Which simple apps make over $10K/month now?

Which simple apps are really making over $10K/month right now?

Yes. Several genuinely narrow apps are currently above $10,000 a month, and some are far beyond it.

The cleanest examples come from businesses whose revenue is connected directly to payment systems such as RevenueCat rather than estimated from download rankings. HabitKit is currently around $31,800 in MRR. Draft AI is around $24,500 in MRR and recently collected roughly $31,000 over 30 days. 3AK Track & Field is around $11,100 in MRR. iCollect Everything sits near $15,400 in MRR while collecting roughly $80,000 in a recent 30-day period. Verbi is around $14,900 in MRR and recently had a much bigger cash month of roughly $85,000.

Photo AI sits in another league. Earlier this year, founder Pieter Levels disclosed about $105,000 in monthly revenue and $80,000 in monthly profit. Cal AI went much further: MyFitnessPal said the calorie-scanning app had passed $30 million in annual revenue before it was acquired.

These products are very different, but they share one trait: the customer can understand the job almost instantly. HabitKit tracks habits visually. Draft AI turns speech into social posts. 3AK builds running programs. iCollect catalogs collections. Verbi lets people practice spoken languages with AI. Photo AI generates photos. Cal AI estimates calories from a picture.

That is the version of “simple app” worth studying. The product can still contain plenty of code behind the scenes, but its reason to exist fits into one sentence.

App	Main job	Current revenue evidence
HabitKit	Visual habit tracking	~$31.8K MRR
Draft AI	Voice-to-social posts	~$24.5K MRR
3AK Track & Field	Running training	~$11.1K MRR
iCollect Everything	Collection inventory	~$15.4K MRR
Verbi	AI speaking practice	~$14.9K MRR
Photo AI	AI photography	~$105K monthly revenue disclosed
Cal AI	Photo calorie tracking	$30M+ annual revenue before acquisition

Why are simple apps suddenly everywhere?

Simple apps are exploding because building one has become dramatically cheaper, while making people care about it remains brutally difficult.

RevenueCat’s latest subscription-app data makes that contrast unusually clear. The number of new subscription apps launching each month has increased roughly sevenfold in four years. About 77% of new subscription-app launches are now on iOS. AI has pushed development costs down further because small teams can add image recognition, writing, speech, coaching and personalization without building those systems from scratch.

The result is a flood of new products. Revenue has not spread evenly across that flood. RevenueCat found that the top quarter of subscription apps grew more than 80% year over year, while the bottom quarter shrank by 33%. The top 10% grew more than 300%, whereas median growth was only around 5%.

Older apps still capture most of the money too. Apps launched before 2020 account for roughly 69% of subscription revenue in RevenueCat’s dataset, while apps launched in 2025 or later contribute only about 3%.

Building an app has become easier much faster than building an app business. The scarce part is distribution, retention and a reason to choose one product from hundreds of similar alternatives.

What actually counts as a “simple app”?

For this article, a simple app means a focused product with one obvious core job that an ordinary user can explain without a product tour.

HabitKit is a good example because almost everything revolves around completing habits and seeing them on a visual grid. 3AK is broader technically, yet the customer proposition stays simple: get a training plan for running faster. Draft AI takes something people already do—talking through an idea—and turns it into ready-to-post content.

Cal AI shows why judging simplicity by codebase size would be misleading. Its front-end promise is almost absurdly simple: point the camera at food and get a calorie estimate. Behind that one action sit computer vision, food databases, subscription infrastructure and a company that eventually grew to seven employees plus contractors.

We therefore care more about product simplicity than engineering simplicity. A narrowly defined experience can become a large business even when a lot has to happen behind the screen.

Can a basic habit tracker really make $30K a month?

Yes. HabitKit currently makes roughly $31,800 in MRR from one of the oldest ideas in the App Store: helping people stick to habits.

The interesting part is how ordinary the underlying category is. Habit trackers existed long before HabitKit. Founder Sebastian Röhl had also built other apps before it, including a fitness product that stalled at roughly $150 MRR.

