How AI is Revolutionizing Database Migration: From Year-long Projects to Quarterly Wins

Database migrations have long been the third rail of enterprise IT. Most organizations know they need to modernize. Few actually pull the trigger.

The math has been brutal: year-long timelines, armies of consultants, and project costs that routinely double or triple initial estimates. But artificial intelligence is rewriting this equation entirely.

Two database experts from Caylent sat down to discuss how they’ve been implementing AI for migrations:

• Ryan Gross, Head of Data and AI 
• Israel Mendes, Engineering Manager

This post is adapted from their conversation. Want to watch it in full? Register to watch the on-demand webinar.

Database Freedom: How AI Enables 3X Faster Database Migrations

The $2 trillion technical debt crisis behind database modernization

Legacy databases are expensive. They're also strategic sandbags. 

Technical debt in the US alone has ballooned to an over $2 trillion cost annually, according to CISQ. Databases make up a sizable chunk of that debt, as Gross noted: “Nearly $200 billion of that cost is spent on licensing costs for databases that could be replaced with open source alternatives.”

In the financial service sector, companies often can’t drive AI powered fraud detection across their entire portfolio of financial products. Why? Simply because many of them are locked up in proprietary databases where the AI tools don't have access.

Why legacy databases limit cloud-native innovation

Proprietary databases create innovation bottlenecks across industries:

• AI-powered fraud detection gets blocked in financial services
• Automated security monitoring can't reach healthcare data
• E-commerce demand forecasting operates with incomplete datasets
• Telco anomaly detection misses critical network patterns

The cloud premium for legacy architectures runs 10-25% higher than modern alternatives. Meanwhile, companies may not spend additional money to replicate production in staging environments because they don't want to pay the existing database licensing costs.

As Gross put it: "You're making architectural decisions [about] environment management. Maybe the way that you're implementing security is different and you're not able to take advantage of the multifaceted capabilities that cloud service providers can bring to bear.”

Why traditional database modernization fails at scale

The complexity starts with decades of accumulated logic. Most legacy databases have been built up over 10 to 20 years. Between different database platforms, despite all using SQL as the primary programming language, there are significant differences in dialects. Oracle and Postgres, for example, have big differences. The gap between Microsoft SQL and Postgres is even greater. MySQL is slightly different from all four.

The resource and risk equation gets worse

Traditional migrations demand what Gross calls "an army of people to parallelize across the thousands of SQL queries and procedures." Testing becomes a bottleneck because databases are at the heart of IT and application architectures. If they go down or break, the business impact is broad.

Integration complexity multiplies the challenge. Application code, BI platforms, Excel spreadsheets, and data lakes all connect downstream. Personnel typically know one platform but not the newer one, creating skill gaps that become barriers to transformation.

"And then oftentimes those people get into IT and find unknown additional technical debt that makes it very hard to estimate," Gross said.

The estimation death spiral

Projects consistently exceed initial estimates. Unknown technical debt discovered during execution makes accurate planning nearly impossible. Companies often abandon entire migrations rather than invest double or triple the original budget.

"In many cases… companies have decided to abandon the entire project and just move on rather than have to invest double or triple what they thought," said Gross.

The AI breakthrough: Quality-first automation at scale

Traditional migrations forced an impossible choice between quality, speed, and cost. You could pick two. AI changes this fundamental trade-off by enabling all three simultaneously through intelligent automation focused on quality first.

Breaking the traditional trade-off triangle

"Normally you've had to choose high quality and low cost... The problem there is you can't stop your feature development during that timeframe. So you're continually building more and more and more work for that team to do, dragging out the timeline," Gross observed.

AI-powered tools like Caylent Accelerate™ solve this through a four-phase architecture:

1. Analyze phase: Extract DDL, stored procedures, views, and functions for comprehensive assessment reporting
2. Translate phase: Convert stored procedures, functions, and views to target databases using performance-tuned prompts
3. Compile phase: Compile on target databases, check basic elements, detect missing dependencies and syntax errors, then retry with GenAI to resolve issues automatically
4. Validate phase: Execute procedures on source and target systems, compare results, identify discrepancies, and automatically generate test cases

Three times faster results

The performance improvements are dramatic:

• Full migrations: migrations that would have taken a year are now being done in four months
• Procedure conversion: 40 weeks down to 10 weeks for large-scale databases (around 2,500 stored procedures)
• Schema conversion: roughly 50% time reduction
• Testing: 85% time savings (5x faster)

Real-world results: The Teamfront success story

Teamfront is a holding company that works with founder-owned software as a service products. One of their portfolio companies, Arborgold, faced rising SQL Server costs that weren't increasing linearly with customer acquisition. The limitations of their SQL Server licensing were keeping them from upsizing their infrastructure, causing performance tuning problems.

The challenge scope

Arborgold had four different SQL Server clusters with over 2,500 different stored procedures. They had started down the path of a traditional manual migration but ran into all the typical challenges: complexity built up over time, no one knowing the details of procedures, and differences between SQL Server and Aurora infrastructure causing a too expensive, slow, error-prone process.

