Hex Notebooks

Collaborative data workspace combining SQL; Python; and notebook apps.

Reviewed by 7wData

On this page

Publisher review

Hex is a cloud-based analytics workspace that combines SQL, Python, and visualization in a single collaborative environment, positioned as a modern replacement for Jupyter plus separate BI tools. Founded in 2019 by former Palantir engineers, it targets cross-functional data teams—analysts, scientists, and business users—who need to share exploratory analysis, build reproducible workflows, and publish results as interactive apps without rebuilding from scratch.

Unlike Jupyter, which was designed for local individual work and creates merge conflicts in Git, Hex offers real-time multiplayer editing with automatic conflict resolution. SQL and Python work together by default, meaning SQL-only analysts can use notebooks without Python expertise. The platform includes Hex Magic, an AI code generator that writes SQL and Python from natural language, and a newer Notebook Agent powered by Claude that can orchestrate multi-step analyses—planning, searching data, creating cells, and synthesizing findings automatically.

Hex integrates with major data warehouses (Snowflake, BigQuery, Databricks, Redshift, Postgres) and includes dbt semantic layer support for consistent metric definitions. Apps can be published one-click from notebooks into stakeholder-facing interactive dashboards with separate permissions, eliminating the rebuild tax of traditional BI workflows.

The trade-off is cost and performance. Pricing combines per-editor subscriptions ($36–$75/month) with pay-as-you-go compute billing ($0.32–$6.70/hour depending on profile), making budgeting unpredictable for heavy workloads. Query performance lags on million-row datasets when multiple queries chain together, and users report the AI agent can be prescriptive, making changes without explicit instruction. For teams already investing in Jupyter, the learning curve is low; for teams evaluating BI platforms, the decision is whether SQL-in-a-notebook justifies the per-seat cost.

Get the AI & data signal, daily.

335k+ subscribers read this every morning. One email, both newsletters. Unsubscribe anytime.

How it works

  1. Multi-language notebooks

    Write SQL, Python, and no-code cells in the same project without switching tools; SQL-only users can work alongside Python teams.

  2. Hex Magic (AI code generation)

    Natural-language prompts generate SQL queries and Python scripts using database metadata; all code appears as drafts for inspection and editing before acceptance.

  3. Notebook Agent

    Claude-powered agent plans analyses, searches tables, creates and chains SQL/Python cells, builds visualizations, and writes findings—controlled via @mentions and diff-based review.

  4. One-click app publishing

    Convert exploratory notebooks into interactive dashboards with separate permissions, charts, and controls—no rebuild required.

  5. Real-time collaboration

    Multiplayer editing with automatic conflict resolution, version history, and comment threads; no Git merge conflicts or file contention.

  6. Data warehouse integrations

    Native connections to Snowflake, BigQuery, Databricks, Redshift, Postgres, and others; includes dbt semantic layer support for standardized metrics.

  7. Reactive execution model

    DAG-based computation ensures reproducibility and correctness; compute pushdown to the warehouse handles datasets of any size.

Strengths and trade-offs

Strengths

  • Eliminates tool fragmentation: SQL, Python, and dashboarding in one environment eliminates context switching and rebuild tax.
  • Accessible to mixed teams: SQL-only users can work alongside Python experts without learning Python; AI agents handle boilerplate.
  • Real-time collaboration with no merge conflicts: Multiplayer editing, version history, and automatic conflict resolution work like Google Docs, unlike file-based Jupyter.

Trade-offs

  • Performance degrades on large datasets: Million-row queries and chained analyses lag; in-memory kernel limits remain a bottleneck despite cloud claims.
  • Unpredictable compute costs: Per-minute billing on top of per-seat subscriptions makes budgeting difficult; larger workloads can inflate costs beyond initial estimates.
  • AI agent prescriptiveness and confusion: Notebook Agent makes changes without explicit instruction; overlapping features (Explore vs Project, Threads vs Collections) confuse new users.

Pricing context

Hex uses a hybrid per-seat and pay-as-you-go model. Community is free with 100 Magic requests/month/user and a 5-project limit. Professional is $36/editor/month (removes project cap, extends version history to 30 days).