HabitKit clicked because its visual grid made the product instantly legible. Röhl has explained that screenshots of the grid received unusually strong reactions before launch. Instead of presenting another list of tasks and streak counters, the app turned consistency into something users could see at a glance.

Distribution changed as the app grew. Its first customers came partly from Röhl building in public, but App Store search later became much more important. HabitKit climbed for searches around “habit tracker,” which put it in front of people already looking for exactly that product.

The revenue trajectory is more impressive than a single screenshot of current MRR. HabitKit was around $3,000 MRR at the end of one earlier year, later crossed roughly $15,000, and now sits above $30,000. That looks much more like a durable consumer utility than a launch spike.

There is no magic in the habit-tracking category itself. HabitKit shows that an old problem can still produce a $10K+ app when the interface is unusually clear and the product earns a strong position in search.

How is an inventory app making around $80K in a month?

iCollect Everything can collect around $80,000 in a strong month because collectors keep needing the same boring job done for years.

The app helps people catalog books, games, movies, trading cards and other possessions. TrustMRR currently shows around $15,400 in normalized MRR, more than 3,200 active subscriptions and roughly $80,000 of revenue over a recent 30-day period. Its App Store presence goes back to 2008, and it has accumulated more than 13,000 ratings.

That long history changes the economics. iCollect has years of reviews, search visibility, structured collection data and users who have already invested time entering their inventories. More than 2,000 teachers across several Tennessee school districts have also reportedly used it to organize classroom libraries.

The gap between roughly $15,000 MRR and around $80,000 in monthly cash collections suggests that subscriptions are only part of the picture. Annual purchases and other in-app purchases can bring cash forward even though they contribute much less to normalized monthly recurring revenue.

iCollect is useful because it kills the idea that a $10K app must ride a hot category. An app built around cataloging possessions can outperform thousands of newer AI products when it owns a small job for long enough.

Why is Draft AI making over $20K MRR when AI writing apps are everywhere?

Draft AI currently clears $24,000 in MRR because it removes one annoying step from content creation instead of asking users to figure out what to do with a general AI assistant.

The workflow is easy to picture. A creator talks through an idea, and Draft AI turns that recording into posts or scripts. TrustMRR currently shows about $24,500 MRR, roughly $31,000 in recent 30-day revenue and around 1,900 active subscriptions.

That means the average economics per subscriber are already substantial. More importantly, the customer arrives with intent. Someone opening Draft AI generally has an idea they want to publish. They do not need to browse a library of AI tools or invent prompts before reaching the useful part.

This narrowness matters more because AI writing itself has become commoditized. ChatGPT, Claude, Gemini and dozens of specialist tools can all rewrite text. Draft AI wins by packaging the model around a repeated behavior: creators have thoughts throughout the day and regularly need to turn those thoughts into something publishable.

There are thousands of AI wrappers with access to similar models. Very few reach $20,000+ MRR. The valuable part here is the workflow around the model.

Can a tiny running niche really support a $10K/month app?

Yes. 3AK Track & Field currently makes roughly $11,100 in MRR by targeting competitive runners far more narrowly than a normal fitness app would.

TrustMRR shows around 3,300 active subscriptions, roughly $14,000 in recent 30-day revenue and close to $130,000 in cumulative revenue. Founder Christian Rac says the app reached roughly $100,000 in revenue within six months at about 80% margins.

The audience is narrow but still large enough. 3AK estimates roughly 50,000 users and has around 1,600 App Store ratings. Its plans range from weekly subscriptions at $6.99 or $9.99 to a $9.99 monthly option and annual plans.

The distribution story is just as important as the pricing. Rac says most growth came organically from owning the track-and-field niche on social media and the App Store. Paid acquisition has remained small, with Meta spending of roughly $50 to $100 a day.

At $10 a month, only around 1,000 paying users are needed to gross $10,000. A niche does not have to be remotely mainstream when a few thousand people care enough about the outcome.

