January 5th, 2026
11 Hex Competitors & Alternatives: Features & Pricing in 2026
By Simon Avila · 30 min read
After testing notebook platforms for data teams, I found the 11 best Hex competitors for collaborative analysis for 2026.
11 Best Hex competitors: At a glance
Some Hex competitors prioritize real-time collaboration, while others emphasize open-source flexibility or enterprise governance. Here's how the top 11 alternatives compare for 2026:
Tool | Best For | Starting Price (billed annually) | Key advantage compared to Hex |
|---|---|---|---|
Natural language analysis without notebooks | Ask questions and get charts without writing code or managing compute costs | ||
Real-time collaborative notebooks | Live multiplayer editing with better version control | ||
JavaScript-based reactive notebooks | Reactive cells that update automatically when data changes | ||
Open-source notebook standard | Free | Complete control over environment and no vendor lock-in | |
Enterprise data lakehouse workflows | Native Spark integration for massive dataset processing | ||
SQL-first analytics with light Python | Built for SQL users who need quick business reporting | ||
Visual canvas-style analysis | Drag-and-drop cells instead of linear notebook structure | ||
Free GPU access for ML work | Free compute with GPU support for machine learning | ||
Building internal tools and dashboards | $10/user/month + $5/end user/month | Low-code app builder for custom business tools | |
Scalable Python and R in the cloud | Easy scaling to large compute without infrastructure work | ||
Search-based analytics for business users | Natural language search interface without notebook complexity |
Why I looked for Hex competitors
I used Hex for collaborative notebook work and ran into per-editor seat costs that added up faster than expected. Compute charges stacked on top of seat pricing, which made budgeting difficult when scheduled jobs ran longer than planned.
The notebook interface worked well for SQL and Python users, but non-technical teammates struggled to get value without learning code.
Performance started to slow when I pushed larger datasets through the platform, and query times stretched longer than I wanted for quick checks. The collaborative features helped with real-time editing, but I needed stronger governance controls and better separation between development work and production dashboards for wider company access.
After some research and testing, I found a few reasons why teams look for Hex competitors:
Unpredictable compute costs: Per-minute billing on top of seat pricing creates budget uncertainty for heavy workloads
Performance limits on large datasets: Query execution slows down when working with tables that have millions of rows
Limited non-technical access: Notebooks require coding knowledge, which blocks business users from self-service analysis
1. Julius: Best for natural language analysis without notebooks
We designed Julius to act as your pocket data scientist. You connect a source like Postgres, Snowflake, or Google Sheets, ask a question in plain English, and Julius returns a chart without you writing SQL or Python. This helps business users get fast answers on metrics or trends without waiting for data teams or learning query languages.
Analysis runs automatically, and you can review each step in a clear, auto-generated Notebook. You can adjust filters, verify the logic, and schedule recurring reports in the same workspace. This makes weekly metric checks easier to repeat without rebuilding the analysis from scratch.
Julius uses AI to learn your data structure over time through its semantic layer. It maps table relationships and column definitions, so follow-up questions reference the same context without repeating setup steps.
You can also customize visualizations with your brand colors and hide code entirely for non-technical users, or switch to code view when you need to verify query logic.
Both business users and technical teams work in the same structure, which makes collaboration straightforward without forcing everyone into separate tools.
Why it beats Hex
Faster setup: Get charts without configuring notebook environments or managing compute resources
Natural language queries: Ask for metrics in plain English instead of writing SQL or Python
Transparent analysis: You can open a Notebook to see the steps Julius took
Predictable pricing: Flat monthly rate without per-editor seats or compute overages
Context retention: Julius remembers your earlier questions and data structure for consistent follow-ups
Pros
Quick chart creation from natural language prompts
Easy sharing with clean visual outputs
Notebook workflow keeps analysis steps organized
Cons
Not built for advanced ML pipelines or deep debugging
Lighter dashboard features than dedicated BI platforms
Pricing
Julius starts at $16 per month for the Plus plan.
Bottom line
Julius works well when you want direct visual answers without managing notebook infrastructure or compute costs. If you need collaborative code editing with version control, Deepnote may be a better fit for that workflow.
2. Deepnote: Best for real-time collaborative notebooks
Deepnote is a collaborative notebook platform built for data teams that need to work together on analysis. It supports Python, SQL, and R in a browser-based environment with real-time multiplayer editing.
I tested Deepnote with a small team on a shared project to see how the collaboration features handled simultaneous edits. The cursor tracking and live cell execution made it easy to see what teammates were working on without conflicts.
Version history helped us roll back changes when needed, and the commenting system kept discussions attached to specific cells instead of scattered across Slack. The notebook stayed responsive even when multiple people ran queries at the same time.Why it beats Hex
Real-time collaboration: See teammates' cursors and edits as they happen without refresh delays
Better version control: Track changes with clear history and easy rollbacks
Integrated SQL support: Query databases directly without switching tools
Pros
Strong multiplayer editing features
Clean interface for collaboration
Good database integration options
Cons
Pricing adds up quickly with more editors
Limited customization compared to self-hosted options
Pricing
Deepnote starts at $39 per editor per month.
