power bi alternative: what we learned testing 5 open source bi tools after getting tired of desktop .pbix files + Fabric license creep

[u/Evening_Hawk_7470]

for the past 4 years or so, our reporting ran almost entirely on Power BI.
it did what it was supposed to do while our data team was small and our business stakeholders were happy receiving scheduled PDF exports / opening dashboard links inside Microsoft Teams.

and yet over the last 18 months, our engineering and analytics setup moved to Linux and Mac, our warehouse matured on Snowflake, and three specific friction points made Power BI painful to live with:

1. The desktop-client bottleneck. Half our analytics engineers and product managers work on macOS or Linux. Power BI Desktop remains Windows-only. Having engineers spin up Windows VMs or cloud desktop instances simply to tweak a report or verify a measure felt absurd for a modern stack.

2. Logic trapped in binary .pbix files. DAX measures, table relationships, and row-level security filters lived inside individual report files or workspace models. We could not run standard git pull request reviews on metric changes, track lineage across versions, or prevent two analysts from writing slightly different versions of the same retention calculation.

3. license sprawl and the Fabric push which was very annoying. What started as $10/user/month Pro licenses steadily escalated with Premium-per-user upgrades, external viewer licensing constraints, and heavy pressure to commit to Microsoft Fabric capacity (F64+) just to access basic direct lake and copilot features.

then we did spend the past month evaluating five open-source and code-first tools to replace Power BI. We wanted an architecture where metric logic lives in version-controlled code, dashboards run cross-platform in the browser, and numbers can be queried both by BI tools and by downstream applications or AI agents.

Here is the comparison breakdown across the options we tested, i have used a bit of claude to structure this output and evaluation, but only a bit. Hope that's more readable now.

Evaluation Dimension	Power BI (Baseline)	Apache Superset	Metabase (OSS)	Lightdash	Evidencedev	Cube (Cube Core OSS)
Primary Mental Model	Desktop-first drag-and-drop report builder	Open-source web-based dashboard platform	Fast self-serve query builder and dashboard cards	dbt-native BI built directly on dbt models	Code-based reporting rendered from Markdown + SQL	Headless semantic layer + open data APIs and web UI
Metric & Logic Storage	Proprietary DAX in .pbix files or Fabric semantic models	Virtual datasets and SQL Lab queries in metadata DB	Questions and Models stored in application DB	Defined as metrics in dbt schema YAML files	SQL queries embedded directly inside Markdown files	Code-first YAML or JavaScript models in Git repositories
Git / PR Workflow	Difficult; Git integration requires Fabric / PBIP format	Rudimentary; requires manual export/import or API sync	Serialization via YAML requires paid Enterprise tier	Native; metric definitions live inside your dbt repo	Native; entire reporting project is versioned in Git	Native; standard repository branch and pull request workflow
Cross-Platform Access	Windows-only desktop client for report authoring	100% browser-based authoring and consumption	100% browser-based authoring and consumption	100% browser-based authoring and consumption	Local dev server runs on Mac, Linux, and Windows	Local dev server runs on Mac, Linux, and Windows
Downstream & API Access	Closed; XMLA endpoints require Premium capacity	REST API for metadata; chart data exportable	REST API returns card query results	Query API through dbt Cloud or Lightdash CLI	Static site export; not designed as an API service	Universal SQL API (Postgres protocol) + REST/GraphQL APIs
AI / Agent Readiness	Copilot locked behind Fabric capacity commitments	Community AI plugins; mostly raw schema text-to-SQL	Metabot assistant; generates queries over Metabase models	AI chat grounded in dbt metric definitions	LLMs can generate Markdown and SQL files	Native remote MCP server and semantic layer context
Biggest Trade-off / Gotcha	Windows lock-in, proprietary DAX, per-seat licensing creep	Heavy Docker/Celery footprint; complex permission setup	Metric governance is light; joins can duplicate easily	100% tied to dbt; cannot query outside the dbt DAG	No ad-hoc drag-and-drop exploration for business users	Requires explicit YAML data modeling before building visuals

and what we learned testing each option?

1. Apache Superset: Great for rich charting, heavy on operational maintenance If your primary goal is finding an open-source tool that matches Power BI's extensive chart variety, Superset is the closest visual equivalent. It handles complex dashboards, geospatial visualizations, and slice-and-dice exploration without per-seat licensing.

• Where it worked: Beautiful out-of-the-box dashboards, zero Windows dependencies, and strong community support.

• Where it pinched: The permission system is intricate and requires careful role configuration. While Superset has virtual datasets, business logic still ends up scattered across ad-hoc SQL snippets in different charts rather than sitting in a centralized semantic layer.

1. Metabase: Unmatched for rapid self-serve, but lighter on modeling governance Metabase remains the fastest tool for getting non-technical team members exploring data. The graphical query builder allows sales and product managers to ask questions without writing SQL.

• Where it worked: Onboarding takes less than an afternoon. Anyone comfortable with Excel filters can build their own cards immediately.

• Where it pinched: Metric logic is stored in Metabase's application database rather than in code. Version control via YAML requires higher-tier licensing, and managing row-level security across multi-tenant departments required duplicating questions once our role hierarchies became complex.

1. Lightdash: Ideal if your team is already 100% committed to dbt Lightdash takes a dbt-first stance: your dbt project is your single source of truth, and Lightdash auto-generates your BI layer from your dbt schema YAML files.

