Power BI, Looker Studio, or a custom Streamlit app on Snowflake? Which dashboards fit which business — and when a BI licence beats a custom build.
Once your data is in Snowflake, the next question is always the same: what do we put on top of it? Dashboards on Snowflake come in three practical flavours for a small or mid-size business — Power BI, Looker Studio, or a custom Streamlit app — and the right answer depends less on the tools than on how your team actually consumes numbers. We build all three for clients, we have no licence to sell you, and the honest summary is: most businesses should start with a BI tool, and a meaningful minority should not stop there.
Power BI: the default for Microsoft shops
If your company lives in Microsoft 365 — Excel, Teams, SharePoint — Power BI is usually the path of least resistance. It connects to Snowflake natively, its modelling layer (DAX) can express serious business logic, and distribution through the Microsoft stack means adoption is rarely a fight. The costs are per-user licensing that scales with headcount, and a skills reality: past the basics, Power BI is a genuine specialty, and half-built models produce confidently wrong numbers. It's the strongest choice for organizations that want governed self-serve — analysts building, everyone else slicing.
Looker Studio: the free, good-enough tier
Looker Studio is free, browser-based, and familiar to anyone who has touched Google Analytics — it's the reporting layer we already deploy in most of our analytics & optimization engagements. Pointed at Snowflake, it produces clean, shareable dashboards at zero licence cost. Its limits are real: a thinner modelling layer than Power BI, fewer enterprise governance controls, and performance that demands well-prepared tables underneath (which also keeps your warehouse bill down — see what Snowflake costs a small business). For an SMB that needs solid KPI reporting without a per-seat bill, it's honestly enough more often than the industry admits.
One caution that applies to both BI tools equally: they are only as fast and as cheap as the Snowflake tables behind them. A dashboard that queries raw, unmodelled data will feel slow to users and keep your warehouse running — and billing — far more than it should.
Streamlit: when the dashboard needs to do something
BI tools display and slice. The moment the requirement includes acting — editing a forecast assumption, approving an exception, writing a decision back to the database, or logic too custom for a BI formula language — you've left dashboard territory and entered application territory. That's where a custom Streamlit app running inside Snowflake fits: Python-based, governed by warehouse permissions, no separate hosting, and buildable in weeks by our custom software development team. We've written a full plain-English guide to Streamlit apps on Snowflake; the short version is that it's the right tool when a BI dashboard keeps getting exported to Excel so someone can actually work with it.
If your team keeps exporting the dashboard to Excel, the dashboard was never the product. The spreadsheet was — and it wants to be an app.
How to choose: five questions
- Do users need to change or enter data, not just view it? Any yes → Streamlit (or custom software); BI tools are read-only by design.
- Is your organization Microsoft-first with existing Power BI skills or licences? → Power BI.
- Is the need standard KPIs on a budget of roughly zero? → Looker Studio.
- Will more than a handful of non-technical users self-serve their own questions? → a BI tool over a custom app, almost always.
- Is the 'dashboard' really one team's operational workflow in disguise? → Streamlit, scoped around that single workflow.
The combination most businesses actually end up with
In practice the mature setup is boring and effective: one BI tool for recurring reporting, plus one or two Streamlit apps for the workflows BI can't hold — all reading the same governed Snowflake tables, so there is exactly one version of the truth. What matters more than the tool choice is the layer underneath: clean, documented, tested tables with agreed definitions of revenue, customer, and margin. Every dashboard disaster we've been called in to fix — numbers that don't match finance, reports nobody trusts, three versions of the same KPI — was a data-modelling problem wearing a dashboard costume, and no amount of switching BI tools fixes it. If you're staring at Snowflake wondering which layer to build first, we'll give you a straight, fixed-price answer — including "use the free one" when that's the truth. Call us or use the contact form on our homepage.
References
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