Database Choice Is a Product Decision

When teams select a database, they're not just picking a storage layer—they're locking in product constraints for years. Database choices determine your application's latency characteristics, operational costs, and failure modes long before users ever see a feature. Yet these decisions often happen in technical silos, treated as implementation details rather than the product-shaping commitments they actually are. The most expensive databases aren't the ones with the highest licensing fees; they're the ones chosen without understanding how their fundamental tradeoffs will constrain what you can build and how quickly you can ship.

The room where these decisions happen matters. When it's just backend engineers and DBAs, you get choices optimized for developer familiarity and operational comfort. That's not wrong, but it's incomplete. Product managers should be present because they understand user expectations around responsiveness and availability. Finance should weigh in because a Postgres cluster on commodity hardware has radically different economics than an Oracle RAC deployment. Customer success teams know which failure modes generate support tickets versus which ones users tolerate. These aren't technical questions dressed up as business concerns—they're genuine product tradeoffs that deserve explicit discussion.

Consider the real-world implications. Oracle Exadata offers the ceiling for transactional performance and availability—engineered storage with RDMA networking that handles the most demanding financial workloads—but you're committing to seven-figure annual costs and accepting that your entire technical strategy now revolves around Oracle's roadmap and pricing changes. AWS Aurora PostgreSQL gives you automated failover and storage that scales automatically to 128TB, but you've traded infrastructure control for cloud vendor dependency, and certain PostgreSQL extensions won't work in Aurora's modified architecture. SQL Server on EC2 lets you leverage existing Microsoft licensing and gives your Windows-focused team a familiar environment, but you're now responsible for patching, backups, high availability configuration, and capacity planning—undifferentiated work that managed services handle automatically.

The tradeoffs aren't immediately obvious. The team that chooses Oracle Exadata for reliability may not realize they've also chosen 18-month procurement cycles for additional capacity and a talent pool weighted toward expensive consultants. The team that picks Aurora for managed convenience discovers two years later they can't use PostgreSQL features they need, and migrating off Aurora means rewriting application code. The team running SQL Server on EC2 finds themselves building custom automation while their infrastructure team drowns in database operations instead of shipping features.

The right question isn't "which database is best?" It's "which set of constraints and costs align with where this product is going?" That requires product thinking, not just technical expertise. Database selection deserves the same rigorous decision-making you'd apply to pricing strategy or market positioning. Like those decisions, you'll live with the consequences for a very long time.

Databases & Data Systems (Unsexy, Critical Work) Series

• Part 1: Database Choice is a Product Decision
• Part 2: Untitled