Mode Analytics built the SQL + Python notebook category — write SQL, get results, transform with Python in the same workspace. QueryFlow's Flow Books bring the same workflow native to Mac, with Claude AI built into both the SQL and Python cells.
Quick answer: QueryFlow is the native macOS alternative to Mode Analytics. Both offer SQL + Python notebooks in one workspace. Mode is cloud-hosted (paid plans start around $50+/month per user); QueryFlow runs locally for $25/month flat. QueryFlow adds Claude AI in both SQL and Python cells with full schema awareness. Best for solo analysts and small data teams who prefer Mac-native desktop tools.
Mode Analytics was one of the first products to ship the SQL + Python notebook model that's now standard. Write SQL to query a database, get tabular results, then transform those results with Python in the same notebook environment. Mode also pioneered shareable, embeddable reports and built strong collaboration features. Pricing: Mode Studio Free for limited use, Pro plans starting around $50/user/month, Enterprise custom.
Mode was acquired by ThoughtSpot in 2023, which has affected its pace of innovation. The product remains capable but the feature velocity has slowed. For Mac-based analysts who want a polished local notebook experience without per-user subscription costs, alternatives have become more attractive.
Flow Books are QueryFlow's native notebook environment. SQL cells query connected databases (Snowflake, Postgres, MySQL, Redshift, BigQuery on roadmap, Salesforce SOQL). Python cells transform results using full Pandas, NumPy, and the Pyodide-shipped scientific Python stack. Cells share state — SQL output is automatically available as a DataFrame in Python cells. Claude AI works in both cell types.
Mode wins: cloud-based collaboration with shared notebooks, more polished sharing/embedding features, native HEX-like dashboards, broader database connector library, multi-user team workflows. QueryFlow wins: native Mac performance (sub-second cold start vs browser tab), Claude AI integration in cells with schema awareness, flat pricing not scaled per-user, works offline for cached schemas, no monthly login sessions to manage.
Solo Mac-based analyst: Mode Pro $50/month = $600/year. QueryFlow $300/year. 5-person small team: Mode 5×$50/month = $3,000/year. QueryFlow ~$1,500/year (5 Macs each subscribed). The math favors QueryFlow especially for small teams. Mode's value proposition is stronger at larger team sizes where collaboration features compound.
Mode notebooks export as SQL files + Python files. Recreating each Mode notebook in QueryFlow as a Flow Book takes 10-20 minutes per notebook depending on complexity. Most teams complete migration of 10-20 notebooks in a couple working days. Run both tools in parallel during validation.
Larger data teams (10+) where collaboration features matter. Heavy use of Mode's dashboard/embedding capabilities for stakeholder reports. Teams that explicitly prefer cloud-managed infrastructure. Workflows requiring Mode-specific connectors QueryFlow doesn't have yet.
Mode exports queries as standard SQL. Paste each query into a QueryFlow SQL cell or save it as a SQL file QueryFlow can open. The dialect translation is rarely needed — both tools use the database's native dialect.
Not directly. QueryFlow's Observatory dashboard is internal monitoring, not for external sharing. For dashboards consumed by stakeholders, pair QueryFlow with Google Sheets (push curated data to a Sheet that team members reference) or write to a destination consumed by Looker/Tableau.
Python cells can produce matplotlib, seaborn, or plotly charts as in any Python notebook. The interactive HEX-style native visualizations Mode offers aren't directly replicated; for that workflow, dedicated visualization tools (Tableau, Sigma) work better.
Pyodide ships with pandas, numpy, scipy, scikit-learn, matplotlib, seaborn, statsmodels, sympy, and dozens more. Packages from PyPI can be installed at runtime. For most analytical Python work, the available libraries match what Mode provides.
In a QueryFlow Flow Book, the last SQL cell's result is automatically available as DataFrame `df` in Python cells. Earlier SQL results are available as `df_1`, `df_2`, etc. Python cells can also write back to a SQL cell's result via assignment. Same conceptual model as Mode.
14-day free trial. Move your Mode notebooks to Flow Books and run analyses without leaving your Mac.