Dac
Define, validate, and serve code-first dashboards with DAC as YAML or TSX.
DAC is a compelling code-first solution for teams already using Bruin. It makes dashboards reviewable, diffable, and deployable like code, with a robust semantic layer and support for 21 chart types. The catch: you must adopt Bruin connections, which locks you into their ecosystem. If you're on Bruin, it's a serious upgrade over UI-driven tools like Metabase or Tableau for version-controlled dashboards. Otherwise, weigh migration cost against the benefits. For AI-agent-driven dashboard generation, DAC's skill integration is ahead of the curve.
Verified 2d ago · liveness 77/100 · cite: rightaichoice.com/tools/dac
- Data engineers who want code-reviewed dashboards in CI/CD
- Teams using Bruin for end-to-end data pipelines and dashboards
- Analysts seeking reproducible, version-controlled dashboards
- AI agents building dashboards programmatically (Claude Code, Codex)
- Users needing a no-code GUI builder (no visual editor)
- Teams without a SQL-based data warehouse
- Anyone requiring real-time streaming dashboards (query-on-request only)
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Skip DAC if you need a no-code GUI builder or are not using Bruin connections, as DAC requires Bruin and has no visual editor.
Free tier is limited to community support; you may need to pay for team collaboration and priority support.
DAC offers a freemium model with a $0/mo free tier, but team and enterprise features require contacting sales, making pricing less transparent than fully self-serve tools like Metabase's open-source. For small teams already on Bruin, the free tier may suffice; larger enterprises may find the contact-sales model a hurdle compared to fixed-price competitors.
In short
Dac — Define, validate, and serve code-first dashboards with DAC as YAML or TSX. Best for Data engineers who want code-reviewed dashboards in CI/CD, Teams using Bruin for end-to-end data pipelines and dashboards, Analysts seeking reproducible, version-controlled dashboards. Free to use.
What's new in Dac
Checked 5 days agoAcross the latest 1 update: 1 feature update.
What people actually say about Dac — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
47 mentions across 3 sources (Hacker News, App Store, Lemmy) · researched Jul 3, 2026.
- +Version-controlled dashboards with YAML/TSX definitions
- +Built-in semantic layer for reusable metrics and dimensions
- +Live reload on file save for instant feedback
- +Static export to self-contained HTML for easy deployment
- +Supports 21 chart types and interactive filters
- −No community feedback available to verify claims
- −App Store reports of persistent crashing
- −Lacks visual editor — steep learning for non-devs
- −Requires Bruin dependency for database connections
- −Uncertain support quality and responsiveness
- • Requires Bruin infrastructure which may incur separate costs
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: September 2026
How we score →Key Features
- YAML dashboards
- TSX dashboards with loops and conditionals
- 21 chart types (line, bar, area, pie, scatter, bubble, combo, histogram, boxplot, funnel, sankey, heatmap, calendar, sparkline, waterfall, XMR, dumbbell, gauge, treemap, radar, candlestick)
- Metric and table widgets
- Semantic layer for reusable metrics and dimensions
- Interactive filters (date pickers, numeric inputs, dropdowns, multiselects, search)
- Jinja templating for SQL injection
- Live reload on file save
- Static export to self-contained HTML via dac build
- Validation and linting via dac validate and dac check
- Data export: CSV, PNG, PDF
- Google Slides export as slide decks
- Single Go binary deployment
- AI agent support via dac skills install (Claude Code, Codex, OpenCode)
- Support for major databases via Bruin connections
About Dac
DAC (Dashboard-as-Code) is a developer tool for building, validating, and serving data dashboards from version-controlled source files. You define dashboards in YAML or TSX, execute queries against your existing Bruin connections, and render them through an embedded React frontend that ships in a single Go binary. There's no GUI builder, no visual editor, and no separate dashboard service to operate—the source file is the source of truth, so every change shows up in git diff and pull requests actually work. It's built for data engineers, analysts, and AI agents who want reviewable, reproducible, and composable dashboards that deploy like code. DAC supports 21 chart types, including line, bar, area, pie, scatters, and candlestick, plus metrics, tables, text, images, and dividers. A built-in semantic layer lets you define metrics and dimensions once in the semantic/ directory and reference them from any widget—DAC generates the SQL. Interactive filters (date pickers, numeric inputs, dropdowns, multiselects, search inputs) inject into SQL via Jinja templating and re-run only the affected widgets in place. Live reload means you edit the file, save, and see the change instantly. Static export via dac build produces self-contained HTML with query results baked in, deployable to S3, GitHub Pages, or anywhere. Data export is available for chart/table data as CSV and dashboard/widget views as PNG/PDF. You can even export dashboards to Google Slides as slide decks. Validation is baked in: dac validate and dac check catch broken queries, missing columns, and schema violations before they reach production. DAC connects to all major databases through Bruin connections, including Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and more. It's also designed for heavy use by AI agents—install the DAC skill via dac skills install and have Claude Code, Codex, or OpenCode build and validate dashboards for you. Compared to hosted tools like Looker, Metabase, or Tableau, DAC treats dashboards as plain text: diffable, reproducible, composable, portable, and honest. There is no hidden state in a service somewhere—what you see in the file is what runs. If your data pipelines, models, and tests live in version control, your dashboards should too.