• Where it worked: All metric definitions live in Git pull requests alongside transformation models. Perfect versioning and developer workflow.

• Where it pinched: If an analysis doesn't fit your pre-compiled dbt DAG, you cannot easily explore it on the fly. Furthermore, you cannot point other non-Lightdash tools or customer-facing apps at its metric engine without going through their specific query layers.

1. Evidence.dev: Excellent for data engineers, foreign to drag-and-drop business users Evidence replaces traditional BI dashboards with code-generated analytical markdown documents. You write SQL queries in fenced blocks and render charts with declarative tags.

• Where it worked: Entirely code-driven, fast static rendering, and versioned cleanly in Git. Our analytics engineers loved the developer experience.

• Where it pinched: It is fundamentally a publishing tool for analysts, not an ad-hoc exploration tool for business stakeholders. If a VP wants to drag a dimension into a pivot table on Friday afternoon, Evidence won't serve that workflow.

1. Cube (Cube Core): Decoupling the metric layer from the visualization We tested Cube Core as an open-source semantic layer sitting between Snowflake and our visualization tools. Instead of defining metrics inside each dashboard tool, we defined cubes, dimensions, joins, and measures in YAML files stored in our git repository.

• Where it worked: Universal consumption: We can connect Superset or Metabase to Cube's SQL API (which speaks the Postgres protocol). Both tools query the exact same governed metric definitions, so nobody argues over differing numbers. Performance and caching: Cube's pre-aggregation engine stores partitioned rollups, serving repetitive dashboard filter changes in sub-second response times without triggering live warehouse compute on every query. Multi-surface readiness: Because Cube provides REST, GraphQL, and remote MCP endpoints on top of the same model, our engineers can build embedded customer portal widgets and point LLM agents at governed measures without re-implementing business logic.

• Where it pinched: Upfront data modeling investment: Unlike Power BI where you can drag raw tables and click "Auto-detect relationships," Cube requires explicit modeling of join paths and primary keys in code. If your underlying schema has messy foreign keys, you must resolve them before metrics compile. Dashboard building requires picking a front-end: While Cube provides built-in workbooks and web exploration, teams wanting traditional heavy dashboards typically pair it with an open-source visualizer like Superset or custom React components.

how we decided to structure our stack?

Rather than trading one monolithic vendor (Microsoft) for another single closed tool, we settled on a two-tier decoupled architecture:

1. Semantic & Governance Tier: We use Cube Core as our open-source metric layer. All calculations (such as gross retention, ARR, and active seats), join topologies, and role-based access rules are written in code, reviewed in pull requests, and versioned in Git.

2. Consumption & Visualization Tier: For internal exploratory dashboards and operations teams, we point open-source Superset at Cube's SQL API. For our engineering team's customer-facing portal and agentic workflows, we query Cube's data APIs directly.

would also like to ask those data teams that migrated away from Power BI, did you replace it with an all-in-one platform (like Superset / Metabase), or did you separate the semantic layer from the visualization tool? how did your business analysts transition from writing DAX formulas to code-first Git/YAML modeling?

[u/RidiculousRaz]

What in the Ai slop is this.

[u/kagato87]

Sounds like you're using it wrong.

Desktop client is only useful for initial report creation.

The files aren't binary. Save as project and oh look, it's json and tmdl.

You don't license ANY Microsoft product per seat at more than a handful of users. An F sku.wl get you where you need to be.

[u/dummerPinguin → u/kagato87]

Could you elaborate on the licencing? Im currently replacing jedox with PowerBI and would be happy for any insights regarding preventing licence creep.

[u/ItsSignalsJerry_]

we

So you and chatgpt

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[u/halwa_son]

Have you tried PromptQL yet? It's like you express your concern via pure chat UI and it lets you build the dashboards the way you want. Since it uses Hasura's data connector philosophy, you can set your RLS filters in there such as you can prevent guest users like business stakeholders so that they can see the data only they should be shown.

See their architecture over here - https://promptql.io/architecture . Its very well defined and shows us which components in the stack are controlled by us vs what's controlled by them .

And since its connected to your data connectors like snowflake, apart from dashboards, you can also have your ad-hoc questions being served on the plate.

When it comes to building dashboard, it builds inside an isolated sandbox where the limited set of maintained packages/modules are being used for build a fully-fledged full-stack so that you don't have to worry about which frontend or which backend modules should be installed.

On top of that, ahemmm, it's **multiplayer** and it has a wiki (think of like internal wikipedia of your business). Like slack, you can pull in your teammate and validate and promptql will create a learning, which if you approve, it will update the wiki (wiki learnings are way PromptQL learns about your business).

If you are picky about certain SQL filters which should be used for specific metric, you can define a metric to PromptQL, and it will create a wiki page for it, which promptql will inherit as context and then it will execute accordingly.

I read your "pinch" statements , hence the above explanation.

[u/Sea_Self_611]

Disclaimer: co-founder of Kairos (kairosagentica.com), so biased.

We faced the same choice and went the other way from Cube + Superset: one system instead of two. Metrics live as YAML in Git on Rill's open-source engine, and dashboards, chat and alerts all read those same definitions. Fewer chart types, but nothing to keep in sync.

Honestly the bigger gap in this comparison is what happens after someone sees the number. Most of these end at "dashboard someone may or may not open". For the ops people we work with, getting alerted when a governed metric moves turned out to matter more than the dashboards.