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Coding Agents🟡Low Code🏆Editor's Choice
H

Hex

Collaborative data science platform that combines SQL, Python, and no-code analysis with AI assistance

Starting atFreemium
Visit Hex →
💡

In Plain English

Collaborative data science platform combining SQL, Python, and no-code analysis with AI assistance and real-time collaboration.

OverviewFeaturesPricingUse CasesIntegrationsLimitationsFAQSecurityAlternatives

Overview

Hex is a modern AI-powered analytics and data science platform designed for entire data teams, blending the flexibility of a notebook environment with the polish of a business intelligence tool. It brings together SQL, Python, R, and a no-code interface in a single collaborative workspace where analysts, data scientists, and business stakeholders can work side by side on the same projects in real time. Rather than forcing teams to choose between scripting power and self-service usability, Hex layers an AI copilot, called Magic, across every surface of the product so that users can generate queries, build charts, debug code, and even produce full notebooks from natural language prompts.

At its core, Hex organizes work into projects that combine a logical compute layer (cells of SQL, Python, or no-code transformations) with an interactive presentation layer (apps and reports). The same underlying notebook can be published as a polished interactive dashboard, embedded into another product, or scheduled to refresh and deliver results to stakeholders. Hex connects natively to modern cloud data warehouses such as Snowflake, Databricks, BigQuery, Redshift, and Postgres, as well as to dbt projects and semantic layers, allowing analysts to query governed data without copying it out of the warehouse.

The AI experience in Hex has matured into a true agentic workflow. Magic can scaffold an entire analysis from a question, propose joins across schemas using warehouse and dbt metadata for context, write and explain SQL or Python, fix broken cells, and summarize results in plain language. Newer agentic capabilities allow Hex to plan multi-step investigations, iterate on results, and produce shareable narratives, positioning the product as a coding agent for analytics rather than just an autocomplete tool. Combined with version control, granular permissions, reviews, and SOC 2 / HIPAA compliance, Hex targets serious enterprise data teams while keeping the onboarding fast enough for individual analysts on the free Community plan.

🎨

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Key Features

AI-Powered Magic Assistant+

Hex Magic uses large language models to generate SQL and Python code from natural language prompts, debug errors with contextual understanding of your project, and explain complex analytical results in plain English. It is schema-aware, meaning it understands your connected database tables and columns, producing accurate queries without requiring users to memorize table structures.

Multi-Language Notebook Environment+

Hex notebooks support SQL, Python, and R cells within the same project, with reactive execution that automatically updates downstream cells when upstream data changes. This allows teams to combine the strengths of each language — SQL for data extraction, Python for transformation and modeling, and built-in charting for visualization — in a single cohesive workflow.

Interactive Data Apps and Publishing+

Any Hex notebook can be published as a polished, interactive data application with parameterized input controls such as dropdowns, sliders, date pickers, and text inputs. Published apps can be shared via link, embedded in other websites or internal tools via iframe, and automatically reflect the latest data when schedules are configured.

Real-Time Collaboration and Version Control+

Multiple team members can edit a Hex project simultaneously with live cursors, presence indicators, and threaded cell-level comments. Built-in version control tracks every edit with full diff history, branching support for development workflows, and the ability to restore any prior version — eliminating the need for external Git integration for most use cases.

Native Data Warehouse Connectivity+

Hex connects directly to Snowflake, Databricks, BigQuery, Redshift, PostgreSQL, and other major databases through managed, admin-configured connections. Query results are cached intelligently to reduce warehouse costs, and connection credentials are managed centrally so individual analysts never need direct access to database passwords or service accounts.

Pricing Plans

Community

Free

  • ✓Single user or small personal projects
  • ✓Access to Magic AI with usage limits
  • ✓Connect to cloud data warehouses and run notebooks
  • ✓Publish public apps and reports
  • ✓Limited compute and project history

Team

Starts around $24 per user/month

  • ✓Multiplayer collaboration and shared workspaces
  • ✓Private apps and reports with permissions
  • ✓Scheduled runs and notifications
  • ✓Standard Magic AI features and dbt integration
  • ✓Email and chat support

Professional

Higher per-user pricing, contact for quote

  • ✓Advanced governance, reviews, and version control
  • ✓More compute, longer runtimes, and larger memory
  • ✓Expanded Magic AI usage and agentic workflows
  • ✓App embedding and external sharing
  • ✓Priority support

Enterprise

Custom pricing

  • ✓SSO/SAML, SCIM, and advanced RBAC
  • ✓Audit logs, IP allowlisting, and HIPAA support
  • ✓VPC peering and private deployment options
  • ✓Dedicated success manager and onboarding
  • ✓Custom compute and AI usage limits
See Full Pricing →Free vs Paid →Is it worth it? →

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Best Use Cases

🎯

Data teams collaborating on exploratory analysis in a shared notebook where analysts write SQL and data scientists add Python modeling in the same project

⚡

Building interactive, self-service dashboards for business stakeholders who need to filter and explore metrics without writing any code

🔧

Automating recurring reports with scheduled notebook runs that refresh data and deliver results via email or Slack on a daily or weekly cadence

🚀

Rapid prototyping of data applications — such as customer segmentation tools or revenue forecasting models — that can be published and shared as interactive web apps

💡

Onboarding new analysts who can leverage AI-assisted query generation to explore unfamiliar data warehouses and learn schema patterns faster

🔄

Embedding live analytics into internal portals or external customer-facing products using Hex's published app embedding capabilities

