Notes on the build
Where dvt fits, why we draw the lines we draw, and the thinking behind dashboards as versioned specs.
dvt as a Snowflake Native App: the security review that runs out of questions
dvt runs as a Snowflake Native App on Snowpark Container Services, so the usual SaaS BI vendor questionnaire collapses to answers your admin can verify.
You could build this yourself. Here's the rest of it.
An honest build vs buy breakdown: what an afternoon with an agent buys you, and what months of work, dozens of architecture decisions, and more than a thousand merged pull requests bought after that.
Skills, subagents, and dvt: make the agent work like your team
Claude Code and Codex can already build dvt dashboards over MCP. Skills and subagents teach them to build *your* dashboards — and dvt's org skills give those conventions a distribution channel.
You don't need a semantic layer to build dashboards with dvt
Raw tables, dbt models, or governed semantic views — dvt works on top of whatever you have. There's no modeling prerequisite, because dvt is the visualization layer and nothing else.
Housekeeping is a read problem
“Read all the dashboards and come up with folders to organize them into.” One sentence, and an agent reorganized our entire internal org. The interesting part isn't that dvt can write dashboards — it's that an MCP server can read every one of them at once.
The code-first dashboard landscape for analytics engineers
A buyer's guide to code-first BI: Lightdash, Evidence, Metabase (open-source), plus dvt, a spec-first hosted alternative.
Build dashboards with an AI agent: how dvt's MCP integration works
Why most AI dashboards dead-end as screenshots — and how dvt's MCP surface lets an agent author a live, versioned dashboard spec instead.
Dashboards as code: version control, review, and rollback for your BI layer
What 'dashboards as code' actually means, what it buys you, and how dvt implements it — versioned JSON specs that live in git alongside your dbt models.
The static HTML trap: how we recognized the need for dvt
AI made everyone a dashboard author overnight. The output was a pile of static HTML files — stale on arrival, impossible to reproduce, and scattered across everyone's downloads folder. That gap is why dvt exists.
The two things dvt actually promises
Faster dashboard dev cycles, and always knowing what's published. What dashboards-as-code buys you after the authoring is done.
Where dvt fits — and the skills that should ride alongside it
dvt is the visualization layer of the data stack, and nothing more. Here's why that narrowness is the point, and the companion skills AI-native data teams should build around it.