Bottom line
Deepnote works well when your team needs real-time collaboration on notebooks with strong version control. If you want to avoid per-editor costs entirely, JupyterLab may be a better match.
3. Observable: Best for JavaScript-based reactive notebooks
Observable is a notebook platform built around JavaScript and reactive programming. Cells automatically update when their dependencies change, which makes building interactive visualizations faster than traditional notebooks.
I tried Observable for creating data dashboards, and the reactive cells auto-updated visualizations whenever data changed without manual refresh commands. The JavaScript focus meant I could use D3.js and other web libraries directly without translation layers.
The public notebook gallery helped me find examples for specific chart types, and forking others' work gave me a starting point for custom visualizations. The learning curve was steeper if you're used to Python-based notebooks, but the reactive approach made iterative visualization work more fluid.
Why it beats Hex
Reactive cells: Changes propagate automatically without manual re-execution
JavaScript ecosystem: Direct access to D3.js, Plotly, and other web visualization libraries
Public sharing: Easy publishing and forking of notebooks for collaboration
Pros
Powerful for interactive visualizations
Strong web-based charting capabilities
Good community and example library
Cons
Requires JavaScript knowledge
Less suited for Python-heavy data workflows
Pricing
Observable offers a free tier. Team plans use custom pricing.
Bottom line
Observable stands out for JavaScript-based visualization work with reactive cells that update automatically. If you need Python or R support instead, Deepnote offers better language flexibility.
4. JupyterLab: Best for open-source notebook standard
JupyterLab is the open-source foundation that most notebook platforms build on. It runs locally or on your own servers and supports Python, R, Julia, and dozens of other languages through kernel extensions.
I ran it on a local machine for data analysis work that required specific Python packages and custom environments. The interface gave me full control over package versions, execution environments, and data storage without vendor restrictions.
Extensions added features like Git integration and table viewers, and the active open-source community meant most problems had existing solutions.
Setup required more technical work than cloud platforms, but the flexibility and zero licensing costs made it worth the effort for long-term projects.
Why it beats Hex
Complete control: Run on your infrastructure without vendor dependencies
No vendor lock-in: Open-source platform with full ownership of your work
Unlimited customization: Add extensions and modify the environment freely
Pros
Free and open-source
Runs anywhere (local, cloud, on-premises)
Strong extension ecosystem
Cons
Requires technical setup and maintenance
No built-in collaboration features
Pricing
JupyterLab is free.
Bottom line
5. Databricks Notebooks: Best for enterprise data lakehouse workflows
Databricks Notebooks is built into the Databricks platform and focuses on large-scale data processing with Apache Spark. It handles SQL, Python, R, and Scala in a unified workspace connected directly to your data lakehouse.
I tested it on a multi-terabyte dataset to see how it managed distributed computing, and the Spark integration handled queries that might have crashed other platforms. The platform handled cluster setup and scaling automatically, so I didn't need to configure servers or manage resources manually.
Collaboration features included commenting and shared workspace organization, though the platform felt heavier than standalone notebook tools. The tight coupling with Databricks storage and compute made sense for teams already invested in that ecosystem.Why it beats Hex
Native Spark integration: Process massive datasets with distributed computing built in
Lakehouse architecture: Direct access to Delta Lake and unified data storage
Enterprise scale: Handles workloads too large for traditional notebook platforms
Pros
Great for big data processing
Strong cluster management
Good enterprise governance features
Cons
Expensive for smaller teams
Requires Databricks platform commitment
Pricing
Databricks uses custom pricing based on compute and storage usage.
Bottom line
Databricks Notebooks excel at processing massive datasets with Spark integration and enterprise-grade infrastructure. If you need notebook collaboration without the lakehouse commitment, Mode may offer a simpler SQL-first approach.
6. Mode: Best for SQL-first analytics with light Python
Mode is an analytics platform built around SQL queries with notebook support for Python and R when you need deeper analysis. It combines a SQL editor, visualization builder, and report scheduling in one workspace.
I liked how the SQL editor responded quickly with autocomplete and schema browsing when I tested Mode for building recurring business reports. Query results fed directly into charts without extra transformation steps, and the report builder let me arrange multiple visualizations into dashboards.
Python notebooks worked well for statistical analysis that SQL couldn't handle, though the interface felt more focused on SQL workflows. Scheduling reports to email or Slack kept stakeholders updated without manual exports, and version history tracked changes to queries over time.
Why it beats Hex
SQL-optimized workflow: Built for teams that work primarily in SQL with occasional Python needs
Fast business reporting: Quick path from query to chart to scheduled report
Simpler interface: Less complexity than full notebook platforms for standard analytics
Pros
Strong SQL editor with good autocomplete
Easy report scheduling and sharing
Good for business intelligence workflows
Cons
Python features feel secondary to SQL
Limited compared to full notebook environments
Pricing
Mode uses custom pricing based on team size and features.
