Dac
Define, validate, and serve version-controlled YAML or TSX dashboards from a single Go binary.
If your pipelines already live in Git, DAC is the most natural dashboards layer you can add — YAML or TSX in the repo, `dac validate` in CI, and a semantic layer that stops metric drift across widgets. The 21 chart types, static `dac build` export, and first-class AI agent support via `dac skills install` cover real gaps Looker and Metabase leave open. The catch is that it assumes Bruin connections and SQL, so teams chasing a drag-and-drop builder should look elsewhere.
Verified 5d ago · liveness 77/100 · cite: rightaichoice.com/tools/dac
- Data engineers who want dashboards code-reviewed in pull requests and gated in CI
- Teams already running Bruin pipelines that want dashboards from the same .bruin.yml connections
- Analysts willing to author YAML or TSX for reproducible, version-controlled dashboards
- Anyone shipping public or client-facing dashboards as static HTML to S3 or GitHub Pages
- Stakeholders expecting a drag-and-drop GUI builder — DAC has no visual editor
- Teams with no SQL warehouse or Bruin connections to point DAC at
- Anyone needing real-time or streaming push dashboards — DAC runs query-on-request
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Skip DAC if you need a drag-and-drop visual dashboard builder for non-technical stakeholders, or if your team is not on Bruin connections — adoption there means an integration project before your first chart renders.
DAC itself is free and open source, but every dashboard depends on Bruin connections, so the compute behind your queries is billed through Bruin Cloud at $0.01 per credit — a 1-minute SQL run at default size is about 4
DAC is free and open source, so the real pricing decision is Bruin Cloud, not the dashboard tool. The Team plan is pay-as-you-go with no seats — $0.01 per credit and $100 in credits plus 50 AI tasks to start — which fits small data teams running modest pipeline volume. Scale is a committed-credit contract with volume discounts for production teams that need SSO, audit logs, and an SLA. Self-hosted open source undercuts hosted BI like Tableau per-seat licensing, but heavy Python or ingestion
In short
Dac — Define, validate, and serve version-controlled YAML or TSX dashboards from a single Go binary. Best for Data engineers who want dashboards code-reviewed in pull requests and gated in CI, Teams already running Bruin pipelines that want dashboards from the same .bruin.yml connections, Analysts willing to author YAML or TSX for reproducible, version-controlled dashboards. Free to start; paid plans from $0.01.
What's new in Dac
Checked 5 days agoAcross the latest 10 updates: 2 changelog entries and 8 community discussions.
How to Keep an AI Context Layer From Going Stale
Bruin blog outlines four feedback loops — local review, CI, scheduled audits and channel feedback — to keep an AI context layer current.
How to Build a Data Platform Without a Large Data Team
Bruin post lays out a data platform architecture for small teams, with a reference stack using the open-source Bruin CLI or dbt Core.
How to Connect an AI Agent to QuickBooks
Bruin guide covers connecting Claude Code, Codex or Cursor to QuickBooks Online for finance queries and approved actions without top-tier QuickBooks.
100 Analytics Engineering Questions for 2026, Answered
Bruin publishes short answers to 100 analytics engineering questions covering SQL, modeling, testing, semantic layers, Git, AI agents and careers.
ChatGPT Dots, Meta Muse and the ChatGPT Data Agent Compared on Company Data
Bruin compares OpenAI Dots and ChatGPT Data agent plus Meta Muse against an AI data analyst that owns the pipeline underneath the answer.
Snowflake ETL Tools: How 52,771 Companies Load Data
Bruin analysis of 52,771 Snowflake companies finds Fivetran leading at 12.7%, followed by ADF, Informatica, AWS Glue, Airbyte and Matillion.
How to Give AI Agents Context About Your Company Data
Bruin covers context engineering for data agents via RAG, MCP, a semantic layer and a data catalog, set up with Bruin CLI, dbt, Cube, Atlan or DataHub.
