A developer has just launched Pocketflow, a cross-platform mobile app that lets users build AI workflows by connecting different AI models together, with Runway Gen-3 video generation costing significantly more credits than static image generation to reflect the computational intensity. The app went live on both the iOS App Store and Android Google Play Store after a five-week development sprint, combining gesture-controlled node editing with a monetization system that charges users based on the actual compute resources each AI task requires.

What Makes This App Different From Other AI Tools?

Pocketflow stands out because it treats AI model execution as a cost-aware system. Rather than offering unlimited access or flat-rate pricing, the app implements a dynamic credit system that reflects real server expenses. When a user runs a Runway Gen-3 video generation node, it consumes 5 credits; a Google Gemini image generation node costs only 1 credit. This approach forces developers to think about computational efficiency while keeping the pricing transparent and fair.

The app launched with several thoughtful design choices aimed at making AI workflows feel natural on mobile devices. Users can customize their workflow canvases with five pastel color themes, and the app tracks all activity through a vertical timeline log with colored bullet points, making credit usage and action history immediately visible.

How Does the Monetization System Work?

Pocketflow offers two paths to keep the app running: weekly or yearly subscriptions, plus consumable virtual credits that users purchase as needed. The developer integrated RevenueCat, a third-party payment platform, to handle paywalls and subscription management seamlessly within the app.

The credit-per-node approach required careful mapping of computational costs. The developer established rates based on how resource-intensive each AI model is to run:

• Image Generation (Google Gemini): Costs 1 credit per execution, reflecting lower server and API expenses for static image synthesis.
• Video Generation (Runway Gen-3): Costs 5 credits per execution, reflecting the significantly higher computational load required to generate video sequences.
• Text-to-Speech: Costs 2 credits per execution, sitting between image and video generation in terms of resource intensity.

One technical challenge the developer solved was the lag between purchase and credit availability. After a user buys credits, the app now polls the RevenueCat backend every 500 milliseconds for up to 10 iterations, ensuring updated balances appear instantly rather than after a 1.2 to 4 second delay.

How to Build a Multiplatform AI App in Five Weeks?

• Use Cross-Platform Frameworks: The developer leveraged Compose Multiplatform, which allowed writing the app once and deploying to both iOS and Android simultaneously, cutting development time in half compared to building separate native apps.
• Integrate Third-Party Services Early: Embedding RevenueCat for payments, Google Sign-In for authentication, and Runway Gen-3 for video generation meant the developer could focus on UI and workflow logic rather than building payment systems from scratch.
• Design for Transparency: Implementing visible credit tracking and activity logs from the start helped users understand costs and build trust in the monetization model, reducing friction during the launch phase.
• Optimize for Mobile Gestures: Building gesture-controlled node editing from the beginning ensured the app felt native to mobile rather than like a desktop tool squeezed onto a phone screen.

Why Does This Matter for the AI Video Generation Market?

Pocketflow represents a shift in how developers are thinking about AI video tools. Rather than positioning Runway Gen-3 as a standalone service, the app treats it as one node in a larger workflow pipeline. Users can chain image generation, video generation, and text-to-speech together, creating complex AI workflows without writing code.

The credit-based pricing model also signals a broader trend: as AI video generation becomes cheaper to run, pricing will increasingly reflect actual computational costs rather than arbitrary tiers. By charging 5 credits for video versus 1 for images, Pocketflow makes the cost difference transparent and justified, helping users make informed decisions about which AI models to use in their workflows.

The five-week timeline from concept to launch on both app stores demonstrates that building production-quality AI applications is becoming faster and more accessible. By combining Compose Multiplatform for the interface, RevenueCat for payments, and existing AI APIs like Runway Gen-3 and Google Gemini, a solo developer can now ship a polished, monetized app in a month and a half.