Tokens, Credits or Effort: How AI App Builder Pricing Actually Works
Every tool in this category costs about $25 a month, and that number tells you almost nothing about what you will actually pay.
The headline price is the least informative fact about any of these products. What matters is the unit — what the meter counts — because the unit decides which of your activities is expensive. And the activity that turns out to be expensive is almost never the one you were thinking about when you signed up.
I build one of these products and it uses one of these models, so factor that in. What follows is the mechanics, which are checkable, and prices as published in mid-2026, which move constantly.
The Three Models
Tokens meter the text the model reads and writes. Bolt works this way: 1M tokens per month on the free tier with a 300K daily cap, Pro at $25/month for 10M tokens with rollover, Teams at $30 per member.
Credits meter interactions or tasks. Lovable works this way: roughly 5 daily and 30 monthly credits free, Pro at $25/month for 100 credits with rollover, Business at $50/month adding SSO and team features. v0 uses a credit variant — about $5 of monthly credits free with a seven-message daily limit, Team at $30 per user, Business at $100 per user.
Effort meters the computation and time a task consumed. Replit moved Replit Agent to this in 2026: the agent estimates the effort each request requires and charges accordingly, with simple changes landing as a single checkpoint typically under $0.25 and complex work costing more. It reached existing Core and Teams subscribers from 1 July 2026.
And flat subscription still exists at the developer-tool end: Cursor is $20/month for Pro, $60 for Pro+, $200 for Ultra, with usage limits rather than a consumption meter.
What Each Model Makes Expensive
This is the part that decides your bill.
Tokens punish reading. A token charge scales with how much context the model had to take in and how much it produced. Which means a long debugging session — where the model reads your files repeatedly, tries something, reads the error, tries again — is expensive. Productive building is comparatively cheap. Being stuck is what costs money, and being stuck is precisely when you least want a meter running.
Credits punish iteration. If a credit is an interaction, then twelve small refinements cost twelve credits whether they were trivial or transformative. Big well-formed requests are efficient; fiddling is not. This rewards knowing what you want before you ask, which is a real skill and not one beginners have.
Effort-based pricing punishes ambition, and hides the number. The agent decides how much effort your request deserves and you find out afterwards. Simple things are cheap. Anything substantial is priced by a judgment you didn’t make and can’t preview. There are also widely reported cases of checkpoints being billed when the operation hung or errored, which is defensible — compute was consumed — and still uncomfortable when you’re the one paying.
Complexity-weighted credits are the model Archie uses: the cost is a function of task complexity and it is visible before you commit rather than after. That is more predictable, and the trade is that it is a coarser instrument than raw token metering, so simple tasks aren’t as cheap as they could theoretically be.
None of these is dishonest. They are different bets about what fairness means when a machine does an unpredictable amount of work.
The Cost Nobody Puts in the Comparison
Every table comparing these tools gets this wrong by omission.
The generation subscription is not the cost of shipping software. For the frontend-led tools, you are also going to pay for a backend — Supabase or equivalent — plus hosting, plus a database that grows, plus file storage, plus whatever authentication provider you end up using. Several of those are free until they suddenly aren’t, and the moment they stop being free is the moment you get traction, which is the worst possible time for a surprise.
There is also a billing layer that most reviews miss entirely: a separate cloud bill that activates as your application scales, independent of the generation credits. And on some platforms, buying extra credits adjusts your ceiling rather than adding a fresh pool on top of what you had.
The number that matters is total cost to ship and keep running. Not subscription price.
The Cost Nobody Prices At All
The rebuild.
If the architecture underneath your application was decided implicitly — by a generator, in week one, from context that didn’t include your business — then there is a reasonable chance you pay for the whole thing twice. Once as it was built and once as it needed to be.
That cost does not appear on any pricing page and it dwarfs every subscription on this page. It is the actual financial consequence of the 70% problem, and the reason I keep insisting that pricing model comparisons are the second-order question.
You can optimize your way to the cheapest possible path to a codebase you have to throw away.
How to Estimate What You’ll Pay
Four steps, and the first is the one people skip.
- Work out whether you iterate or specify. Do you send one considered request or fifteen small ones? Iterators should avoid tokens. Specifiers do well on credits. Be honest — most people iterate far more than they think, especially early.
- Estimate your debugging ratio. What fraction of your time is spent fixing rather than building? If it is high, avoid any model where being stuck costs money, which means tokens first and effort-based second.
- Add the infrastructure line. Backend, hosting, storage, auth. If the tool doesn’t include them, put a real number next to each before comparing anything.
- Ask what a rebuild would cost. Then weight the whole comparison by how likely one is. This changes the ranking more than any of the first three.
Which Model to Pick
If you are prototyping and will throw it away, take the cheapest fast loop and don’t think about it further. Tokens are fine — you won’t be debugging a throwaway for long.
If you are building something you intend to keep, avoid the model that charges you for being stuck. You will be stuck. Everyone is stuck a meaningful fraction of the time, and a pricing model that makes difficulty expensive turns a bad afternoon into a bad invoice.
If you need to forecast a number for someone else — a board, a co-founder, a budget — avoid anything where the price is revealed after the work. Effort-based pricing is reasonable and it is not forecastable.
If you are a developer, flat subscriptions like Cursor’s are the best value in this category by a wide margin, because your judgment substitutes for the compute other tools spend on guessing.
The Honest Version
Compute costs money and somebody has to pay for it. Every model on this page is a genuine attempt to allocate an unpredictable cost fairly, and the companies that changed their pricing in 2026 mostly did it because the old model was losing money, not because they found a cleverer way to extract more.
So the useful frame isn’t which one is cheapest. It’s which one is aligned with how you actually work. A model that charges for the thing you do most will feel extractive no matter how reasonable the rate is.
And all of it is a rounding error against building the wrong thing twice.
Price the rebuild first. The subscription is noise.
Related Reading
Tool-by-tool comparison including what each includes: the best AI app builders in 2026. Why the rebuild is the real cost: vibe coding broke its promise.
Frequently Asked Questions
What is the difference between tokens and credits in AI app builders? Tokens meter the text the model reads and writes, so cost scales with context size and output length — which makes long debugging sessions expensive. Credits meter interactions or tasks, so cost scales with how many times you ask, which makes iteration expensive. Bolt uses tokens; Lovable and v0 use credit variants.
What is effort-based pricing? A model where the agent estimates the computation and time a request requires and charges accordingly. Replit moved Replit Agent to this in 2026, with simple changes typically landing as one checkpoint under $0.25 and complex work costing more. The defining characteristic is that the price is known after the task runs rather than before.
Which AI app builder is cheapest? There is no single answer because the models are not comparable. Most start around $20 to $25 per month, and what you actually pay depends on whether your work pattern is metered expensively. Estimate against your own iteration and debugging habits rather than comparing headline prices.
Why did my AI app builder bill come in higher than expected? Usually one of three reasons: you spent more time debugging than building and your model charges for reading, you iterated in many small requests on a per-interaction meter, or you hit infrastructure costs — backend, hosting, storage — that sit outside the generation subscription and activate as usage grows.
Do I get charged when a task fails? On effort-based pricing, sometimes yes — there are widely reported cases of Replit checkpoints being billed when an operation hung or errored, since compute was consumed either way. On token models you are charged for what was processed regardless of outcome. Worth checking each provider’s current policy directly.
What costs are missing from most pricing comparisons? Two. Infrastructure the generation subscription doesn’t cover — backend, hosting, database growth, file storage, authentication — which for frontend-led tools is a substantial separate bill. And the rebuild, if the initial architecture cannot take your next feature. The second dwarfs every subscription and appears on no pricing page.