How to Onboard an AI Data Agent Like a New Hire
Bruin details a four-phase onboarding plan — orientation, onboard, internship, hire — for giving an AI data agent graduated access.
Bruin 2026 W39: Dashboard analysis, Cloud login, ClickHouse SCD2
Weekly release adds dashboard analysis, Cloud login, ClickHouse SCD2 materializations, reverse ETL destinations and pipeline workflow improvements.
Bruin 2026 W37: Partitioned backfills, read-only CLI connections
Release adds partitioned backfills and read-only connections in the CLI, dashboard folders and notification rules in Cloud, and CDC into BigQuery and Snowflake.
What people actually say about Dac — is it worth it?
We scanned public community sources for Dac on Jul 3, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Dac? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- YAML dashboards for declarative definitions with no code
- TSX dashboards with loops, variables, and conditionals for dynamic layouts
- 21 chart types including sankey, waterfall, XMR, candlestick, and treemap
- Metric, table, text, image, and divider widgets alongside charts
- Semantic layer in semantic/ for reusable metrics and dimensions with generated SQL
- Interactive filters: date pickers, numeric inputs, dropdowns, multiselects, search
- Jinja templating injects filter values into SQL and re-runs only affected widgets
- Live reload on file save — no restart or rebuild
- Static export to self-contained HTML via dac build with results baked in
- Validation and linting in CI via dac validate and dac check
- Data export: chart and table data as CSV, dashboard and widget views as PNG/PDF
- Google Slides export rendering dashboards as slide decks with baked-in data
- Single Go binary with an embedded React frontend, runs locally or on a server
- AI agent support via dac skills install for Claude Code, Codex, or OpenCode
- Connections to 30+ platforms via Bruin connections in .bruin.yml
About Dac
DAC (Dashboard-as-Code) from the Bruin team lets you define data dashboards as plain .yml or .dashboard.tsx files in your repo, validate them, and serve them locally. You write YAML for declarative layouts, or TSX when you need loops, variables, and conditionals — looping over a list of tables to generate dozens of similar views from a single definition. `dac serve` runs the dashboard at localhost:8321 against a connection configured in .bruin.yml, rendered through an embedded React frontend that ships in one Go binary. It ships 21 chart types — line, bar, area, pie, scatter, bubble, combo, histogram, boxplot, funnel, sankey, heatmap, calendar, sparkline, waterfall, XMR, dumbbell, gauge, treemap, radar, and candlestick — alongside metrics, tables, text, images, and dividers. A semantic layer defined once in semantic/ lets any widget reference reusable metrics and dimensions, with DAC generating the SQL. Interactive filters (date pickers, numeric inputs, dropdowns, multiselects, search) inject into SQL through Jinja templating and re-run only affected widgets in place. Live reload shows edits on save with no restart, while `dac validate` and `dac check` catch broken queries, missing columns, and schema violations in CI. For shipping, `dac build` produces self-contained HTML with query results baked in, deployable to S3 or GitHub Pages without a runtime server. Chart and table data export as CSV, dashboard and widget views export as PNG/PDF, and dashboards render as Google Slides decks. DAC reaches Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, ClickHouse, DuckDB, Amazon S3, MongoDB, Elasticsearch, CrateDB, CSV files, and Google Analytics 4 through Bruin connections. `dac skills install` lets Claude Code, Codex, or OpenCode build and validate dashboards directly. It's built for data engineers and analysts who already keep pipelines and models in version control. If you want a hosted GUI builder, this isn't the tool — dashboards here are reviewed in
Behind the Verdict
