Explore 4 boilerplates in this collection. Find the perfect starting point for your next project.
Production-Ready SaaS Starter Kits in Astro, Next.js, and SvelteKit
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SaaS boilerplate with AWS-powered features to accelerate your MVP development
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Next.js + Serverless SaaS Starter Kit with Authentication, Payment, Teams, and Dashboards
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Production-ready AWS serverless kit using best practices
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DynamoDB provides a powerful data storage solution with specific transaction models, indexing strategies, and query capabilities suited for SaaS applications. Our DynamoDB boilerplates implement database-native features—from ACID transactions to advanced indexing—with schemas optimized for DynamoDB's query engine and scaling characteristics.
DynamoDB boilerplates are designed around the database's data modeling approach and transaction semantics. They leverage DynamoDB-specific features like JSONB columns, full-text search, aggregation pipelines, or partition keys depending on the database type. The schema design follows DynamoDB's best practices for normalization (SQL) or document structure (NoSQL), with strategic indexes on query-heavy columns. Migration systems use DynamoDB-native tools for version-controlled schema evolution.
Browse our collection of 4 DynamoDB 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.
DynamoDB 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 DynamoDB's strengths—whether that's PostgreSQL's JSONB, MySQL's full-text search, MongoDB's aggregation pipeline, or Redis's data structures.
DynamoDB boilerplates include production-tested schemas for multi-tenancy, user management, subscriptions, and billing. The design follows DynamoDB'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 DynamoDB's query engine.
DynamoDB boilerplates implement database-specific query optimizations including strategic indexing on frequently queried columns, query plan analysis, proper use of DynamoDB's query features (prepared statements, query builders, aggregations), and N+1 query prevention. Connection pooling is configured for DynamoDB's optimal settings, and caching layers are positioned to reduce database load while maintaining data consistency.
DynamoDB 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 DynamoDB's scaling patterns—whether that's PostgreSQL's logical replication, MongoDB's sharding, or DynamoDB's automatic partitioning.
DynamoDB boilerplates include migration systems using database-specific tools (Prisma migrations, Django migrations, Flyway, Liquibase, or native tools). They follow DynamoDB's best practices for zero-downtime deployments, backward-compatible schema changes, and data migrations. Backup strategies leverage DynamoDB's native backup features (pg_dump, mysqldump, mongodump) with automated scheduling and point-in-time recovery configurations.