RAG vs Fine-Tuning: Choosing the Right LLM Approach for Your Product
Both RAG and fine-tuning improve LLM performance on your specific use case — but they solve different problems. Here's how to choose.
Backend
PostgreSQL
Relational data architecture, pgvector for AI, and production PostgreSQL
30+Years battle-tested
pgvectorAI embeddings built-in
ACIDFull transaction safety
10TB+Scale tested in production
HOW WE USE IT
PostgreSQL in our stack
PostgreSQL is our default relational database. We design schemas for complex data models, optimize slow queries with proper indexing, and leverage PostgreSQL's extensions — especially pgvector for AI embedding storage and similarity search.
CAPABILITIES
What we deliver
- Database schema design and normalization
- pgvector for AI embedding storage
- Query optimization and index design
- Prisma and SQLAlchemy ORM integration
- Read replicas and connection pooling (PgBouncer)
- Database migrations and versioning
USE CASES
How we apply PostgreSQL
AI Vector Store
pgvector extension for semantic search — store and query embeddings alongside relational data in one database.
Multi-tenant SaaS DB
Row-level security (RLS) for multi-tenant data isolation, with Prisma ORM and automated migrations.
Financial Data Layer
ACID-compliant transaction processing with proper isolation levels and audit logging for FinTech compliance.
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
Frequently Asked Questions
Didn't find what you were searching for? Reach out to us at [email protected] and we'll assist you promptly.
PostgreSQL is the most feature-rich open-source relational database — JSONB for semi-structured data, pgvector for AI embedding storage, PostGIS for geospatial, full-text search, window functions, and strong ACID compliance. For AI applications, pgvector makes PostgreSQL a compelling alternative to a separate vector database for lower-scale RAG systems. We choose PostgreSQL as our default for new applications. MongoDB is preferred when the data model is genuinely document-oriented with high schema variability. MySQL is acceptable for existing stacks but offers fewer advanced features.
A production PostgreSQL setup includes: a managed service (RDS, Cloud SQL, or Azure DB for PostgreSQL) for automated backups and failover, connection pooling with PgBouncer, read replicas for query-heavy workloads, index strategy reviewed and benchmarked under production load, logical replication for zero-downtime migrations, and pgvector configured for vector similarity search if needed. All schema changes go through reviewed migrations with rollback procedures.
Setting up a production PostgreSQL backend for a new application (schema design, migrations, connection pooling, and backup configuration) takes 2-4 weeks. Migrating an existing database from MySQL or another system takes 4-8 weeks including data validation. Adding pgvector for a RAG or similarity search use case takes 1-3 weeks on top of an existing PostgreSQL setup.
FROM OUR CLIENTS
Built with teams who ship
The team took our AI concept from whiteboard to production in 10 weeks. The architecture they designed handles 10x our expected load with no issues.
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