Integration Ecosystem

3 integrations

Hex works with these platforms and services:

🔗 Other
JDBCODBCREST API
View full Integration Matrix →

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Hex doesn't handle well:

  • ⚠Hex is purpose-built around cloud data warehouses and modern data stacks, so teams without a warehouse or working primarily with on-prem databases or local CSVs will see limited value. Compute is sandboxed per project with memory and runtime limits that vary by plan, which can constrain heavy ML training or very large in-memory Pandas workloads compared to a dedicated Spark or GPU environment. Magic AI, while strong at first-draft SQL and Python, still requires expert review on complex joins, business logic, and statistical work and can hallucinate column names or relationships. Customization of published apps is more limited than dedicated front-end frameworks, and advanced governance features such as private deployments, VPC peering, fine-grained audit logs, and HIPAA support are gated to higher-priced or enterprise plans that require contacting sales.

Pros & Cons

✓ Pros

  • ✓Magic AI assistant generates SQL, Python, and full notebook scaffolds from natural language and is context-aware of connected warehouses and dbt models
  • ✓Single environment unifies SQL, Python, R, and no-code cells, so analysts and data scientists can collaborate without switching tools
  • ✓Notebooks can be published as polished interactive apps and dashboards without rebuilding the analysis in a separate BI tool
  • ✓Deep native integrations with Snowflake, Databricks, BigQuery, Redshift, dbt, and semantic layers keep work governed inside the warehouse
  • ✓Real-time multiplayer editing, comments, version history, and review workflows make collaboration feel closer to Figma or Google Docs than a traditional notebook
  • ✓Generous free Community tier and a transparent usage-based pricing model lower the barrier to evaluating the product

✗ Cons

  • ✗Pricing scales quickly once teams need advanced governance, embedding, or higher compute, and enterprise tiers require sales contact
  • ✗Heavily oriented toward cloud data warehouses; teams without a modern warehouse or those working primarily with local files get less value
  • ✗Magic AI suggestions can be confidently wrong on complex joins or domain-specific logic and still require expert review
  • ✗Apps and dashboards, while attractive, are less customizable than dedicated BI tools like Looker or Tableau for pixel-perfect reporting
  • ✗Performance on very large in-memory Python workloads can lag specialized environments since compute is shared and capped per plan

Frequently Asked Questions

How does Hex Magic AI work and what can it do?+

Hex Magic is an AI assistant integrated directly into the notebook environment. It can generate SQL queries and Python code from natural language descriptions, debug errors by analyzing your code and stack traces, explain complex query results or code logic in plain English, and suggest visualizations based on your data. Magic understands the context of your project including your schema, prior cells, and connected data sources, so its suggestions are tailored to your specific analysis rather than generic code completions.

What data sources can Hex connect to?+

Hex offers native connectors for major cloud data warehouses and databases including Snowflake, Databricks, Google BigQuery, Amazon Redshift, PostgreSQL, MySQL, SQL Server, and Amazon Athena. It also supports connections through generic JDBC/ODBC drivers for other databases. Data connections are configured at the workspace level by admins, and individual users can query any data source they have been granted access to without needing to manage credentials themselves.

Can non-technical team members use Hex?+

Yes, Hex is designed to bridge technical and non-technical users. Business stakeholders can interact with published Hex apps through parameterized input controls like dropdowns, date pickers, and text fields without ever seeing the underlying code. They can explore interactive charts, filter dashboards, and download results. The viewer role allows non-technical users to consume and interact with analyses at no additional editor seat cost on most plans.

How does Hex handle version control and collaboration?+

Hex includes built-in version control that automatically tracks every change to a project, allowing users to view diffs, restore previous versions, and understand who changed what and when. Multiple users can edit the same project simultaneously with real-time cursors and presence indicators, similar to Google Docs. Projects also support threaded comments on individual cells, status workflows for review and approval, and branching for developing changes without affecting the published version.

Can I schedule Hex projects to run automatically?+

Yes, Hex supports scheduled runs that automatically re-execute your notebooks on a cron-based schedule — hourly, daily, weekly, or custom intervals. Scheduled runs can trigger email notifications or Slack alerts when they complete or when specific conditions are met in your data. This makes Hex suitable for production reporting workflows, automated data quality checks, and recurring analytical pipelines that need to stay up to date without manual intervention.

🔒 Security & Compliance

🛡️ SOC2 Compliant
✅
SOC2
Yes
—
GDPR
Unknown
—
HIPAA
Unknown
✅
SSO
Yes
—
Self-Hosted
Unknown
—
On-Prem
Unknown
✅
RBAC
Yes
—
Audit Log
Unknown
—
API Key Auth
Unknown
—
Open Source
Unknown
—
Encryption at Rest
Unknown
—
Encryption in Transit
Unknown
🦞

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What's New in 2026

Through late 2025 and into 2026, Hex has leaned heavily into agentic AI for analytics. Magic has evolved from a code-completion assistant into a multi-step agent that can plan investigations, iterate on results, and produce narrative summaries with citations back to the underlying SQL and data. Hex has expanded semantic layer and dbt metadata awareness so Magic understands governed metrics, deepened integrations with Databricks and Snowflake (including support for newer warehouse-native AI features), and added richer app-building primitives such as improved layouts, conditional logic, and embedding controls. Enterprise governance has also been strengthened with more granular audit logging, expanded HIPAA coverage, and refined review and approval workflows aimed at regulated industries.

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Quick Info

Category

Coding Agents

Website

hex.tech
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