Your database decision could come back to haunt you
The New Stack Meredith Shubel
Pick the wrong database for an AI prototype, and the production bill shows up later. For regulated companies, that can mean a messy re-platform instead of a smooth launch.
Based on reporting by The New Stack, Meredith Shubel — read the original for the full story.
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Developers building AI prototypes want speed first. Fair enough. But if the database they choose can’t survive the jump to production, that shortcut can turn into a very expensive reroute later.
That risk is smaller for startups and looser internal apps. It gets much uglier in healthcare, finance, and government, where security, compliance, deployment options, availability, and reliability are not nice-to-haves. Phillip Merrick, co-founder and CEO of pgEdge, says the fix often means retargeting the whole application to a different compliant platform, which brings rework, operational overhead, and even a second procurement cycle.
The trap is that many prototype-friendly platforms and production platforms are not really the same product at different settings. They’re built by different teams for different buyers. So a team can end up with something that feels great in a prototype, then discover it cannot run on-premises or in a tightly controlled cloud environment without a lot of pain.
Merrick ties that mismatch to a broader AI problem: too many prototypes never make it to production, or they arrive there with little business value. He cites MIT NANDA’s finding that 95% of organizations get “zero return” on generative AI investments, and IDC research with Lenovo saying that only four out of every 33 AI proofs of concept reach production. The database choice is not the only reason, but it’s clearly part of the mess.
pgEdge is pitching Starfleet as a way around that split. Merrick says it is a 100% Postgres platform meant to work from a free trial through managed cloud, BYO cloud, and on-premises deployment, without changing the underlying database. He also says developers can get it running in under two minutes, use the company’s Agentic AI Toolkit, and rely on copy-on-write branching to work against database copies without cloning everything each time.
The real selling point is continuity. If the same application behaves the same way in the cloud, in a company cloud, or on-premises, the team avoids the old prototype-to-production tax. That’s the part vendors keep trying to market as flexibility, but in practice it’s usually just the cost of getting it wrong in the first place.
My take — AI-written commentary, not fact-checked reporting
The industry loves acting as if prototype convenience is free. It never is. In regulated environments, “we’ll fix it later” is just a polite way of saying “we’ll pay twice,” and usually with more meetings the second time around.
Read more about this at: The New Stack