Team is $75/editor/month and includes real-time collaboration, shared components, and scheduled automation. All paid plans include Medium compute (standard notebooks) at no additional charge. Advanced compute profiles (16GB+ RAM, GPU) incur pay-as-you-go charges of $0.32–$6.70/hour based on profile size; billing occurs every $100 of spend monthly or at cycle end depending on contract terms.

Enterprise is custom and includes SSO, audit logging, HIPAA support, and dedicated support. Community users are limited to free tier features; paying editors unlock Magic, the Notebook Agent, and scheduling.

Getting started with Hex Notebooks

  1. Sign up for Community account

    Visit hex.tech, create an account with your email, and access the free Community tier. You'll receive 100 Hex Magic requests monthly and can create up to 5 projects. Community users skip compute costs but have limited version history and no scheduled automation features.

  2. Connect your data warehouse

    From your workspace settings, select 'Add data source' and choose your warehouse type: Snowflake, BigQuery, Databricks, Redshift, or Postgres. Enter your credentials and test the connection. Optionally, configure dbt semantic layer integration to sync standardized metric definitions across your team's notebooks.

  3. Create notebook and write queries

    Create a new notebook and open a SQL cell. Write your first query targeting tables in your connected warehouse. Add a Python cell below to transform or visualize the results. SQL-only expertise is sufficient; you can stay in SQL or add Python cells as needed for advanced transformations.

  4. Use AI to generate analysis

    Type a natural-language prompt (e.g. 'Show sales by region') into a Hex Magic cell to generate a SQL or Python code draft. Review the generated code and edit as needed before accepting. For complex analyses, @mention the Notebook Agent and describe what you need; it will plan, search tables, and create linked cells.

  5. Publish notebook as shareable app

    Click 'Publish' in your notebook and choose which charts, tables, and interactive controls to display. Set permissions to grant access to specific collaborators or viewers. Turn on 'Schedule' to set the notebook to refresh automatically on a daily or weekly cadence without manual reruns.

Frequently Asked Questions

What is Hex Notebooks?

Hex is a cloud-based analytics workspace founded in 2019 that combines SQL, Python, and visualization in one environment. It's positioned as a modern alternative to Jupyter plus separate business intelligence tools, enabling cross-functional teams to build, share, and publish interactive analyses without tool switching or rebuilding dashboards.

How does Hex compare to Jupyter?

Unlike Jupyter, Hex offers real-time multiplayer editing with automatic conflict resolution, eliminating Git merge conflicts. It integrates SQL and Python by default so SQL-only analysts can work without Python expertise. Hex also enables one-click publishing to interactive dashboards, whereas Jupyter requires rebuilding in separate BI tools.

What is Hex Magic?

Hex Magic is an AI code generator that writes SQL queries and Python scripts from natural language prompts using database metadata. All generated code appears as drafts for review and editing before acceptance, ensuring users maintain control over outputs without executing untested code.

What does the Notebook Agent do?

The Notebook Agent, powered by Claude, orchestrates multi-step analyses automatically. It plans workflows, searches data tables, creates and chains SQL and Python cells, builds visualizations, and synthesizes findings. Users control it via @mentions and can review changes through diff-based review before execution.

How much does Hex cost?

Hex pricing combines monthly per-editor subscriptions with variable compute billing. Community is free with limits on projects and Magic requests. Professional costs $36/editor/month, Team costs $75/editor/month, and advanced compute profiles incur $0.32–$6.70 hourly charges. Enterprise plans are custom. Per-minute billing makes budgeting unpredictable.

What are the main limitations of Hex?

Performance degrades on million-row datasets and chained queries. Compute costs are unpredictable due to per-minute billing on top of subscriptions. The Notebook Agent can be prescriptive, making changes without explicit instruction. Interface confusion also exists with overlapping features like Explore vs Project and Threads vs Collections.

Alternatives in this category

Integrations

Snowflake Databricks BigQuery dbt Postgres

How Hex Notebooks compares

Direct head-to-head against 3 competitors. Picked by 7wData.

