Unlimited plan with a finite credit pool. HeyGen retired its old unlimited plans. Cursor increasingly links agent usage to the actual work being done. Meanwhile, ChatGPT, Replit, Lovable, Perplexity, ElevenLabs and many others still make sure new users can experience useful AI for free.

So free itself is surviving very well.

What is fading is the assumption that a free user should be allowed to consume the product indefinitely at roughly the same intensity as someone who pays.

For self-serve AI SaaS, we would usually keep a free path when the user needs to experience the product before believing the pitch. Give enough usage to reach a genuine "this is useful" moment, put a clear ceiling around expensive work, and make serious repeated usage the reason to upgrade.

For expensive enterprise deployments, high-compute agents and consumer utilities with weak distribution effects, charging earlier can be the better business.

Free tiers still work. They just need to earn their keep now.