Dac
FreemiumDefine, validate, and serve interactive dashboards as code using YAML or TSX.
By Tanmay Verma, Founder · Last verified 06 Jul 2026
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Added 6d ago
77/100Safe BetIn short
Dac — Define, validate, and serve interactive dashboards as code using 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.
Is Dac actually worth it?
LiveSee what real users actually say. We scan live discussions, reviews and complaints across the web and hand you an honest verdict — in under a minute.
3 free scans · no card needed · downloadable report
Editorial Verdict
Best for
Data engineers who want code-reviewed dashboards in CI/CDTeams using Bruin for end-to-end data pipelines and dashboardsAnalysts seeking reproducible, version-controlled dashboardsAI agents building dashboards programmaticallyTeams requiring static HTML export for public dashboards
Not ideal for
Users needing a no-code GUI builderTeams without a SQL-based data warehouseAnyone requiring real-time streaming dashboardsOrganizations not using Bruin connections
DAC fits teams already using Bruin who want code-reviewed, reproducible dashboards. Its semantic layer and static export are genuinely useful; the lack of real-time streaming is a hard limit. If you need a GUI builder or don't have a SQL warehouse, look at Metabase or Tableau instead.
Skip Dac if Skip DAC if you need a visual GUI builder, don't use Bruin connections, or don't have a SQL-based data warehouse.
Compare with: Dac vs Formula Bot, Dac vs Querio, Dac vs Cropin
Last verified: July 2026
What's new in Dac
Checked 6 days agoAcross the latest 10 updates: 6 feature updates, 1 launch and 3 news mentions.
FeatureBlog·8 days agoNewest
Pentaho vs Bruin: A Modern Alternative for Data Pipelines
Comparison of Pentaho and Bruin for teams evaluating PDI, Kettle, and legacy ETL alternatives, highlighting governed pipelines and AI analytics.
LaunchBlog·8 days ago
Migrating from Pentaho Data Integration to Bruin
Migration guide for moving Pentaho PDI/Kettle jobs to Bruin, covering ingestr, SQL/Python assets, quality checks, DAC dashboards, MCP, and AI analytics.
NewsBlog·10 days ago
Bruin Project Competition: over 70 end-to-end data pipelines from the Data Engineering Zoomcamp community
Recap of the first Bruin Project Competition with 70+ pipelines built by the DataTalksClub Data Engineering Zoomcamp community.
FeatureBlog·10 days ago
Agentic Salesforce to Snowflake ELT: From One Prompt to a Governed Pipeline
Demonstrates building a Salesforce to Snowflake ELT pipeline using Bruin CLI, MCP, Cloud, and agent skills across bronze-silver-gold layers.
FeatureBlog·17 days ago
How to Build a Salesforce to Snowflake Pipeline with Bruin
Guide for ingesting Salesforce into Snowflake, modeling bronze-silver-gold layers, and using Bruin agents for dashboards, Slack, and self-healing.
NewsBlog·18 days ago
Learning AI Programming, Agentic Data Engineering, and AI Data Analysis
Curated list of open/paid courses for AI programming, agentic data engineering, and AI data analysis by career path and experience level.
FeatureBlog·21 days ago
dlt Alternatives: Bruin, Airbyte, Sling, Meltano, and Fivetran Compared
Comparison of dlt alternatives for data ingestion, including a MongoDB-to-Postgres benchmark across Bruin/ingestr, Airbyte, Sling, Meltano, and Fivetran.
NewsBlog·24 days ago
What Is a Semantic Layer?
Explains semantic layers, metric drift across dashboards, and how Bruin CLI/DAC reuse metrics, dimensions, filters, and segments as SQL.
FeatureBlog·24 days ago
Best Semantic Layer Tools in 2026: dbt, Cube, Looker, Power BI, Lightdash, Bruin
Comparison of semantic layer tools including dbt, Cube, Looker, Power BI, Tableau, AtScale, Lightdash, and Bruin CLI/DAC across governance and AI-agent fit.
FeatureBlog·24 days ago
BigQuery TVFs for AI Agents: A Lightweight Semantic Layer Pattern
Describes using BigQuery table-valued functions as a lightweight semantic layer for AI data agents to execute governed queries.
What independent users actually report about Dac
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).