How many paying users does a simple app need to make $10K/month?

A simple consumer app usually needs somewhere between a few hundred and a couple thousand paying users to cross $10,000 a month.

At an effective $5 per payer per month, the number is 2,000. At $10, it falls to 1,000. At $20, it is only 500. The current apps fit comfortably within that range.

Draft AI has roughly 1,900 active subscriptions and around $24,500 MRR. 3AK has about 3,300 and roughly $11,100 MRR. Verbi has around 3,200 active subscriptions and about $14,900 MRR. Differences in annual plans, weekly pricing, discounts and subscription mix explain why subscriber counts do not translate neatly into the same revenue per customer.

This arithmetic is one reason $10K/month is a realistic milestone for a focused app. A founder does not need millions of downloads if pricing is decent and the paying audience has a real reason to stay.

The hard part is finding those first 500 to 2,000 payers cheaply enough.

Effective monthly revenue per payer	Paying users needed for $10K/month
$5	2,000
$10	1,000
$15	667
$20	500
$30	334

Are simple AI apps making more money than normal apps?

AI apps are making more money per paying customer right now, but their users leave faster.

RevenueCat’s latest dataset puts median first-year revenue per payer at about $30 for AI apps versus roughly $21 for non-AI apps, a 41% premium. AI apps also convert trial users to paid subscriptions more easily: median trial-to-paid conversion is around 8.5% compared with 5.6%.

That helps explain the rapid revenue ramps we see in products such as Verbi, Draft AI, Photo AI and Cal AI. AI can make a simple utility feel dramatically more powerful without making the interface more complicated. Take a photo, speak a sentence or start a conversation and the software does work that previously required several manual steps.

Retention is where the picture changes. RevenueCat currently finds 12-month monthly-plan retention of about 6.1% for AI apps versus 9.5% for non-AI apps. On annual plans, AI apps retain roughly 21.1% compared with 30.7%. Refund rates are also higher, at around 4.2% versus 3.5%.

That is a meaningful difference. AI can make people pay quickly, but novelty wears off fast when there is no repeated need underneath it.

Photo AI illustrates both sides. Pieter Levels disclosed roughly $105,000 a month in revenue and $80,000 in profit earlier this year, so there is no question that a narrow AI product can become a serious business. Revenue had previously reached even higher levels, though. The product has survived far beyond its initial launch, yet competition and rapidly improving image models keep moving the ground beneath it.

The strongest simple AI apps attach AI to something people already do repeatedly: creating content, practicing a language, logging food or producing usable photos.

Why can Verbi collect $85K in a month with only about $15K MRR?

Verbi shows why “monthly revenue” can be dangerously misleading: it recently collected roughly $85,000 over 30 days while normalized MRR remained around $14,900.

The language-learning app currently has roughly 3,200 active subscriptions. Its main job is straightforward: let learners practice real conversations with an AI tutor instead of spending all their time on flashcards and drills.

A large gap between cash collected and MRR usually comes from billing cadence. Someone paying $60 for an annual subscription gives the company $60 today, although that contract contributes only $5 to normalized monthly recurring revenue. Lifetime purchases create an even wider gap because the whole payment arrives upfront without adding recurring revenue.

iCollect Everything shows a similar pattern, with around $15,400 MRR but roughly $80,000 in a recent month. Draft AI is much closer, at roughly $24,500 MRR against around $31,000 in 30-day revenue.

When we say an app “makes $10K/month,” we therefore need two categories. Apps above $10K MRR have clearly crossed the recurring threshold. Apps that repeatedly collect more than $10K in monthly cash can also qualify, but we should not pretend the two economics are identical.

Do subscriptions and aggressive paywalls explain these $10K apps?

Subscriptions help a lot, and the latest data shows that asking users to pay early can dramatically lift revenue, but pricing cannot rescue an app people stop using.

RevenueCat finds that hard-paywall apps convert downloads to paid users at about 10.7%, versus only 2.1% for freemium apps. Their median revenue per install after 60 days is roughly $3.09 compared with $0.38 for freemium.

