Name: Mode
Price: Freemium

What is Mode?

Mode is an analytics platform built for analysts who want to write SQL, run Python or R notebooks and publish dashboards without switching tools. Its shared SQL editor keeps every query saved and organised so teams build on each other's work rather than rewriting it, and its notebook layer supports advanced analysis beyond what a dashboard tool covers. Customers have included Bloomberg, Shopify, Lyft and Reddit. The ownership change is the context readers need. ThoughtSpot acquired Mode in a $200 million cash and equity deal completed in July 2023, and Mode became a wholly owned subsidiary, with its SQL IDE and connected notebooks folded into ThoughtSpot's data workspace alongside natural-language search. If you are evaluating Mode, you are effectively evaluating ThoughtSpot's code-first layer, so check current packaging and whether it is still sold standalone.

Key Features

Shared SQL EditorPython and R NotebooksHelix In-Memory EngineInteractive DashboardsAutomated Report SchedulingSQL AutocompleteCustom Chart BuilderHTML Chart CustomizationSlack and Email SharingAPI AccessEmbedded AnalyticsSnowflake ConnectorBigQuery ConnectorReport Version HistoryParameter Filters

How to Use Mode

1Connect Mode to your data warehouse (Redshift, Snowflake, BigQuery, PostgreSQL, and more)

2Write SQL queries in the collaborative editor with autocomplete and query history

3Load results into Python or R notebooks for advanced statistical analysis

4Build interactive dashboards with drag-and-drop charts, filters, and text blocks

5Share reports via Slack, email, or embed them in your internal tools or product

✅ Best For

Data analysts and analytics engineers who want to write SQL and run Python or R notebooks in one place and share polished, interactive reports with business stakeholders without switching between multiple tools.
Analytics teams at mid-size SaaS and technology companies that need a centralized platform for both ad-hoc analysis and recurring stakeholder reporting, replacing a fragmented stack of SQL editors, notebooks, and dashboarding tools.

❌ Not For

Non-technical business users who need a no-code, drag-and-drop analytics experience, as Mode's core workflow is SQL-first and assumes familiarity with writing queries.
Large enterprises requiring advanced data governance, fine-grained role hierarchies, or SSO without upgrading to the Enterprise plan, as these features are gated behind higher-tier pricing.

Reviews

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Pricing

Studio$0

✓Up to 3 users
✓private DB connections
✓SQL
✓Python
✓R
✓10MB/query

BusinessCustom

✓250GB/month data
✓scheduling
✓API access
✓Slack sharing
✓team collaboration

EnterpriseCustom

✓50+ seats
✓SSO
✓SCIM
✓admin APIs
✓large-scale visualizations
✓premium support

Prompts to Try

Show me a 90-day retention curve for users who signed up in January

Write a Python script to build a linear regression on our sales data

Which cohort of users has the highest lifetime value based on acquisition channel?

Schedule a weekly SQL report of new MRR by pricing plan delivered to Slack

Build a dashboard showing product engagement by feature for the last 30 days

Use Cases

A growth analyst at a SaaS company writes a SQL query in Mode to segment users by activation milestone, then runs a Python notebook on the same results to build a survival analysis, sharing the complete findings as a single branded report with the product team.

A data team lead uses Mode's shared SQL editor to standardize all company metric definitions in a single library, so every analyst pulls from the same source tables instead of writing conflicting queries.

An analytics engineer at an e-commerce startup builds a weekly revenue report in Mode that auto-refreshes every Monday at 8am and sends a Slack digest to the sales team, replacing 3 hours of manual reporting.

A product manager uses Mode's parameter filters to explore user retention by cohort interactively, drilling from monthly aggregates to weekly breakdowns without asking the data team for a new report.

A head of data uses Mode's report history to audit how a key churn metric was calculated 6 months ago, tracing the exact SQL and Python logic without relying on spreadsheet documentation.

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