Behind the Verdict
DAC excels at turning dashboards into code. The YAML and TSX authoring formats give you declarative simplicity and programmatic power—loops, conditionals, and load-time queries in TSX let you generate dozens of similar views from a single definition. The semantic layer is a standout: define a metric once, reuse it everywhere, and let DAC generate the SQL. Validation via dac validate and dac check integrates into CI, catching broken queries and schema issues before they reach production. The biggest strength is the code-first philosophy. Every change is a git diff, PR reviews actually work, and dashboards are reproducible on any machine. Static export to self-contained HTML means you can host dashboards anywhere—S3, GitHub Pages—without a server. Live reload and interactive filters (date pickers, dropdowns, multiselects) inject into SQL via Jinja, re-running only affected widgets in place, which is great for exploration. However, DAC's dependency on Bruin is a notable constraint. You can't use DAC without Bruin connections, which means teams not already using Bruin face a migration cost. There's no GUI builder—if you want to click and drag, this isn't for you. Also, it's not real-time streaming; queries run on request, so it's not suited for live monitoring dashboards that need sub-second updates. Where DAC fits best: data engineering teams that already use Bruin for pipelines and want dashboards in the same repo, AI agents that need to generate dashboards programmatically, and teams that value version control and reproducibility. It's less ideal for business users who want self-serve analytics without writing code.
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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.
You need to add a KPI dashboard to your existing Bruin pipeline repo.
Outcome: Write a YAML dashboard in minutes, run dac serve to preview live, and commit to git for PR review.
You want to create a recurring weekly sales report without manual chart building.
Outcome: Define a dashboard with semantic layer metrics, use dac build to export static HTML, and schedule deployment to S3.
You're asked to generate a marketing dashboard from natural language.
Outcome: Install the DAC skill, let the agent generate YAML/TSX, validate with dac check, and serve at localhost:8321.
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.
Limitations
- DAC is a tool for defining, validating, and serving data dashboards from version-controlled source files, with dashboards defined in YAML or TSX and executed against existing Bruin connections.
- It renders through an embedded React frontend in a single Go binary and supports interactive filters and a semantic layer for reusable metrics.
- The tool is designed for use by both humans and AI agents, such as Claude Code, Codex, or OpenCode, and can be exported as static HTML.
- It does not include its own data source and requires a connection to a supported database via Bruin.
- Additionally, DAC lacks a GUI builder and does not support real-time streaming dashboards; queries are executed on request.
as of 2026-09-01
Verification history
We have re-verified Dac 7 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-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
- — 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 7 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.
Free
$0/mo
Ideal for
Solo developers and small teams exploring code-first dashboards with unlimited dashboards and community support.
What this tier adds
Starting tier with unlimited dashboards and community support, but no collaboration features or priority support.
Team
Contact
Ideal for
Growing teams that need collaboration features and priority support without enterprise security requirements.
What this tier adds
Adds collaboration features and priority support compared to the free tier.
Enterprise
Contact
Ideal for
Large organizations with advanced security needs, SSO, and dedicated support requirements.
What this tier adds
Adds advanced security, SSO, and dedicated support on top of Team features.
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 offers a freemium model with a $0/mo free tier, but team and enterprise features require contacting sales, making pricing less transparent than fully self-serve tools like Metabase's open-source. For small teams already on Bruin, the free tier may suffice; larger enterprises may find the contact-sales model a hurdle compared to fixed-price competitors.
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.
For a data engineer already on Bruin, you can install DAC, connect your warehouse, and have a first dashboard live in about 30 minutes. Analysts new to code-first dashboards may take 1-2 hours to learn YAML and queries. AI agents can be productive in minutes after installing the DAC skill.
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 Looker: You'll need to recreate dashboard definitions as YAML/TSX and set up Bruin connections, but you gain version control and code review.
- →From Metabase: Export your SQL queries and rebuild dashboards as YAML; you lose the GUI but gain reproducibility.
- →From Tableau: Recreate calculated fields and workbook visuals in DAC's semantic layer and YAML files.
- ↗To Metabase: Export your YAML definitions and rebuild dashboards in the GUI, but you lose version control.
- ↗To Looker: You'll need to port LookML definitions, which is complex; DAC doesn't provide direct migration tooling.
- ↗To a custom React app: Use DAC's static export or TSX to generate HTML, then embed in your own app.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Dac
Common stack mates teams adopt alongside Dac, with the specific reason each pairing earns its keep.
Obviously AI
No-code predictive AI for classification, regression, and time-series from tabular data
Northbeam
Marketing intelligence platform for DTC brands needing deterministic attribution, MMM+, and first-party data feeds to ad algorithms.
MindsDB
MindsHub turns plain-language tasks into finished apps, dashboards, and documents from your connected data.
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 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.
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.
Alternatives to Dac
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No-code predictive AI for classification, regression, and time-series from tabular data
Frequently Asked Questions
Best-of guides
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