Every software project carries a real cost — a team to staff, infrastructure to stand up, data to migrate. Rolling that cost into launching a new product or entering a new market is easy to justify.
Legacy software offers no such upside. Without a new market to conquer, or a new feature to release, it's just old screens being rearranged. This, however, is a perfect opportunity to try Vantage.
Cost🔗
Vantage skips all three. No team — you and an agent, rebuilding the app together. No infrastructure — it runs on your laptop, fast. No new security sign-off — the same connection your team already grants DBeaver is all it needs.
No data migration necessary🔗
Vantage speaks to a wide range of databases — Oracle, MySQL, even DynamoDB — through your existing tables and relations, exactly as they are. Nothing moves.
Your agent can go further: read through the old codebase, pull out the business logic buried in it, and rebuild it inside Vantage. Tens of thousands of lines of legacy code become a few hundred.
No need to assemble engineering team🔗
A team of experienced developers will tell you straight: a powerful application needs a powerful framework. With Vantage you make your AI assistant operate like a professional software engineer — re-use, not rewrite, saving you tokens and the time spent debugging silly errors. A frontier AI model, left to its own devices, will start coding in React — and bury itself in a codebase fifty times larger than it needs to be.
That's exactly the approach Vantage eliminates. It's a low-code framework built specifically for AI agentic coding, aimed at delivering results at the lowest possible token cost.
Feed it anything — a screenshot, the old codebase, a schema, a spreadsheet, an API. Say what you want rebuilt, and a working Vantage app comes out the other side.
"I took screenshots of my legacy admin system, added credentials for my non-prod environment to a
— Peter.envfile, and asked Claude: 'Replicate my old admin in Vantage.' Fifteen minutes later, I was using it to enter data."
Real use-case — cost comparison🔗
We ran a real cost comparison between the different approaches, and recorded the projected numbers. This is what we found:
What else is worth an evening🔗
Not everything worth building is mission-critical. Some of it is just something your team has been meaning to hack together — Vantage is fast enough to actually do it:
Feature switches
Company currently pays a third party for a "feature switch" interface and API. Perfect candidate for Vantage — cut the cost. Spend one evening building it, then present it to the stakeholders.
Custom CRD monitoring
We run Kubernetes with custom CRDs. K9s doesn't support our extensions, and every console that does wants money for it. Build our own internal monitoring console in Vantage.
Live sales, on the wall
The TV on our office wall could be showing real-time sales instead of sitting blank. Build the app in Vantage, drop it on a Raspberry Pi, and plug it straight into the screen.
Enjoy the show, while CI runs
Our developers spend a lot of time watching CI builds and waiting on approvals — we run a fairly unique build/approval process. Vantage can give it a proper visualization: something to watch while sipping tea.
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The agent writes it, then checks its own work🔗
SKILL.md files instruct your agent on how to write yaml files and use mcp to check its work. A typical session involves making a change and checking it right away:
Writing pages/revenue-by-day.yaml.
Executing mcp__vantage-ui__list_logs
"Vantage reloaded our page, but is reporting query errors. Investigating."
Executing mcp__vantage-ui__preview_query
Executing mcp__vantage-ui__run_data_script
"I made a mistake using an incorrect aggregate column in a subquery. Applying the fix."
Writing pages/revenue-by-day.yaml.
Executing mcp__vantage-ui__list_logs
"The page reloaded and there are no errors."
The ability to observe the running app — while keeping it away from production and sensitive user data — is a great way to make sure it works as intended. run_data_script only works if you allow it.
Build once, share with the whole team🔗
One person builds the tool; the whole team opens a link. A Vantage app is only config — YAML and Rhai in a folder — so sharing it is sharing a git repo, or a zip if that's easier. The console travels. The data never does.
You build it
Your agent wires up the pages, tables and actions over the local MCP server — against dev credentials.
You publish it
Push the folder to your corporate git, or hand over a zip. It's just code — no data, no credentials inside.
The team opens it
One vantage:// link installs the app and pulls it from git. Each person signs into the data themselves.
The DBeaver test🔗
If a database client on a cleared laptop passes your security review, Vantage has the same model: a desktop app on your machine, speaking directly to your backends with credentials you hold. Your existing development practice applies unchanged:
- Dev credentials while building. You and your agent work against the dev environment; production credentials exist only where the finished app runs.
- The agent reads; it doesn't write. The agent's MCP access to a running console is read-only — page structure, debug queries, logs. Writes happen in the app, by whoever drives it, under their own credentials.
- The PII-cleared team gets the prod build. Ship the finished app to the cleared team; they connect it with production access. Developers — and their agents — never see it.
- Telemetry, stated plainly. The free version carries crash analytics — and an enterprise build can turn it off or route it to your own account.
See it in action🔗
Periscope shows this shape for Kubernetes — a full control room written entirely in YAML, drilling from namespaces to workloads to pods. The AWS control console example wraps the aws CLI to surface exactly the resources you operate, nothing more — it lands in the examples repo soon.
arrow_forwardNext rung: your console is already live — open screens update by themselves. Here's what that means →