We're a small team. Our current setup is a mess of spreadsheets, Looker Studio, and a Power BI dashboard someone built and left. It doesn't scale.
Need a single platform we can all use for internal reporting and client-facing dashboards. Budget is a factor but not the only one. Must handle large datasets without constant performance tweaking and have a sane learning curve.
Priorities:
- Centralized semantic layer/model. One person defines metrics, everyone uses them.
- Row-level security that actually works for embedding.
- Direct query to BigQuery and Snowflake without crippling cost.
- Total cost under $25k/year.
Considering Metabase, Superset, Tableau, and Power BI Pro. The open-source options look good on paper but I'm concerned about maintenance overhead. Tableau's cost and complexity seem overkill. Power BI's licensing is confusing.
What actually works at this scale without needing a dedicated admin?
Power BI Pro's licensing isn't confusing, it's just expensive. You'll blow past your budget fast with five users needing publish-to-web for client dashboards. Their embedded offering is a separate, costly SKU.
Your main issue is the semantic layer requirement. Metabase's "data model" is weak. Superset's semantic layer is better but you're right about the admin tax.
Consider Lightdash. It's open source, built on a dbt core, so your metric definitions live in git. It ticks your boxes for centralized metrics and RLS for embedding. Hosted option fits your budget and avoids the maintenance headache you're worried about.
You won't find a platform that does direct query to big datasets without performance tweaks. That's a pipe dream. You'll be caching regardless of vendor claims.
—Skeptic
Good point about Lightdash's dbt-core foundation. That git-based metric layer is a real advantage for a small team, especially for version control and peer review.
One caveat I'd add from a cost perspective: you mentioned "direct query to BigQuery without crippling cost." Lightdash (or any direct query tool) shifts the cost burden almost entirely to your data warehouse. With five active analysts, your BigQuery/Snowflake bill can easily spike unless someone actively manages query patterns and implements caching.
The $25k budget for the BI tool might be safe, but you could see a $10-20k increase in your cloud data platform costs if you're not careful. It's often the hidden trade-off.
You're right about Power BI's embedded SKU being a separate cost trap, and it's a critical point for client-facing work. The shift from Pro to Premium per capacity for embedding is where most budgets break.
I'll add a nuance to your Lightdash suggestion regarding the semantic layer. While its integration with dbt is excellent for metric consistency, it does lock you into the dbt ecosystem. For a team new to it, that's an additional learning curve and a paradigm shift in how you manage transformations. It's powerful, but not a trivial adoption.
Your final point on caching is absolutely correct. Any platform promising seamless direct query on large datasets is overselling. The real evaluation should be on how transparent and manageable the caching layers are.
Method over hype
> direct query to BigQuery and Snowflake without crippling cost
This was my biggest worry too, coming from Prometheus where query cost is flat. The cloud warehouse bill can sneak up on you fast with a five-person team clicking around. A tool with good, easy caching settings is crucial. It's less about the BI platform cost and more about what it does to your data platform bill.
I've been testing Metabase for a side project. Their semantic layer *is* light, but for a small team, maybe that's okay? You define the metric in a saved question and everyone uses that as a starting point. It's not as clean as dbt + Lightdash, but there's less to learn upfront. The RLS for embedding seems solid from what I've read.
Would you be open to a managed open-source option? That hits the budget and kills the maintenance worry.