Where DAC bites hardest is drift. Looker, Metabase, Tableau and Mode all keep dashboards as rows in a hosted service — reviewed by clicking, not diffing. DAC flips that: the source file is the source of truth, every change shows up in git diff, and the same file reproduces the same dashboard on every machine. If your pipelines and models are already version-controlled, this is the dashboards layer that matches how you actually ship. Pick DAC when you have data engineers reviewing dashboards via PR, when you want metrics defined once in semantic/ and reused everywhere, or when you need static HTML output you can drop on S3 or GitHub Pages with no runtime. The single Go binary matters too — nothing to operate, nothing to keep patched. If you're wiring AI agents into your data workflow, `dac skills install` hands Claude Code, Codex, or OpenCode a way to build and validate dashboards against your connections. Pass on DAC when nobody on the team writes YAML or TSX, or when stakeholders expect to drag widgets onto a canvas. There's no GUI builder here, and no visual editor is coming per the docs. Query-on-request is the model — this isn't a real-time streaming dashboard, so if you need sub-second pushes from Kafka or similar, look elsewhere. The dependency on Bruin connections is the migration cost. If your stack already runs Bruin pipelines, `dac serve` and `dac build` slot in with no extra setup — .bruin.yml is right there. If you don't, you're adopting Bruin to use DAC, which is a bigger decision than picking a dashboard tool. That's not a flaw, but it's honest to call it out before someone signs off. On pricing, the open-source engine is free forever and self-hostable, so you can run DAC and Bruin CLI on your own CI at $0. Bruin Cloud's Team plan is pay-as-you-go at
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Real-world workflow fit
Concrete scenarios for the personas Dac actually fits — and what changes day-one when you adopt it.
Adds a `dashboards/sales.yml` that queries the warehouse connection already in .bruin.yml, defines a revenue metric in semantic/, and runs `dac serve` to check layout at localhost:8321.
Outcome: Dashboard is committed alongside the pipeline, reviewed in the PR like any other code, and `dac validate` blocks a missing column before merge.
Author a YAML dashboard with a date-picker filter that injects via Jinja, then run `dac build` to produce self-contained HTML with query results baked in.
Outcome: A static file lands on S3 or GitHub Pages with no runtime server, and the same source file reproduces it every week.
Runs `dac skills install` so Claude Code or Codex can author dashboards, then asks the agent to build a campaign performance view using the semantic layer.
Outcome: The agent generates and validates the dashboard file itself, and the engineer reviews the diff instead of clicking through a UI.
Use Cases
- Create a sales dashboard with revenue over time and top customers using YAML.
- Build a marketing dashboard with campaign performance metrics using the semantic layer.
- Generate a static weekly report as self-contained HTML for email distribution.
- Empower an AI agent to auto-generate dashboards from natural language prompts.
- Version-control your company's KPI dashboards alongside your data pipelines.
- Use TSX to loop over a list of tables to generate dozens of similar views from one definition.
- Export a dashboard as a Google Slides deck for a recurring business review.
- Run dac validate in CI to block broken queries and missing columns before merge.
Limitations
- DAC requires an existing Bruin connection to a supported database (Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and more via Bruin) — it does not include its own data source, so teams not already on Bruin inherit an integration project first.
- There is no GUI or visual dashboard builder; definitions are authored as YAML or TSX files.
- It is designed to be driven by both humans and AI agents (Claude Code, Codex, OpenCode) rather than point-and-click business users.
as of 2026-10-03
Verification history
We have re-verified Dac 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Dac tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/forever
Team
$0.01/credit, pay-as-you-go
Ideal for
Growing data teams in production that want managed scheduling, governance, and AI analytics without seat-based pricing.
What this tier adds
Adds managed pipeline scheduling, historical backfills, visual lineage, asset catalog, AI Data Analyst across 8 channels, and unlimited members.
Scale
Custom
Where the pricing makes sense
The company stage and team size where Dac's pricing actually pencils out — and where peers do it cheaper.