10% positive90% critical
Recurring strengths
- +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
Recurring frustrations
- −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
Patterns worth knowing
Tool is unknown — no relevant community discussion exists
Seen on Hacker News, Lemmy
Crashing issues on App Store
Seen on App Store
Learning curve
advancedProductive in ~Days of setup
Hidden costs people mention
- • Requires Bruin infrastructure which may incur separate costs
Viability Score
77/100
Safe BetHow likely is Dac to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Define dashboards in YAML or TSX
- 21 chart types including candlestick and sankey
- Built-in semantic layer for reusable metrics and dimensions
- Interactive filters (date pickers, dropdowns, multiselects, search)
- Jinja templating for SQL injection
- Live reload on file save
- Static export to self-contained HTML
- Validate and lint dashboards (dac validate, dac check)
- Data export: CSV, PNG, PDF
- Google Slides export as slide decks
- Single Go binary deployment
- Designed for AI agent usage (Claude Code, Codex, OpenCode)
- Version-controlled source files as single source of truth
- Supports major databases via Bruin connections
- TSX supports loops, conditionals, and load-time queries
About Dac
FreemiumIntermediateNo APICLI · Desktop
DAC (Dashboard-as-Code) is a developer-first tool for defining, validating, and serving interactive data dashboards from version-controlled source files. It targets data engineers, analysts, and AI agents who want reviewable, reproducible dashboard definitions without a GUI builder. Dashboards are defined in YAML or TSX, executed against your existing Bruin connections, and rendered through an embedded React frontend that ships as a single Go binary. DAC supports 21 chart types (line, bar, area, pie, scatter, bubble, combo, histogram, boxplot, funnel, sankey, heatmap, calendar, sparkline, waterfall, XMR, dumbbell, gauge, treemap, radar, candlestick) plus metrics, tables, text, images, and dividers. It includes a built-in semantic layer where you define metrics and dimensions once in a semantic/ directory and reference them from any widget; DAC generates the SQL. Interactive filters (date pickers, dropdowns, multiselects, search inputs) inject into SQL via Jinja templating and re-run affected widgets in place. Live reload allows you to edit the file, save, and see changes immediately without restart. Static export via dac build produces self-contained HTML with query results baked in, deployable to S3 or GitHub Pages. Data export to CSV, PNG, PDF, and Google Slides export are also supported. Validation commands (dac validate, dac check) catch broken queries before production. DAC supports all major databases via Bruin connections: Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and more. Unlike Looker, Metabase, or Tableau — which store dashboards in a database and require a GUI — DAC treats the dashboard file as the single source of truth, enabling git diff, PR review, and CI validation. It is designed for AI agent usage (Claude Code, Codex, OpenCode). The trade-off: no visual editor, so SQL and YAML/TSX skills are required.
Behind the Verdict
DAC is a purpose-built tool for a specific workflow: version-controlled, code-reviewed dashboards backed by Bruin connections. It shines when your team already uses Bruin for data pipelines and wants dashboards that live in the same repo. The semantic layer is a real differentiator — define metrics once and reuse them across dashboards, and DAC generates the SQL. The TSX format allows loops and conditionals, making it easy to generate dozens of similar views from one definition. Strengths: full code review workflow (git diff, PRs, CI validation), static export for hosting anywhere, live reload during development, AI agent support (Claude Code, Codex, OpenCode), semantic layer, 21 chart types, Google Slides export. Weaknesses: no GUI builder; requires Bruin connections (so not standalone); no streaming or real-time data; Team/Enterprise pricing requires contacting sales; learning curve for YAML/TSX. Best for data engineers and analysts who prefer code over clicking. Not for non-technical users or teams without a SQL data warehouse.
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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.
Data engineer
You want to create a sales dashboard for the business team. You write a YAML file describing widgets, queries, and filters, commit it to your repo, and run dac serve to preview locally. After review via pull request, you run dac build and deploy the static HTML to S3.
Outcome: The dashboard is live, reproducible, and version-controlled. Changes go through code review.
AI agent
You ask an AI agent (e.g., Claude Code) to 'build a dashboard showing monthly revenue and top products.' The agent uses the DAC skill to generate a .dashboard.tsx file, validates it with dac validate, and serves it at localhost:8321.
Outcome: The dashboard is built from natural language in minutes, with validated SQL.
Analyst
You need to generate a weekly static report as HTML for email distribution. You write a TSX dashboard with metrics and charts, run dac build, and get a self-contained HTML file.
Outcome: The report is generated automatically, can be hosted on GitHub Pages, and is consistent every week.
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.
Models Under the Hood
Bruin connection (database-specific)
as of 2026-07-06
Limitations
- DAC requires a Bruin connection to a supported database; it does not include its own data source.
- Dashboard functionality is limited to what can be expressed in YAML/TSX.
- Real-time streaming is not supported.
- Pricing for Team/Enterprise tiers requires contacting sales.
as of 2026-07-06
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
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
Individual developers or small teams already using Bruin who want unlimited dashboards with all chart types and semantic layer at no cost.
What this tier adds
Free tier includes all features (unlimited dashboards, all chart types, semantic layer, static export, local serve) with no user limits — no payment needed.
Team
Contact
Ideal for
Teams that need collaboration features, role-based access, and priority support beyond the free tier.
What this tier adds
Adds collaboration features, role-based access, and priority support over the Free plan.
Enterprise
Contact
Ideal for
Large organizations requiring SSO/SAML, dedicated support, and custom integrations.
What this tier adds
Adds SSO/SAML, dedicated support, and custom integrations over the Team plan.
Integrations
PostgresMySQLSnowflakeBigQueryRedshiftDatabricksClickHouseDuckDBAmazon S3MongoDBElasticsearchCrateDBCSV FilesGoogle Analytics 4AWS Athena
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's free tier is generous (unlimited dashboards, all chart types, semantic layer) and best for individual developers or small teams already using Bruin. Compared to Looker or Tableau, which charge per user or per dashboard, DAC is much cheaper. Team and Enterprise tiers require contacting sales, so there's no transparent pricing for larger teams.
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.
If you already have Bruin connections configured, install DAC via dac skills install and run dac serve on an existing dashboard file — first dashboard appears in under 10 minutes. Writing a new dashboard from scratch takes 30-60 minutes depending on complexity.
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.
Migrating in
- →From Metabase/Tableau/Looker: Export dashboards as SQL queries and YAML definitions; manually recreate widgets in DAC since there's no import tool.
Migrating out
- ↗To Metabase/Tableau/Looker: Export dashboard SQL queries and rebuild dashboards in the target tool; no automated export exists.
Resources & Guides
Frequently Asked Questions
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Common stack mates teams adopt alongside Dac, with the specific reason each pairing earns its keep.
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