This tool

Hex Notebooks

Pricing
Hex uses a hybrid per-seat and pay-as-you-go model. Community is free with 100 Magic requests/month/user and a 5-project limit. Professional is $36/editor/month (removes project cap, extends version history to 30 days). Team is $75/editor/month and includes real-time collaboration, shared components, and scheduled automation. All paid plans include Medium compute (standard notebooks) at no additional charge. Advanced compute profiles (16GB+ RAM, GPU) incur pay-as-you-go charges of $0.32–$6.70/hour based on profile size; billing occurs every $100 of spend monthly or at cycle end depending on contract terms. Enterprise is custom and includes SSO, audit logging, HIPAA support, and dedicated support. Community users are limited to free tier features; paying editors unlock Magic, the Notebook Agent, and scheduling.
Target
Hex is a cloud-based analytics workspace that combines SQL, Python, and visualization in a single collaborative environment, positioned as a modern replacement for Jupyter plus
Deployment
cloud
Strength
Eliminates tool fragmentation: SQL, Python, and dashboarding in one environment eliminates context switching and rebuild tax.
Watch for
Performance degrades on large datasets: Million-row queries and chained analyses lag; in-memory kernel limits remain a bottleneck despite cloud claims.

Deepnote

Pricing
Free up to 3 editors. Team plan ~$39/editor/month (annual). Enterprise: custom quote.
Target
Data science teams wanting real-time collaborative Python, R, and SQL notebooks in the cloud.
Deployment
SaaS, open-source (Apache 2.0)
Strength
Real-time multiplayer editing with inline comments, connecting 60+ data sources including BigQuery and Snowflake.
Watch for
Per-editor seat pricing escalates quickly for larger teams. Enterprise costs are opaque, quote-only.

Mode Analytics

Pricing
Studio plan free. Pro ~$6,000/year. Enterprise up to ~$50,000/year. All paid tiers require sales contact.
Target
SQL-first data analysts sharing dashboards with non-technical business stakeholders inside mid-to-large companies.
Deployment
SaaS
Strength
SQL editor, Python/R notebooks, and auto-refresh dashboards in one platform, piping query results directly into notebooks.
Watch for
Acquired by ThoughtSpot in 2023 for $200M. Mode absorbed into Analyst Studio (GA 2025), creating migration friction and roadmap uncertainty.

Databricks Notebooks

Pricing
Consumption-based via DBUs. All-Purpose Compute ~$0.40-0.55/DBU. Median annual buyer spend ~$250,000.
Target
Enterprise data and ML engineering teams running large-scale workloads on AWS, Azure, or GCP.
Deployment
SaaS, multi-cloud (AWS, Azure, GCP)
Strength
Mixed-language cells (Python, SQL, R, Scala) with automatic versioning and notebook-scoped library isolation.
Watch for
Consumption billing causes unpredictable costs. Clusters left running trigger bill shock. Proprietary Delta Lake and MLflow create vendor lock-in.

User reviews

No user reviews yet. Be the first to write one.

Sources

Reporting on this tool draws on these publicly available sources.

  1. hex.tech — Exact pricing tiers: Community (free), Professional ($36/editor/month), Team ($75/editor/month), Enterprise custom; compute billing model and included credits.
  2. hex.tech — Notebook Agent capabilities, Claude Sonnet 4 integration, multi-step analysis orchestration, @mention control, diff-based review.
  3. learn.hex.tech — Advanced compute pricing ($0.32–$6.70/hour), per-minute billing, spend limits, availability on Team and Enterprise only, 90-day usage logs.
  4. www.g2.com — User complaints on performance lag with large datasets, AI prescriptiveness, interface confusion; praise for collaboration, SQL-Python integration, and support.
  5. hex.tech — Positioning versus Jupyter: real-time collaboration vs JSON merge conflicts, SQL-without-Python accessibility, one-click publishing vs rebuild tax, semantic layer integration.
  6. www.crunchbase.com — Founded 2019, founders Barry McCardel, Caitlin Colgrove, Glen Takahashi (ex-Palantir), San Francisco HQ.
  7. learn.hex.tech — Hex Magic and AI features overview, code generation from natural language, integration with database metadata.
  8. hex.tech — Core features: multi-language cells, reactive execution, collaboration, AI agents, app publishing, integrations with Snowflake, BigQuery, dbt.