DAC is free and open source, so the real pricing decision is Bruin Cloud, not the dashboard tool. The Team plan is pay-as-you-go with no seats — $0.01 per credit and $100 in credits plus 50 AI tasks to start — which fits small data teams running modest pipeline volume. Scale is a committed-credit contract with volume discounts for production teams that need SSO, audit logs, and an SLA. Self-hosted open source undercuts hosted BI like Tableau per-seat licensing, but heavy Python or ingestion
Setup time & first value
How long it actually takes to get something useful out of Dac — broken out by persona, not the marketing-page minute.
A Bruin user with an existing .bruin.yml can have a first dashboard rendering in roughly 15-30 minutes: install the binary, run the quickstart, point at a connection, `dac serve`. Teams without Bruin connections should budget days rather than hours, since the connection has to exist before DAC can query anything. Adding the semantic layer and CI validation (dac validate and dac check) is a second
Switching to or from Dac
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Metabase: export existing question SQL and rewrite each as a YAML widget or TSX component referencing your Bruin connection.
- →From Looker: lift the SQL from LookML-derived queries into YAML widgets, and move reusable metrics into semantic/ to approximate LookML's modeling layer.
- →From Tableau: extract the underlying custom SQL from each workbook and rebuild the chart spec in DAC's 21 chart types, accepting that calculated-field logic must be re-expressed in SQL.
- →From a hand-rolled dashboard script: replace the templating and charting layer with a YAML definition and `dac build` for static output.
- →From Mode: move report SQL into YAML or TSX widgets and use Google Slides export to approximate Mode's scheduled report delivery.
- ↗To Metabase: since DAC dashboards are plain YAML/TSX, extract each widget's SQL and recreate the question in Metabase's GUI for non-technical stakeholders.
- ↗To Looker: promote the semantic/ metric definitions into LookML measures and rebuild widgets as Looker tiles.
- ↗To a static site pipeline: `dac build` output is self-contained HTML, so moving to any static host requires no rewrite — only a change of deployment target.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Dac”, and we withheld 6: 6 could not be judged, because “Dac” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Dac.
Official links
Tools that pair well with Dac
Common stack mates teams adopt alongside Dac, with the specific reason each pairing earns its keep.
Formula Bot
Better Analyst (formerly Formula Bot) turns plain-English questions into charts, dashboards, and automated data workflows.
Uber Freight AI
Uber Freight Insights AI: logistics AI chat, KPI dashboards and ROI-ranked supply chain recommendations for shippers.
Domo
Domo prepares governed data for AI agents, then layers BI dashboards, workflows, and embedded analytics on top of it.
Featured Head-to-Head Comparisons
Dac vs Nectar Energy
For commercial real estate owners and facility managers targeting carbon reduction and automated HVAC/lighting control, Nectar Energy is the clear choice with its BMS integration and new ESG reporting. But if your team is data engineering-focused and needs code-reviewed, version-controlled dashboards with AI agent compatibility, DAC offers a unique, developer-first approach at a freemium price point. Your decision hinges on whether your problem is physical building optimization or dashboard creation from SQL data.
Dac vs Geologicai
GeologicAI and Dac serve entirely different domains, so the choice depends on your industry. If you're in critical minerals mining and need fast, accurate core analysis with AI, GeologicAI is the clear winner. If you're a data engineer or analyst needing code-first, version-controlled dashboards, Dac offers a modern, free-to-start solution. They are not directly comparable; pick based on your sector.
Dac vs Screenplayiq
These tools serve entirely different domains. Choose Dac if you are a data engineer needing code-reviewed, version-controlled dashboards from SQL databases. Choose ScreenplayIQ if you are a screenwriter or producer wanting AI-driven script analysis with box office predictions. There's no overlap.
Alternatives to Dac
View allFormula Bot
Better Analyst (formerly Formula Bot) turns plain-English questions into charts, dashboards, and automated data workflows.
Uber Freight AI
Uber Freight Insights AI: logistics AI chat, KPI dashboards and ROI-ranked supply chain recommendations for shippers.
Frequently Asked Questions
Best-of guides
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