The AI-powered solution in action

The initial manual estimate projected 2,600 hours of work. The AI-powered approach dramatically changed the equation:

• 70% of procedures converted using AI-driven automation witb Caylent Accelerate™ 
• 20% converted with generative AI assisted by human-in-the-loop guidance
• Final 10% hand-coded by Caylent experts for edge cases
• Automatic test parameter generation and validation throughout

The system also identified and retired a large number of stored procedures that were never actually being used.

The business impact

Teamfront achieved a 90% faster timeline compared to traditional approaches. They successfully migrated to Amazon Aurora PostgreSQL using Babelfish extensions, which allowed them to maintain more of their existing database code while enabling long-term maintainability.

The validation-first approach that changes everything

A broader industry study by DORA revealed a troubling paradox: 90% of engineers reported productivity increases from AI tools, with 40% reporting significant increases. But when measured over six months, actual team throughput decreased by 1.5%.

The problem: AI generates more code faster without proper testing, creating more defects discovered later in the process when they're much more costly to fix.

"By focusing on using generative AI not only to do the code transformation, but also to do the test case generation, we're able to take the testing curve and move it right next to the development curve," explained Gross.

This approach catches around 80% of problems in automated feedback loops. Early-stage fixes cost roughly 1.2x normal effort versus much higher costs for later-stage corrections.

Technical capabilities that transform migration work

Modern AI tools handle complex database migration challenges:

• Automated pattern recognition: Handles recursive queries the same way Postgres does, manages concatenation and data type adjustments across SQL dialects
• Context-aware code generation: Adds COALESCE to avoid null values, handles complex datetime operations, rewrites objects with proper casing
• Intelligent error resolution: Retry mechanisms automatically fix missing dependencies and syntax errors
• Comprehensive test generation: Executes source and target procedures, compares results, identifies discrepancies

"We are handling recursive in the same way that Postgres is. We are handling concatenation and we are adjusting data types... We also added very different complex daytime and concatenation, which is also going to be hard to do when we're doing translations," noted Mendes.

Security, compliance, and enterprise readiness

Enterprise database migrations demand bulletproof security. The entire solution runs within your AWS account—none of your data ever leaves your environment. AWS Bedrock ensures none of your data, code, or prompts can be used to train any models.

The deployment architecture

Agentic AI workers can scale infinitely to tackle work in parallel, a perfect pairing for AWS serverless tools:

• AWS Lambda functions for processing
• Amazon S3 for storage
• Amazon DynamoDB for metadata
• Amazon CloudWatch for monitoring

Everything deploys using Infrastructure as Code (IaC). All processing gets logged with token usage tracking for cost management.

"We're using all AWS serverless,” said Mendes. “So Lambdas, S3s, DynamoDB tables, CloudWatch, everything's inside your AWS environment. Everything is easily deployed using IaC.”

The strategic path forward: From assessment to production

As organizations consider the need to modernize their databases, they often find themselves at different stages in the process. Regardless of where you are in the journey, Caylent is here to guide you every step of the way.

• Exploring the Business Case: We help organizations establish a clear roadmap to build a directional business case focused on licensing cost savings
• Ready to Migrate: Our team crafts a tailored migration strategy that balances speed, cost, and minimal downtime to meet your unique needs
• Migration in Motion: We can help you deploy your migration using Caylent Accelerate™ for automated conversions, minimizing overall manual migration effort and errors

The ROI analysis framework

The assessment process starts with code analysis under non-disclosure agreements. This detailed examination determines features and maps them to knowledge bases, producing highly detailed estimation ranges.

Working with AWS teams, organizations can build directional business cases for licensing savings, reduced management overhead, and the agility unlocked around multiple environments and the ability to spin up databases ephemerally and on demand.

The suggested approach: request a complimentary ROI analysis to understand the range, then typically drive a planning and design engagement that can often be funded upfront so costs focus on executing the migration rather than endless assessment.

The future of database modernization

Traditional barriers are falling rapidly. AI tooling addresses the complexity that once made migrations ROI-negative. Quality-first approaches eliminate this risk, so enterprise modernization becomes an obvious choice.

3x faster execution makes business cases compelling. But speed isn't the only benefit—moving now provides access to dozens of new cloud features released monthly, creating competitive positioning against organizations stuck on legacy platforms.

"There are dozens of new features, at least in AWS, released every single month, not just on the database platform, but on the broader platform that you have access to. And it is natively integrated into the modern database platform," added Gross.

The question is no longer whether to modernize legacy databases. It's whether to lead the transformation or follow the competition.

Related reading:

• Data Modernization Strategy | Caylent
• AWS Foundations & Migrations | Caylent
• Caylent Accelerate™ for Database Modernization
• Enterprise Data Modernization: How To | Caylent

How Cayent Can Help

Ready to modernize your database infrastructure with confidence? Caylent offers a complimentary database modernization analysis to help you get started. Our team will analyze your organization’s SQL code and deliver an AI-powered estimate that includes estimated project costs, migration timelines, object complexity, and the effort required. You’ll gain a clear view of your current environment and a strategic path forward, without the guesswork. To learn more about Caylent Accelerate™, visit: https://caylent.com/caylent-accelerate