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"Why do I need to give Flyway this extra database?" That's one of the most common blockers we hear from users setting up a Flyway project. Flyway has long had a concept of a build database or shadow database that is a dedicated sandbox database for Flyway to do background work in. It's required for Flyway to be able to simulate what running a set of migrations will do to a database.

We have all watched AI speed up the way we write software. With tools like Copilot and ChatGPT, developers can spin up boilerplate, write complex functions, or draft entire micro services in minutes instead of days. It feels like magic. But there is a silent catch that we do not talk about enough: writing the code is only Day 1. The real challenge is Day 2 operations, which is everything that happens after that code is deployed.

India is the world's fourth-largest economy and accelerating fast. Here's why it should be on your network roadmap. Few markets combine scale, speed, and government intent the way India does right now. With a digital economy on track to reach USD $1 trillion by 2030, hyperscalers committing tens of billions in fresh investment, and connectivity infrastructure undergoing a generational upgrade, India has become one of the most consequential expansion decisions a technology business can make.

Most of what runs on a modern platform is built to stay up. Applications serve requests, workers process queues, and managed services keep databases and caches available around the clock. But a meaningful share of the real work does not fit that always-on shape.

When I ask an AI agent to change code, I also want it to run the application and test what it changed. Asking it to write some tests is a start. But if it invents the expected responses from the same assumptions it used to write the code, those tests can miss the same mistake. Traffic replay gives the agent something concrete to test against: requests and responses captured from a working application.

CloudZero’s own AI Economics Pulse for September found the 75th percentile of its 430-company customer panel crossed 10% of its cloud bill on AI for the first time in August. Gartner’s latest survey found only 22% of organizations have scaled AI successfully and 11% don’t know what their own function spent on it last year. CJ Gustafson showed what that gap looks like on an actual invoice this week.

Worldwide AI spending will reach $2.59 trillion in 2026, up 47% from 2025, according to Gartner. Yet only 37% of organizations report any earnings impact from AI, McKinsey finds. These AI statistics cover what companies spend, what AI costs to run, and the ROI they're actually getting. That gap between the two headline numbers is the story of AI in 2026.