Why Oracle Customers Are Finally Finding the Exit: The Economics of Database Migration

Oracle customers face a difficult truth: everyone wants to leave, but the cost has been prohibitive. AI and cloud economics are changing that calculation.

Every CTO wants off Oracle. That's not news to anyone, including Oracle.

The question has never been whether companies want to migrate. It's been debated whether they can justify the cost.

Spencer Kimball, CEO of Cockroach Labs, has watched this calculation change. His company helps Fortune 100 companies migrate off Oracle databases, and he's seeing the economics shift in ways that weren't possible two years ago.

"Everyone wants to get off of them," Kimball says of Oracle. "They're expensive and almost punitive."

The Lock-In Nobody Chose

Oracle won the database wars. They came out the other side with a product so entrenched that "nobody would be fired for using it."

That created a cash cow. And Oracle took advantage of it.

The lock-in is technical and financial. Stored procedures written in Oracle's dialect. License agreements with terms that favor Oracle. Infrastructure designed around Oracle's requirements.

But the real lock-in is economic. Migration projects used to cost $10 million over three years with 100+ consultants from firms like Accenture. The ROI was negative. You'd reduce technical debt and gain some intangibles, but you wouldn't make the money back—at least not immediately.

So companies sat on growing mountains of technical debt. The pain wasn't acute enough to justify the cost.

Cloud Started the Shift

The cloud created the first forcing function. Oracle's hardware requirements don't match cloud economics. InfiniBand networks and specialized hardware made sense in the data center era. In the cloud, you need databases that run anywhere: AWS, Azure, Google Cloud, private data centers, and hybrid environments.

"The idea that you can actually buy expensive and custom hardware to support your database is a fading memory," Kimball says.

This created urgency. If you're going to move to the cloud anyway, do you really want to lift and shift Oracle? Or do you want to modernize?

But urgency alone doesn't fix the ROI problem.

AI Changed the Math

AI is impacting two aspects of migration economics.

First, it's lowering costs. AI tools can now analyze undocumented legacy code, generate documentation, create unit tests, and suggest complete migrations. Work that used to require 100 consultants might now require 25.

"If Accenture with AI technologies is able to go from 100 consultants to 25 for the same project, then the ROI for more of those legacy workloads becomes positive," Kimball explains.

Second, AI is creating new urgency. Companies that don't modernize their databases can't easily add AI capabilities to their existing applications. If you want to compete against challengers using AI-powered products, you need a modern data architecture.

"How do you invest them with intelligence and the kinds of capabilities that are going to make you succeed?" Kimball asks. "You've got to rationalize that with your existing products."

Oracle's Strategic Shift

Oracle's recent earnings show growth in AI infrastructure. They've become the fourth most popular cloud provider, and they're investing heavily in AI data centers.

Kimball sees this as both a strength and a vulnerability. "In some ways, they've simply become an AI data center play in terms of where the recent market cap and where they're putting their investments and focus."

That means Oracle is moving further from databases. For companies looking at database modernization, Oracle's strategic direction matters. Are they investing in the database products you're using? Or are they investing in becoming an AI infrastructure provider?

The Cost of Scale

AI workloads bring a specific challenge: scale. Virtual agents could create 10x or 100x the database traffic of human users.

"What happens if you have 10 times that? What happens if you have 100 times that?" Kimball asks. "Every vertical architecture is going to break."

If you're spending $10 million annually on your database today, do you want to spend $100 million in three years? The economics don't work. AI agents might improve customer experience, but they won't generate ten times the revenue to cover ten times the database costs.

This is where distributed architectures have an advantage. Cockroach Labs runs large-scale test clusters (300 nodes, petabytes of data) that used to cost $500,000 for a duration of three weeks. By running on spot instances, they dropped the cost to $100,000—an 80% reduction.

Distributed databases built for resilience can handle spot instances being preempted. Monolithic databases can't.

By incorporating multi-tenancy (packing multiple workloads on the same physical database) and storage optimization (using S3 for cold data and locally attached SSDs for hot data), Kimball believes they can achieve a 10x cost improvement over the next few years.

The Resilience Factor

Downtime costs matter more than license fees for many organizations. Some financial services customers measure downtime costs in millions of dollars per minute.

Cockroach Labs released benchmark results comparing CockroachDB against Oracle GDD 23ai under chaos conditions: pod failures, network partitions, and disk stalls. CockroachDB showed 5x better resilience.

That's not marketing speak. The methodology is open and documented, so anyone can reproduce the tests.

For organizations where downtime is expensive, resilience becomes part of the TCO calculation. A database that costs more but stays up during failures might actually save money.

Regulatory Pressure

EU regulations are adding another forcing function. The DORA regulation requires companies to pay attention to third-party provider risk. You can't just assume AWS is too big to fail.

"You simply have to have a better and better story about what happens when there's an outage," Kimball says.

This favors databases that can run actively across cloud providers. If AWS goes down, your application keeps running on Azure or Google Cloud. No data loss. No postmortem. No downtime.

That capability is difficult with legacy monolithic architectures. It's built into distributed databases from the start.

The Migration Window is Opening

Two forces are converging: costs are dropping (thanks to AI-assisted migration tools), and benefits are increasing (thanks to cloud requirements, AI workloads, and regulatory pressure).

"Where do the costs decrease in their dollars and cents and total complexity? How many people have to be on the projects? What are the opportunity costs?" Kimball asks. "You want costs to go down and value to go up, and that's actually happening."

The window for migration is opening. The ROI calculation that was negative two years ago is becoming positive.

For CTOs and CIOs who have wanted to modernize but couldn't justify the cost, the math is finally working in their favor. The cloud started the conversation. AI is accelerating it.

Oracle customers are finding the exit. The question is whether your organization will wait until the pain becomes acute, or whether you'll move while you still have time to plan the migration properly.