Some AI Tools Still Don’t Have an App – Why?
I spend a lot of time testing AI platforms. Whether it’s a productivity assistant, a code helper, or a virtual companion, I’m always curious how these tools fit into daily life. But every so often, I run into the same surprise: a service so popular it’s practically a household name, yet it has no standalone mobile app.
At first glance, it feels like an oversight. After all, if you want people to build habits around your platform, wouldn’t you make it as accessible as possible? The reality is a little more complicated. Some companies have strategic reasons to hold back on releasing apps, even after their products hit critical mass.
Below, I’ve gathered three of the biggest reasons why some AI tools still don’t have an app, and why that might not be as odd as it seems.
1. App Stores Add Friction (and Fees)
One of the most common reasons is the app ecosystem itself. On the web, an AI platform can launch updates instantly, experiment with new features, and manage subscriptions however it likes. But once you build a native app, especially for iOS, you sign up for layers of compliance and a significant revenue share. Apple, for example, takes up to 30% of in-app purchases. For AI companies that rely on subscription revenue, this isn’t trivial.
Imagine charging $20 per month for a premium model and then losing $6 of that to platform fees.
There’s also the complexity of approval processes. App stores often require strict content policies, privacy disclosures, and periodic reviews. For AI tools that evolve quickly or generate user-specific outputs, this can become a constant administrative burden.
Some platforms prefer to stay web-based so they can iterate at their speed and avoid unexpected rule changes that could disrupt their business model overnight.
2. Web Apps Already Feel Like Native Apps
Another factor is that modern web technology has blurred the line between browser experiences and installed apps. Thanks to progressive web apps (PWAs), an AI tool can feel almost identical to a native application.
A PWA can:
- Save data locally
- Send push notifications
- Work offline in some cases
- Launch from the home screen with a single tap
Many users don’t even notice the difference. For smaller teams, maintaining a single responsive web app is often more sustainable than building and updating two separate codebases for iOS and Android.
Take AI writing assistants, for example. Several popular ones (like Jasper) have opted to prioritize their web dashboards, which are optimized for mobile screens. They still deliver 95% of the functionality without a dedicated app.
It’s not always about a lack of ambition. Sometimes, it’s just a question of resource allocation. When the web version already does the job, building a parallel app may feel redundant.
3. Brand Strategy and Early Priorities
Finally, there’s a strategic layer that often goes unnoticed. Some AI companies deliberately start web-only to focus on early adopters who don’t mind logging in through a browser. This audience tends to be more forgiving of rough edges, more engaged with product updates, and more willing to pay for premium tiers.
In other words, web-first launches attract the most motivated users, the ones who will give detailed feedback and stick around long enough to shape the roadmap.
Candy AI is an example I keep thinking about. For a platform that has had so much success building emotionally intelligent AI companions, it’s almost strange that there isn’t a fully-fledged mobile app yet. But when you look closer, it makes sense. Candy AI porn section kicked off immediately, knowing what would give them that edge advantage.
Their team has put enormous effort into refining its conversational model and memory systems. Launching those features reliably on the web firstletst them iterate faster and collect more data about what works. It also gave them breathing room to test compliance frameworks before committing to an app store distribution.
When your platform’s core value is personalized interaction, you don’t always want to split focus across too many surfaces too soon. In Candy AI’s case, the choice to stay web-first seems less like a gap and more like a deliberate sequence: get the engine right, then scale the access points.
Other examples are similar. Claude, by Anthropic and Perplexity A,I both launched web-only and took time to evaluate mobile demand. In a fast-moving industry, where large language models improve by the month, it can feel smarter to optimize core functionality before spreading across more platforms.
The Tradeoff of Convenience vs. Control
Of course, there’s a cost to staying web-only. Apps have become the default way people expect to engage with services on mobile. When I tried a new web app, I immediately looked for it in the App Store. Not seeing them there felt counterintuitive. Even a well-optimized mobile site can feel less integrated, no biometric login, no app icon badge, no tight OS-level integrations. For some users, these missing conveniences are enough to push them to a competitor that offers an app.
That’s why many AI companies eventually build one anyway. But for those prioritizing iteration speed, regulatory clarity, and platform control, the delay is often worth it.
You Can’t Ignore Them
As AI tools become more central to daily work and life, I expect we’ll see more of them crossing the threshold into app stores. The benefits of having a branded, discoverable presence on iOS and Android are hard to ignore.
But in the meantime, if your favorite AI platform doesn’t have an app, don’t assume it’s an oversight. More often, it’s a calculated decision, a tradeoff between short-term convenience and long-term sustainability. So next time you find yourself wondering why you can’t download an AI service to your phone, consider the bigger picture: the hidden complexity of modern app ecosystems and the strategic patience it takes to launch well, not just fast.
