After deploying both PostgreSQL and MongoDB across five production systems for Costa Rican and Latin American clients, we distilled the decision framework we actually use when a new project lands on our desk — no ideology, just trade-offs measured in dollars and milliseconds.
The database conversation in 2024 is less about SQL versus NoSQL and more about understanding which query patterns your application will live in for the next three years. Over the past eighteen months, the Gianko backend team has shipped projects on both engines for clients ranging from a regulated fintech processing wire transfers to a content-heavy media platform serving 50,000 daily readers. Each engagement gave us concrete data points that abstract benchmark articles simply cannot provide — real schema evolution pain, real operational costs on AWS, and real debugging sessions at 2 AM when a query plan goes sideways.
PostgreSQL dominated in three of the five projects. The fintech client needed ACID-compliant transactions across multiple tables — account balances, transaction logs, and audit trails — where eventual consistency was not an option. We ran PostgreSQL 16 on RDS with read replicas and measured consistent sub-5ms query times on indexed lookups against a 140-million-row transaction table. The relational model also proved invaluable for the two e-commerce projects, where the product-variant-inventory-order relationship graph is inherently tabular and benefits enormously from JOINs, foreign key constraints, and the mature ecosystem of migration tooling around Prisma and Drizzle ORM. Operational cost on RDS for these workloads settled at roughly $380/month for a db.r6g.large instance with Multi-AZ — predictable and easy to budget.
MongoDB earned its place in the remaining two projects. The media platform needed to store articles with deeply nested metadata — author bios, revision histories, embedded media arrays, SEO configurations — that changed shape frequently as the editorial team iterated on their CMS. A document model absorbed those schema changes without migrations, and MongoDB Atlas’s built-in full-text search eliminated the need for a separate Elasticsearch cluster, saving the client approximately $200/month in infrastructure. The second MongoDB win was a real-time IoT dashboard aggregating sensor data from agricultural equipment across Costa Rica’s Central Valley. The write-heavy workload — 8,000 inserts per second during harvest season — mapped naturally to MongoDB’s append-optimized storage engine, and the aggregation pipeline handled time-series rollups without requiring a dedicated analytics database.
The practical takeaway we share with every client during discovery: if your data has clear relationships and your business logic depends on transactional integrity, PostgreSQL is the safer, more cost-effective default. If your data is document-shaped, schema-volatile, or write-heavy with flexible query patterns, MongoDB reduces friction. The worst decision is choosing based on team familiarity alone — we have seen two projects arrive at Gianko mid-migration because the original team picked MongoDB for a highly relational domain and spent six months fighting denormalization bugs. Know your access patterns first, then pick your engine.
