AI-powered automation is transforming database migrations. Read expert insights on faster, safer, and more cost-effective modernization for enterprises.
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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:
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
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.
Proprietary databases create innovation bottlenecks across industries:
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.”
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.
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.
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.
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.
"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:
The performance improvements are dramatic:
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.
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 initial manual estimate projected 2,600 hours of work. The AI-powered approach dramatically changed the equation:
The system also identified and retired a large number of stored procedures that were never actually being used.
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.
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.
Modern AI tools handle complex database migration challenges:
"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.
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.
Agentic AI workers can scale infinitely to tackle work in parallel, a perfect pairing for AWS serverless tools:
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.”
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.
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.
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:
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
Ryan Gross leads Cloud Data/AI/ML delivery at Caylent. Through his 15+ years of experience, Ryan has guided over 50 clients in building tech-driven data and AI cultures across various industries. By identifying technology trends, and leading the development of asset backed consulting offerings to realize value, he builds a growth culture within his team. Ryan is also a frequent conference speaker on emerging data and AI trends.
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Israel Mendes is an Engineering Manager at Caylent with 8 years of experience in database systems and analytics. A technology enthusiast and early adopter, he made history as Caylent's first dedicated data professional, where he helped establish and scale the company's Cloud Data Engineering practice alongside other key leaders. His technical knowledge and strategic thinking have contributed to shaping Caylent's data capabilities, such as leading the technical development of Caylent Accelerate for Database Modernization. Israel's technical background, combined with his collaborative approach, enables him to guide teams effectively while delivering results for clients on their data modernization journeys.
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Ryan Gross
Israel Mendes
