Explore 16 boilerplates in this collection. Find the perfect starting point for your next project.
The Next.js SaaS Boilerplate for busy developers
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A simple & batteries included SaaS boilerplate
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A grown up boilerplate encouraging splitting backend and frontend using Next.js and NestJS
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NextJS & AI wrapper Boilerplate to turn ideas into reality and earn online
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A Python starter kit for your next SaaS
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React landing page boilerplate with easy customization
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A collection of prebuilt Next.js Full-Stack Web Development features and components
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Next.js + Serverless SaaS Starter Kit with Authentication, Payment, Teams, and Dashboards
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A comprehensive Next.js SaaS template with pre-configured authentication, billing, and localization
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Showing 9 of 16 boilerplates
MongoDB provides a powerful data storage solution with specific transaction models, indexing strategies, and query capabilities suited for SaaS applications. Our MongoDB boilerplates implement database-native features—from ACID transactions to advanced indexing—with schemas optimized for MongoDB's query engine and scaling characteristics.
MongoDB boilerplates are designed around the database's data modeling approach and transaction semantics. They leverage MongoDB-specific features like JSONB columns, full-text search, aggregation pipelines, or partition keys depending on the database type. The schema design follows MongoDB's best practices for normalization (SQL) or document structure (NoSQL), with strategic indexes on query-heavy columns. Migration systems use MongoDB-native tools for version-controlled schema evolution.
Browse our collection of 16 MongoDB boilerplates to find the perfect starting point for your next SaaS project. Each boilerplate has been carefully reviewed to ensure quality, security, and production-readiness.
MongoDB 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 MongoDB's strengths—whether that's PostgreSQL's JSONB, MySQL's full-text search, MongoDB's aggregation pipeline, or Redis's data structures.
MongoDB boilerplates include production-tested schemas for multi-tenancy, user management, subscriptions, and billing. The design follows MongoDB'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 MongoDB's query engine.
MongoDB boilerplates implement database-specific query optimizations including strategic indexing on frequently queried columns, query plan analysis, proper use of MongoDB's query features (prepared statements, query builders, aggregations), and N+1 query prevention. Connection pooling is configured for MongoDB's optimal settings, and caching layers are positioned to reduce database load while maintaining data consistency.
MongoDB boilerplates are structured for horizontal and vertical scaling using the database's native scaling features. This includes read replicas, sharding strategies (if applicable), connection pool sizing, and query optimization for distributed systems. The architecture supports MongoDB's scaling patterns—whether that's PostgreSQL's logical replication, MongoDB's sharding, or DynamoDB's automatic partitioning.
MongoDB boilerplates include migration systems using database-specific tools (Prisma migrations, Django migrations, Flyway, Liquibase, or native tools). They follow MongoDB's best practices for zero-downtime deployments, backward-compatible schema changes, and data migrations. Backup strategies leverage MongoDB's native backup features (pg_dump, mysqldump, mongodump) with automated scheduling and point-in-time recovery configurations.