Save 40+ hours when developing your next Flutter app
Fast Flutter Template is a comprehensive boilerplate that handles all the basics for you so you can concentrate on building unique features for your app. Based on Riverpod for state management, it includes:
Authentication system
State management with Riverpod
Navigation with auto_route
Local storage with Hive
Dark/Light Theme support
Internationalization setup
Firebase Integration
RevenueCat for in-app purchases
Onboarding screens
Push notifications
Fastlane CI/CD scripts
The template is constantly updated and includes access to a Discord server where the developer provides support with any questions regarding app development.
A fully customizable starter kit to seamlessly launch responsive Android, iOS, and Web apps with Flutter powered by Firebase and Vertex AI.
Dart
Custom UI
Material
Firestore
Lemon Squeezy
RevenueCat
Firebase
Flutter
Features:
AI
Analytics
Auth
Backend
CRUD
Feedback
i18n
+11 more
Frequently Asked Questions
Dart
What makes Dart ideal for SaaS development?
Dart excels in SaaS development due to its robust ecosystem, strong typing capabilities, and excellent library support. Dart boilerplates leverage language-specific features to provide type-safe database queries, efficient API routing, and optimized runtime performance. The language's maturity means you get battle-tested packages for authentication, payment processing, and background jobs that integrate seamlessly.
Firebase
What Firebase-specific architecture patterns are implemented?
Firebase boilerplates leverage the framework's native architecture patterns including its routing system, middleware pipeline, and controller/handler structure. They implement Firebase's conventions for separating concerns, dependency injection, and service layer patterns. The codebase follows Firebase's best practices for organizing models, views/components, and business logic to ensure maintainability as your application grows.
Flutter
What Flutter-specific architecture patterns are implemented?
Flutter boilerplates leverage the framework's native architecture patterns including its routing system, middleware pipeline, and controller/handler structure. They implement Flutter's conventions for separating concerns, dependency injection, and service layer patterns. The codebase follows Flutter's best practices for organizing models, views/components, and business logic to ensure maintainability as your application grows.
Riverpod
What Riverpod-specific architecture patterns are implemented?
Riverpod boilerplates leverage the framework's native architecture patterns including its routing system, middleware pipeline, and controller/handler structure. They implement Riverpod's conventions for separating concerns, dependency injection, and service layer patterns. The codebase follows Riverpod's best practices for organizing models, views/components, and business logic to ensure maintainability as your application grows.
Flutter
What Flutter-specific component architecture is used?
Flutter boilerplates follow the framework's component composition patterns with reusable, atomic design components. They implement Flutter's best practices for component structure, props handling, event management, and lifecycle methods. The component library includes authentication flows, dashboards, data tables, forms with validation, and navigation—all built with Flutter's native features like hooks (React), composition API (Vue), or directives (Angular).
Firestore
What Firestore-specific features are leveraged in these boilerplates?
Firestore boilerplates utilize the database's native capabilities including its transaction model (ACID for SQL, eventual consistency for NoSQL), indexing strategies (B-tree, GiST, full-text search), and advanced features like JSON columns, array types, window functions, or document queries. The schema design takes advantage of Firestore's strengths—whether that's PostgreSQL's JSONB, MySQL's full-text search, MongoDB's aggregation pipeline, or Redis's data structures.
Hive
What Hive-specific features are leveraged in these boilerplates?
Hive boilerplates utilize the database's native capabilities including its transaction model (ACID for SQL, eventual consistency for NoSQL), indexing strategies (B-tree, GiST, full-text search), and advanced features like JSON columns, array types, window functions, or document queries. The schema design takes advantage of Hive's strengths—whether that's PostgreSQL's JSONB, MySQL's full-text search, MongoDB's aggregation pipeline, or Redis's data structures.
RevenueCat
What RevenueCat API features are implemented?
RevenueCat boilerplates implement the provider's complete API suite including checkout sessions, subscription lifecycle management, customer portal, webhook event handling, and invoice generation. They use RevenueCat's latest API version with proper error handling, idempotency keys, and retry logic. The integration includes RevenueCat-specific features like payment intents, setup intents, subscription schedules, and tax calculation APIs.
Dart
What Dart-specific tools and libraries are included?
Dart boilerplates include the language's most popular and production-proven tools. This typically includes testing frameworks, linters, formatters, build tools, and package managers specific to Dart. You'll get pre-configured toolchains that enforce best practices, automated testing pipelines, and development environments optimized for Dart development workflows.
Firebase
How does Firebase's ORM/database layer work in these boilerplates?
Firebase boilerplates use the framework's native ORM or query builder (Prisma, Eloquent, Active Record, SQLAlchemy, etc.) with pre-configured models for users, subscriptions, teams, and common SaaS entities. They include optimized queries, relationships, migrations, seeders, and database connection pooling. The implementation leverages Firebase's specific features like eager loading, query scopes, and transaction handling for performance.
Flutter
How does Flutter's ORM/database layer work in these boilerplates?
Flutter boilerplates use the framework's native ORM or query builder (Prisma, Eloquent, Active Record, SQLAlchemy, etc.) with pre-configured models for users, subscriptions, teams, and common SaaS entities. They include optimized queries, relationships, migrations, seeders, and database connection pooling. The implementation leverages Flutter's specific features like eager loading, query scopes, and transaction handling for performance.
Riverpod
How does Riverpod's ORM/database layer work in these boilerplates?
Riverpod boilerplates use the framework's native ORM or query builder (Prisma, Eloquent, Active Record, SQLAlchemy, etc.) with pre-configured models for users, subscriptions, teams, and common SaaS entities. They include optimized queries, relationships, migrations, seeders, and database connection pooling. The implementation leverages Riverpod's specific features like eager loading, query scopes, and transaction handling for performance.
Flutter
How is state management handled in Flutter boilerplates?
Flutter boilerplates use the framework's recommended state management approach—whether that's React Context + hooks, Redux Toolkit, Zustand, Pinia (Vue), NgRx (Angular), or Svelte stores. They include pre-configured state slices for authentication, user data, subscriptions, and UI state with proper TypeScript typing. The implementation follows Flutter's patterns for global state, local component state, and server state synchronization.
Firestore
How is the Firestore schema designed for SaaS applications?
Firestore boilerplates include production-tested schemas for multi-tenancy, user management, subscriptions, and billing. The design follows Firestore's best practices for data modeling—whether that's normalized tables with foreign keys (SQL), embedded documents vs. references (MongoDB), or partition key strategies (DynamoDB). Schemas include proper constraints, default values, and relationship management optimized for Firestore's query engine.
Hive
How is the Hive schema designed for SaaS applications?
Hive boilerplates include production-tested schemas for multi-tenancy, user management, subscriptions, and billing. The design follows Hive's best practices for data modeling—whether that's normalized tables with foreign keys (SQL), embedded documents vs. references (MongoDB), or partition key strategies (DynamoDB). Schemas include proper constraints, default values, and relationship management optimized for Hive's query engine.
