DreamFlow is an AI-first visual app builder from the team behind FlutterFlow that targets builders who want production-ready mobile and web apps without assembling a separate design tool, codegen assistant, Flutter toolchain, and deployment pipeline by hand. Users can start from a natural-language prompt, a template, or a blank Flutter project, then move between an AI agent, a visual canvas, and a code editor while all three surfaces stay synchronized. Apps are built on Flutter, integrate with Firebase or Supabase, and can deploy to the web, Apple App Store, and Google Play Store.
Key Features
- Tri-surface editing: Builders switch between agentic prompts, a visual widget tree with properties panel, and an integrated code editor without losing sync across the workspace.
- Prompt-to-app generation: The DreamFlow Agent can scaffold screens, components, and functionality from text descriptions, including optional image references for design direction.
- Template and scratch starts: Projects can begin from professionally designed templates or Flutter's starter counter app, then grow through AI, visual edits, or direct code changes.
- Flutter multi-platform output: Responsive layouts target phones, tablets, and web from one codebase, with deployment flows for web, iOS, and Android.
- Managed backend integrations: Firebase and Supabase connections are supported out of the box for authentication, data, and full-stack scaling.
- One-click deployment and export: Paid tiers add store deployment, full Flutter project export, Git access on Pro, and local run on simulators or physical devices.
- Context-aware AI models: The agent can use multiple models, with Pro offering priority access to premium models for generation and iteration.
- Local Run: Documentation highlights running projects locally on mobile devices for faster device testing outside cloud-only preview loops.
Building Blocks and Components
The workspace combines an agent panel for conversational feature building, a widget tree for structural edits, drag-and-drop visual tools, and a Dart code editor for precise control. Builders can wrap widgets, jump from canvas selections to code, and combine AI requests with manual property changes as complexity grows. Sample documentation workflows show the agent generating habit trackers, games, and chat apps in minutes, then leaving clear next steps such as cloud storage, notifications, or server-backed leaderboards.
Data and Backend Options
DreamFlow positions itself as a full-stack builder by wiring projects to Firebase or Supabase during setup, including project configuration and generated client code. This lets prototypes move from local-only data to hosted authentication and databases without rebuilding the app in a separate backend tool. Backend choice stays explicit in the UI so teams can align with an existing Firebase or Supabase estate.
Publishing and Hosting
The platform emphasizes production workflows rather than mockups alone. Free plans include one-click web deployment, while Hobby and above extend deployment to Play Store and App Store targets. Pro adds Git-based version control for ongoing production iteration, and Enterprise advertises private cloud deployment, SLAs, and bring-your-own-model options for regulated teams.
Pricing and Plans
| Plan | Price | Credits | Notable limits |
|---|
| Free | $0 per month | 10 starter credits | Private projects, visual and code editing, managed Firebase or Supabase, web deployment |
| Hobby | $20 per month | 100 per month | Web, iOS, and Android deployment, code export, local run on simulators and devices |
| Pro | $90 per month | 500 per month | Git access, priority premium model access, full Hobby deployment and export capabilities |
| Enterprise | Custom | Custom | Dedicated support, onboarding, advanced access control, BYO models, SLA, private cloud |
Credits fund AI-assisted generation and iteration; documentation examples cite fractional credit costs for individual agent builds. The free tier is positioned for experimentation, Hobby for launching a complete app, and Pro for maintaining a production development workflow with version control and higher model priority.
Pros and Cons
Pros:
- Unifies AI generation, visual Flutter editing, and code in one synced workspace instead of separate vibe-coding and IDE tools.
- Flutter output supports web, iOS, and Android from a single project with store deployment paths on paid plans.
- Firebase and Supabase integrations reduce the glue work needed to reach authenticated, data-backed apps.
- Code export and Git on higher tiers lower lock-in compared with pure hosted builders that never expose source.
- Backed by the FlutterFlow team with documentation, templates, and a stated builder community scale on the marketing site.
Cons:
- AI work is credit-metered, so heavy agent iteration on complex apps can require frequent plan upgrades or tighter scoping.
- Store deployment, export, local run, and Git are not available on the free tier, which limits end-to-end mobile shipping without paying.
- Flutter and Dart remain the underlying stack, which may not fit teams standardized on React Native or native Swift and Kotlin workflows.
- Enterprise features such as private cloud, BYO models, and SLAs require a sales conversation rather than self-serve checkout.
Who Should Use DreamFlow
DreamFlow fits indie founders, product builders, and small teams that want to ship Flutter apps quickly with AI assistance but still retain visual and code-level control. It is a strong match when the goal is a production mobile or multi-platform app with Firebase or Supabase rather than a static marketing site. It is less ideal for organizations that need deep native platform APIs outside Flutter, want unlimited AI iteration without credits, or require fully on-premise builder infrastructure without an Enterprise engagement.