That is a huge early advantage. Interestingly, it almost disappears when we look at long-term retention. After a year, annual-plan retention is about 27% for hard-paywall apps and 28% for freemium apps. The paywall changes how many people pay; it does very little to make those people keep wanting the product.

Plan length matters much more. Across RevenueCat’s current dataset, median one-year retention sits around 1–2% for weekly subscriptions, 6–14% for monthly subscriptions and 20–40% for annual subscriptions depending on category.

Weekly plans can still produce impressive cash quickly. 3AK, for example, offers $6.99 and $9.99 weekly plans alongside monthly and annual subscriptions. For a product that acquires users cheaply, strong first-month monetization can compensate for ugly retention.

Annual plans produce healthier-looking economics because the customer commits more money upfront and renews less frequently. RevenueCat currently finds annual plans generating roughly twice the revenue per install of monthly plans and about five times the revenue per install of weekly plans.

So yes, monetization design can turn the same download volume into vastly different revenue. But once the first renewal arrives, users still ask the simplest possible question: “Do I use this enough to keep paying?”

Why does iPhone keep showing up in these success stories?

iPhone remains the best starting point for many small consumer subscription apps because iOS users generate much more revenue per install.

RevenueCat’s latest data shows that about 77% of new subscription-app launches happen on iOS. Median revenue per install is also substantially stronger on the App Store than on Google Play, and North American iOS customers are especially valuable.

Billing reliability adds another advantage. Around 31% of subscription cancellations on Google Play come from payment failures, more than twice the App Store rate of roughly 14–15%.

This helps explain why a large share of indie success stories begin with SwiftUI or an iPhone-only launch. HabitKit, Draft AI and 3AK all fit comfortably into an ecosystem where a founder can ship one platform, use RevenueCat for subscription infrastructure and reach relatively high-spending customers without supporting every device immediately.

Android still matters once an app grows. Verbi is already available on both major mobile platforms, while Cal AI expanded into a much larger consumer business. For the first $10K, though, iOS often gives a solo developer better economics with less engineering work.

Is distribution now harder than building the app?

Yes. For most simple apps today, getting enough people to discover the product is harder than reproducing its basic features.

Look at how differently the current winners found users. HabitKit gradually built App Store search visibility around “habit tracker.” 3AK says its track-and-field niche drives organic social and App Store acquisition, with very little paid spending. iCollect has nearly two decades of store history and more than 13,000 ratings. Cal AI had an unusually shareable demo: point your phone at a meal and immediately get a calorie estimate.

Photo AI had another advantage entirely. Pieter Levels already knew how to launch products publicly, build SEO traffic and reach an internet audience. A developer starting from zero can recreate pieces of the product without inheriting those distribution assets.

The supply data makes the problem worse. Subscription-app launches have grown about sevenfold in four years, while the top quarter of apps increasingly captures the growth. Features are being copied faster, AI capabilities are available through the same APIs, and polished interfaces can now be produced by very small teams.

A simple app still needs a reason to be found. Search rankings, an audience, a social niche, shareable results, reviews, content or an unusually strong keyword can matter more than another ten features.

Which simple app categories look strongest today?

The strongest simple-app categories right now have repeat usage and a result users can immediately understand.

Health and fitness is particularly attractive. RevenueCat currently puts median first-month realized value per payer at about $24 for Health & Fitness, the highest category in its dataset, and first-year value at roughly $36. That fits what we see with 3AK and Cal AI: users have measurable goals and a reason to come back repeatedly.

Creator tools can also work when they sit inside an existing routine. Draft AI has a clearer retention story than a novelty generator because creators repeatedly need posts and scripts. Language practice has similar potential: Verbi can become a recurring habit if users genuinely use it to speak every day.

Collection and inventory software has less hype but better natural durability. Someone who has spent months building a database in iCollect has a reason to keep it around. Habit tracking works for similar reasons when users make the app part of a daily routine.

