Mode Analytics Review: SQL + Python BI for Data Teams
What it is: Mode is a collaborative analytics platform combining SQL, Python/R notebooks, and visual reporting in one place. Analysts explore in SQL, extend the results in a notebook, and publish the output as a shareable report — all without leaving the tool. Since 2023 it has been part of ThoughtSpot.
Quick answer: Mode Analytics is one of the best BI tools for SQL-first data teams: a fast SQL editor, Python and R notebooks running on the query results, and versioned, shareable reports in a single workflow. It is weaker for non-technical self-service and dashboard polish, and pricing is quote-based (roughly $200+/editor/month historically). Great analyst tool; not a company-wide BI standard.
Mode Analytics Review Verdict: Is It Worth It?
For analyst-heavy teams, yes. Mode's SQL-to-notebook-to-report pipeline is genuinely faster than stitching Jupyter, a BI tool, and Slack screenshots together, and version history plus a searchable query catalog make analyses reusable instead of disposable. The honest caveats: business users can't build much themselves, visualization polish trails Tableau, and since the ThoughtSpot acquisition you should confirm the current packaging and roadmap before signing a multi-year deal.
What Makes Mode's SQL + Python Notebooks Different?
In Mode, a Python or R notebook attaches directly to a SQL query's result set — the dataframe is just there, with no export, upload, or connection step. That means an analyst can run a regression, build a cohort curve, or apply forecasting on warehouse data seconds after querying it, then drop the output straight into a stakeholder-facing report. It's the tightest SQL-to-Python loop of any mainstream BI tool.
What It Does Best
Flexible workflow. Start with SQL exploration, add Python analysis, create visual reports. All in one notebook-style interface.
Version control built-in. Git-like versioning for reports. See who changed what and when. Roll back to previous versions.
Collaboration features. Comment on charts, share findings, schedule reports. A data catalog shows all queries across the organization so analyses get reused rather than rewritten.
Key Features
SQL editor: Fast editor with autocomplete, formatting, and query history
Python/R notebooks: Run Python and R directly on query results for advanced analytics
Report builder: Drag-and-drop visual reporting on SQL results, with white-label embedding
Version history: Git-like tracking of all changes
Data catalog: Search and discover queries across the organization
Visual Explorer: Point-and-click exploration layer for lightweight self-service
How Much Does Mode Cost? (Pricing)
Mode's pricing is quote-based. Historically there was a free Studio tier for individuals and a Business tier starting around $200 per editor per month, with viewer seats priced lower and Enterprise custom-priced. Since the ThoughtSpot acquisition, packaging has shifted toward sales-led quotes — budget for a mid-market BI contract, and confirm current tiers and any free plan directly with sales before planning a rollout.
Is Mode Still Independent? (ThoughtSpot Acquisition)
No. ThoughtSpot acquired Mode in June 2023 for about $200M. Mode continues to operate as a product, and the integration pairs Mode's analyst workflow with ThoughtSpot's search-based self-service BI. For buyers this cuts both ways: more resources behind the product, but also packaging changes and a roadmap increasingly tied to ThoughtSpot's platform — worth probing during procurement.
When to Use It
✅ Data team needs exploratory analytics
✅ Want SQL + Python in one place
✅ Need report version control
✅ Balancing ad-hoc and production reporting
✅ Data discovery and knowledge sharing important
When NOT to Use It
❌ Non-technical users building dashboards
❌ Need a fully-featured, governed BI platform
❌ Want drag-and-drop interface only
❌ Small team without SQL skills
❌ Need complex in-tool data transformations (do them in dbt/warehouse instead)
Common Use Cases
Data exploration: Ad-hoc analysis and hypothesis testing
Analyst workflows: SQL + Python for comprehensive analysis
Executive reporting: Production dashboards from SQL queries
Data discovery: Search and reuse queries across teams
Collaborative analysis: Comment and discuss findings with stakeholders
Mode vs Alternatives
vs Looker: Mode wins for ad-hoc, code-first analysis and speed to first insight; Looker wins for governed, modeled metrics served to hundreds of business users. Many teams run both.
vs Hex: Hex is the closest modern rival — notebook-native with slicker Python/AI features and reactive app building; Mode is stronger on classic BI reporting, scheduled delivery, and the query catalog. Hex generally feels newer; Mode more report-oriented.
vs Tableau: Mode better for SQL-first teams and reproducible analysis; Tableau far better visualization depth and business-user self-service.
vs Jupyter: Mode better for sharing, scheduling, and collaboration; Jupyter more flexible and free, but you build the sharing layer yourself.
Unique Strengths
Notebook meets BI: Best blend of exploration and production reporting
Version control: Proper version history — rare among BI tools
Data catalog: Excellent query discovery and reuse
SQL + Python: Seamless workflow between languages
If your team is SQL-first but wants something open source instead, compare Redash — simpler and self-hosted, without the notebooks.
Bottom line: The best middle ground between notebooks and BI tools for teams that live in SQL and Python. Strong collaboration, versioning, and query reuse; weaker for non-technical self-service. Post-ThoughtSpot, verify pricing and roadmap before committing — but the core analyst workflow remains best-in-class.