Explore 43 boilerplates in this collection. Find the perfect starting point for your next project.
The All-In-One Boilerplate to Transform Your Product into SaaS in Hours
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Remix SaaS boilerplate with authentication, payments, and more to launch quickly.
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SAAS Starter Kit built for Elixir and Phoenix LiveView
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Next.js + Serverless SaaS Starter Kit with Authentication, Payment, Teams, and Dashboards
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SaaS Codebase on .NET with Vue, React, Svelte and Tailwind CSS
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Launch trending directories in minutes with this ready-to-use template
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Production-ready mobile app starter kit for launching startups faster
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The ultimate Astro SaaS starter kit
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The Next.js SaaS Boilerplate for busy developers
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Showing 9 of 43 boilerplates
PostgreSQL provides a powerful data storage solution with specific transaction models, indexing strategies, and query capabilities suited for SaaS applications. Our PostgreSQL boilerplates implement database-native features—from ACID transactions to advanced indexing—with schemas optimized for PostgreSQL's query engine and scaling characteristics.
PostgreSQL boilerplates are designed around the database's data modeling approach and transaction semantics. They leverage PostgreSQL-specific features like JSONB columns, full-text search, aggregation pipelines, or partition keys depending on the database type. The schema design follows PostgreSQL's best practices for normalization (SQL) or document structure (NoSQL), with strategic indexes on query-heavy columns. Migration systems use PostgreSQL-native tools for version-controlled schema evolution.
Browse our collection of 43 PostgreSQL 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.
PostgreSQL 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 PostgreSQL's strengths—whether that's PostgreSQL's JSONB, MySQL's full-text search, MongoDB's aggregation pipeline, or Redis's data structures.
PostgreSQL boilerplates include production-tested schemas for multi-tenancy, user management, subscriptions, and billing. The design follows PostgreSQL'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 PostgreSQL's query engine.
PostgreSQL boilerplates implement database-specific query optimizations including strategic indexing on frequently queried columns, query plan analysis, proper use of PostgreSQL's query features (prepared statements, query builders, aggregations), and N+1 query prevention. Connection pooling is configured for PostgreSQL's optimal settings, and caching layers are positioned to reduce database load while maintaining data consistency.
PostgreSQL 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 PostgreSQL's scaling patterns—whether that's PostgreSQL's logical replication, MongoDB's sharding, or DynamoDB's automatic partitioning.
PostgreSQL boilerplates include migration systems using database-specific tools (Prisma migrations, Django migrations, Flyway, Liquibase, or native tools). They follow PostgreSQL's best practices for zero-downtime deployments, backward-compatible schema changes, and data migrations. Backup strategies leverage PostgreSQL's native backup features (pg_dump, mysqldump, mongodump) with automated scheduling and point-in-time recovery configurations.