AI photo apps can generate much more revenue, as Photo AI proves, but that category is unusually exposed to model improvements and copycats. Generic AI assistants face the same problem even more aggressively.

Simple app category	Current example	What makes it work
Habit tracking	HabitKit	Repeated daily use
Specialized fitness	3AK	Clear measurable outcome
Food tracking	Cal AI	Removes manual logging
Creator workflow	Draft AI	Repeated content need
Language practice	Verbi	Frequent speaking practice
Inventory	iCollect Everything	Long-lived user data
AI photography	Photo AI	Immediate high-value output

Are these simple apps actually durable businesses?

Some clearly are, while the newer AI examples still have a lot to prove.

HabitKit has already moved through several revenue levels rather than appearing suddenly with one viral month. iCollect is an even stronger durability case because the product has existed since 2008. Photo AI has survived several generations of image models and continues to generate six figures a month.

The newer cohort is harder to judge. 3AK was founded recently and has scaled unusually fast. Verbi is newer still. Their current revenue is real, but we do not yet have enough renewal history to treat their present run rates like iCollect’s.

The broader subscription data argues for caution. Median year-one retention recently fell from roughly 31% to 28% for annual plans and from 10% to 8% for monthly plans. Weekly retention remains near 1%. AI apps perform worse than non-AI apps at every major subscription duration.

AI products are especially good at getting users to pay quickly. Whether Verbi, Draft AI and the next wave of narrow AI utilities can hold those customers for several years is a much tougher question.

A simple app becomes genuinely durable when the user keeps producing something valuable inside it: a habit history, a collection database, training progress, language ability, published content or some other accumulation that grows with repeated use.

Can one person really run a $10K/month app?

Yes. A solo founder can run a $10K/month app today, and Photo AI shows that the ceiling can be far higher.

Pieter Levels disclosed roughly $105,000 in monthly Photo AI revenue and about $80,000 in monthly profit while continuing to operate without employees. HabitKit also grew from a solo indie project, while 3AK’s founder reports margins around 80%.

Modern app infrastructure makes that possible. Payments, authentication, analytics, crash reporting, push notifications, databases, AI models and subscription management can all be rented instead of built by an internal team.

There is still plenty of work hiding behind a “simple” product. Someone has to deal with support, App Store reviews, bugs, pricing tests, marketing, refunds and subscription failures. AI apps also have ongoing inference costs that a conventional habit tracker barely faces.

Still, the labor economics are unusually good. A software product can serve another thousand customers without hiring another thousand people. That is why relatively modest revenue can already produce a very attractive business for one person.

Which simple apps make over $10K/month now, and what should we learn from them?

Yes, simple apps are genuinely making over $10,000 a month right now, but the best examples are focused products with unusually good distribution and repeat usage rather than random weekend projects.

HabitKit currently sits around $31,800 MRR. Draft AI is around $24,500. iCollect Everything is roughly $15,400. Verbi is around $14,900. 3AK Track & Field is just over $11,000. Photo AI has disclosed about $105,000 in monthly revenue. Cal AI showed the extreme upside by passing $30 million in annual revenue before being acquired.

The threshold itself is surprisingly small. Depending on pricing, roughly 500 to 2,000 effective monthly payers can be enough to cross $10,000. That number is reachable inside a niche such as track training, collecting or creator workflows.

What has become harder is earning those customers. New subscription-app launches have multiplied, AI lets competitors copy features quickly, and revenue growth is increasingly concentrated among the best-performing apps. A simple product with no distribution edge can disappear immediately.

The apps worth copying conceptually are the ones that compress a recurring job into a very short experience. Take a photo instead of logging a meal. Speak instead of writing a post. Open a grid instead of managing a complicated habit system. Practice a conversation instead of completing another vocabulary lesson.

That is where the $10K/month opportunity looks strongest today: one clear job, frequent use, visible value in the first session and a believable way to reach a few thousand people who already want that job done.