By Jae S. Jung
At WAM DevTech, we spend a lot of time helping businesses modernize the parts of their operation that most people never see. While working with an organization, we saw a familiar situation: they needed to scale their database to support growth, but the licensing costs tied to SQL Server made it increasingly expensive to do so.
In the past, recommending a move from SQL Server to PostgreSQL, or any migration to a different database, would have been considered a heavy lift. The cost, complexity, and risk often outweighed the potential benefits. But with the combination of AWS Database Migration Service (DMS) and ChatGPT, the equation changed. The migration effort became more practical, less costly, and ultimately more achievable than ever before.
That is when we recommended something bold: move the entire operation to Amazon Aurora PostgreSQL.
To make the switch, we relied on AWS Database Migration Service (DMS) both for syncing data between SQL Server and PostgreSQL during preparation and for the final migration. PostgreSQL does support cross-database connections, but with limitations compared to SQL Server's synonyms and cross-database views. By leveraging DMS, we overcame these limitations and kept data aligned across systems during the transition.
When it came time for the actual migration, DMS handled the heavy lifting of moving data from SQL Server to PostgreSQL. With careful planning and multiple practice runs, we completed the final cutover in just one evening. This minimized downtime and gave the organization confidence that their new system was ready to go.
Of course, the migration also meant converting application logic. Queries using TOP 10 were rewritten as LIMIT 10. Functions like DATEDIFF became PostgreSQL equivalents such as AGE() or DATE_PART. String concatenations switched from + to CONCAT, booleans from 1/0 to true/false, and inserts were updated with PostgreSQL's RETURNING syntax
This is where ChatGPT became a valuable partner. By using AI to help translate SQL Server queries into PostgreSQL syntax, we saved time, reduced errors, and streamlined what would otherwise have been a tedious manual process. The combination of AWS DMS for data migration and ChatGPT for SQL conversion made the entire project realistic and achievable.
One of the biggest wins came with how Aurora PostgreSQL handles read and write traffic. Out of the box, Aurora provides a writer endpoint for transactions and reader endpoints that scale across replicas. Applications can direct heavy reporting or query workloads to readers while preserving performance on the writer. This is not just high availability, it is true horizontal scalability.
By comparison, RDS for SQL Server only offers a similar capability through Enterprise Edition with Always On Availability Groups. But here is the catch: Always On is primarily designed for high availability and disaster recovery. If the primary SQL Server instance goes down, a secondary replica can take over. While readable replicas exist, they are not integrated into the same kind of seamless reader endpoint model as Aurora. In practice, this means SQL Server Enterprise provides resiliency, but not the same scale-out performance that Aurora delivers.
For this organization, Aurora PostgreSQL delivered both: enterprise-grade resiliency and cost-effective horizontal scaling, without paying the steep licensing costs tied to SQL Server Enterprise.
The financial results were clear right away. After the migration, the organization reduced their AWS bill by more than $5,000 per month compared to running on SQL Server Web Edition. While that was the immediate win, the bigger story was what would have happened as the SaaS application supporting multiple clients continued to grow.
SQL Server Web Edition is inexpensive but not designed for scalability. As more clients signed on, the organization would eventually have been forced to move up to SQL Server Enterprise Edition and provision multiple instances. At that point, costs would not simply rise in a straight line. They would have increased at an exponential rate, driven by both licensing and infrastructure requirements.
By migrating to Aurora PostgreSQL, the SaaS platform avoided this costly ceiling. Aurora allows storage to scale automatically and read replicas to be added on demand, all without enterprise licensing fees. This means the business can onboard new clients with predictable, controlled infrastructure costs.
In other words, the $5,000 monthly savings was just the beginning. The real return comes from building a SaaS foundation that can grow without cost curves spiraling out of control.
In addition to lowering costs, they gained:
Thanks to the combination of AWS DMS and ChatGPT, the migration was not only possible but also efficient.
For businesses considering a similar move, three lessons stood out from this project:
For the organization, this migration was not just about technology. It was about creating breathing room in their operations, room to grow without costs spiraling out of control.
At WAM DevTech, that is always our goal. We do not push modernization for its own sake. We recommend it when the numbers and the strategy line up. In this case, moving from SQL Server to PostgreSQL was the clear path forward: one investment that continues to pay off month after month.
