Explore 101 boilerplates in this category. Find the perfect starting point for your next project.
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Production-Ready SaaS Starter Kits in Astro, Next.js, and SvelteKit
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The All-In-One Boilerplate to Transform Your Product into SaaS in Hours
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A Python starter kit for your next SaaS
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Swift boilerplate with modules to build your iOS app, AI tool, or game quickly
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Highly modular NextJS AI Boilerplate that allows you to ship an AI App super fast
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The Simple, Speedy & Efficient Next.js Boilerplate
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The Laravel SaaS starter kit that gives you the tools to launch your next SaaS today
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The ultimate Astro SaaS starter kit
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Production-ready mobile app starter kit for launching startups faster
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Database selection determines your data model architecture, from transaction guarantees to scaling strategies. Each database offers distinct approaches to indexing, replication, and query optimization. Our collection features database-specific boilerplates that leverage each database's native capabilities and performance characteristics.
Database-specific boilerplates provide schemas optimized for each database's data model (relational tables, documents, key-value, or column-family), implement database-native indexing strategies (B-tree, hash, full-text), leverage database-specific query features (window functions, aggregation pipelines, graph queries), and include database-optimized connection pooling and caching layers.
Browse our collection of 101 databases 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.
Database boilerplates leverage native features: PostgreSQL uses JSONB and advanced indexing, MySQL implements full-text search, MongoDB uses aggregation pipelines, Redis leverages in-memory data structures, DynamoDB utilizes partition keys. Each schema is designed around the database's specific strengths.
SaaS schemas include multi-tenant architecture patterns (shared database with tenant_id, database-per-tenant, or schema-per-tenant), user authentication tables, subscription management, and audit logs. Each database type uses its optimal data modeling approach—relational normalization for SQL, document embedding for NoSQL.
Query optimizations include strategic indexing, N+1 query elimination, query result caching, connection pooling, and prepared statements. SQL databases use EXPLAIN analysis, NoSQL databases optimize index usage. Each implementation follows database-specific performance tuning guidelines.
Scaling strategies vary by database: SQL databases use read replicas and vertical scaling, MongoDB implements sharding, DynamoDB auto-scales, Redis uses clustering. Boilerplates include connection pooling, caching layers, and schema designs that support horizontal scaling patterns.
Migration systems are database-specific: Prisma for type-safe migrations, Django ORM for Python apps, Active Record for Rails, Flyway for Java. All include version control for schema changes, rollback capabilities, and seed data. Backup strategies use native tools with automated scheduling.