Design mistakes that lead to expensive problems
Or why thoughtful design can save thousands of dollars down the road
The most efficient PostgreSQL environments are intentional. How you design your database environment will shape performance, scalability, and operational cost for years to come. Early missteps often lead to unnecessary infrastructure expansion and painful retrofits.
Architectural decisions shape not only how your database performs, but how efficiently your systems scale, recover, and evolve. In modern environments, where applications rely on interconnected services and global infrastructure, design flaws in the data layer can create disproportionate downstream costs — from performance degradation to operational rework and increased cloud spend.
Capacity planning, for example, requires a deliberate approach. How much data will you store, and where? How many concurrent users will query that data — and how often? A misjudgment here leads either to insufficient resources (and performance issues) or gross over-provisioning (and higher bills).
Workload forecasting is another equally important example. Will your applications be read-heavy, write-intensive, or balanced? Will activity spike during certain times of day or in response to external events? Matching your PostgreSQL setup to your actual workload avoids scaling reactively later.
We also see common pitfalls in data modeling and type selection. Storing overly large data types — or failing to normalize where appropriate — creates downstream issues with memory pressure and indexing. Worse, these inefficiencies can go unnoticed until the system reaches scale.
This is by no means a comprehensive overview of everything that goes into architecting PostgreSQL, nor is it intended to dwell on technical details. Rather, it’s a reminder that designing your PostgreSQL architecture requires thoughtful planning, for both current and future needs. Without foresight, cost-saving decisions made today may result in higher expenses down the road.