For years, Microsoft SQL Server has been the default database choice for enterprise applications — ColdFusion included. But as organizations move to the cloud, especially AWS, that old comfort zone has become increasingly expensive and limiting.

At WAM DevTech, we guided a client through the transition from SQL Server to Amazon Aurora PostgreSQL, and the results were immediate — lower costs, greater scalability, and a modern foundation for growth.

What was once a cost-heavy, rigid environment became a flexible, cloud-native system that scaled effortlessly and prepared the business for the future.

Here's what we learned and why Aurora PostgreSQL proved to be the better fit.

The Cost Difference Is More Than Licensing

Let's start with the obvious: SQL Server licensing is expensive.

Even when using RDS for SQL Server, you're still paying for Microsoft's per-core licensing and edition limitations. Want read replicas? You'll need Enterprise Edition. Need high availability? Again, Enterprise Edition.

By comparison, Aurora PostgreSQL runs on open-source PostgreSQL — no licensing fees, no edition gates. You only pay for what you actually use: compute and storage.

In this project, the client reduced their AWS monthly costs by over $5,000 compared to SQL Server Web Edition — and that savings compounds over time as data and traffic grow.

But the real advantage came from scalability.

Scalability Without the "License Ceiling"

RDS for SQL Server supports Multi-AZ for failover but lacks true reader/writer separation. That means it's difficult to scale read-heavy workloads without additional complexity and cost.

Aurora PostgreSQL, by contrast, provides:

• A dedicated writer endpoint for transactions
• Multiple reader endpoints for read traffic
• Automatic storage scaling up to 128 TB
• The ability to add replicas instantly — with no licensing constraints

For our client, this changed everything. Their SaaS platform could now serve more users, run large reports, and handle concurrent workloads — all without provisioning multiple costly SQL Server instances.

This isn't just high availability. It's true horizontal scalability, built right into the architecture.

Built for Cloud Efficiency

SQL Server runs in AWS — but Aurora PostgreSQL was built for AWS.

It integrates natively with:

• AWS Lambda (for serverless and event-driven workloads)
• AWS Database Migration Service (DMS) (for continuous replication and live migration)
• Amazon Redshift (for analytics and reporting)
• Amazon S3 (for backups, exports, and data lakes)

For this client, that ecosystem meant more than convenience — it meant architectural freedom. After the migration, we were able to synchronize PostgreSQL data into Redshift for scalable reporting and tie it into their existing infrastructure for future analytics initiatives.

Flexibility for the Future

Modernization is about flexibility. Aurora PostgreSQL allows you to evolve without disruption.

You can:

• Run it as a traditional relational database
• Scale into serverless mode for variable workloads
• Connect APIs and microservices through AWS Lambda
• Migrate seamlessly between environments

SQL Server, by contrast, remains bound to Microsoft's ecosystem — scaling requires more licenses, and integration options are limited.

Aurora PostgreSQL gave our client a future-proof data layer — one that could grow with their business instead of holding it back.

AI: The Game-Changer in Modern Database Migration

Just a few years ago, database migrations meant months of manual work — rewriting queries, aligning schemas, and hoping nothing broke in production.

Today, AI changes that equation.

Using ChatGPT, we translated complex SQL Server stored procedures and data logic into PostgreSQL syntax accurately — reducing what used to take weeks to a matter of hours.

AI didn't just make the process faster — it reduced risk by identifying mismatches, syntax differences, and performance bottlenecks before migration ever happened.

Paired with AWS Database Migration Service (DMS) for real-time synchronization, the combination made the process practical, efficient, and low-risk.

AI helped us:

• Convert SQL Server functions and queries into PostgreSQL equivalents automatically
• Detect schema inconsistencies before deployment
• Recommend query optimizations for better performance
• Accelerate validation with AI-assisted code review

In short, AI became the bridge between legacy and modern, turning a high-risk, high-effort migration into a predictable, achievable milestone.

Real-World Results

The outcomes were clear:

• AWS cost savings: Over $5,000/month
• True scalability: Reader/writer endpoints and on-demand replicas
• Simplified maintenance: Automatic storage scaling and backup
• New capabilities: Integration with Redshift and serverless workflows

But the real win was strategic: the business now has an infrastructure that scales predictably, adapts easily, and supports growth without financial or technical bottlenecks.

AI and the Future of Modernization

What's most exciting about this evolution is how AI now makes modernization at scale achievable.

AI doesn't replace developers — it empowers them.
It handles the repetitive, error-prone parts of migration so teams can focus on architecture, scalability, and innovation.

As models improve, AI will continue shortening modernization cycles and lowering the cost of transformation.

At WAM DevTech, we see AI not as the trend — but as the catalyst for more thoughtful, efficient modernization.

Final Thoughts

If you're still running ColdFusion or another legacy application on SQL Server, this is your moment to rethink your foundation.

Aurora PostgreSQL isn't just a cheaper alternative — it's a modern data platform designed for scalability, integration, and long-term sustainability.

With AI, AWS DMS, and Aurora PostgreSQL, modernization is no longer a massive rebuild — it's an intelligent transition that preserves business knowledge while unlocking growth.

At WAM DevTech, we've seen firsthand how combining these technologies makes modernization both practical and profitable.