Bottom line
Mode works well for SQL-focused teams that need occasional Python support and recurring reports. If you want visual canvas organization instead of linear reports, Count offers a different approach to analysis layout.
7. Count: Best for visual canvas-style analysis
Count is a notebook platform that uses a visual canvas layout instead of linear cells. You arrange queries, charts, and text in a freeform workspace that looks more like a design tool than traditional notebooks.
I enjoyed testing it for building analysis reports and found the canvas approach made it easier to organize complex projects with multiple data sources. Dragging cells around the workspace helped me group related analysis together, and the visual layout made presentations clearer than scrolling through linear notebooks.
The SQL and Python support covered standard analysis needs, and the collaborative features let teammates comment on specific canvas elements. The learning curve was low if you're used to design tools, though the structure was less natural for code-heavy workflows.
Why it beats Hex
Canvas interface: Freeform layout instead of linear notebook structure
Visual organization: Easier to see relationships between analysis components
Lower learning curve: Familiar drag-and-drop interface for non-technical users
Pros
Intuitive visual workspace
Good for presentation-ready reports
Strong collaboration features
Cons
Higher price per editor than most alternatives
Less suited for deep technical work
Pricing
Count starts at $99 per editor per month.
Bottom line
Count offers a unique canvas-style interface that makes analysis reports more visual and organized. If you need free compute with GPU access for machine learning, Google Colab provides that without monthly fees.
8. Google Colab: Best for free GPU access for ML work
Google Colab is Google's free notebook platform built on Jupyter with direct access to GPU and TPU computing. It runs entirely in the browser and connects to Google Drive for file storage.
I tested it for machine learning experiments that needed GPU acceleration, and the free tier gave me enough compute power for training smaller models without local hardware.
The interface matched standard Jupyter notebooks, so existing code ran without changes. Sharing notebooks through Google Drive links made collaboration simple, and the integration with Google's ecosystem meant I could pull data from Sheets or Drive without extra setup. Session limits kicked in after a few hours of use, which interrupted longer training runs.Why it beats Hex
Free GPU access: Train models without paying for compute or owning hardware
Zero setup: Start coding immediately in the browser
Google integration: Direct connections to Drive, Sheets, and other Google services
Pros
Completely free tier with GPU support
Easy sharing through Google Drive
No installation required
Cons
Session timeouts interrupt long-running work
Limited compared to paid platforms
Pricing
Google Colab is free. Colab Pro costs $9.99 per month for longer sessions and better GPUs.
Bottom line
9. Retool: Best for building internal tools and dashboards
Retool is a low-code platform for building internal applications, admin panels, and dashboards. It uses drag-and-drop components connected to databases and APIs instead of traditional notebook cells.
Testing Retool for creating a custom data dashboard showed me how the component library covered most interface needs without writing much code. Connecting to Postgres and REST APIs took minutes, and the query editor handled SQL and JavaScript for data transformation.
I liked that the visual builder made it faster to create polished interfaces than coding from scratch, though complex logic still required custom JavaScript. The platform worked better for building tools that others would use rather than exploratory analysis work.Why it beats Hex
Low-code builder: Create dashboards and tools with drag-and-drop components
Production-ready interfaces: Polish that notebooks can't match for end-user tools
Flexible pricing: Separate costs for builders and end users
Pros
Fast development for internal tools
Good component library
Strong database and API integrations
Cons
Not designed for exploratory analysis
Learning curve for the builder interface
Pricing
Retool starts at $10 per user per month for builders, plus $5 per user per month for end users.
Bottom line
10. Saturn Cloud: Best for scalable Python and R in the cloud
Saturn Cloud is a cloud platform for running Jupyter notebooks with easy scaling to larger compute resources. It handles Python and R workloads with simple controls for adjusting CPU, memory, and GPU allocation.
I tested it for data processing that outgrew my local machine, and the platform let me scale up to larger instances without infrastructure management. The notebook interface matched standard Jupyter, so existing code worked without changes.
Dask integration helped with parallel processing on larger datasets, and the resource controls made it easy to match compute power to workload needs. The pay-as-you-go pricing meant I only paid for active compute time, though costs climbed quickly with larger instances.
Why it beats Hex
Flexible scaling: Adjust compute resources up or down based on workload needs
Dask integration: Built-in parallel processing for larger datasets
Pay-as-you-go: Only pay for compute you actually use
Pros
Easy scaling without infrastructure work
Good Dask and parallel processing support
Standard Jupyter interface
Cons
Costs can grow quickly with heavy usage
Requires monitoring to control spending
Pricing
Saturn Cloud uses pay-as-you-go pricing based on compute usage.
Bottom line
11. ThoughtSpot: Best for search-based analytics for business users
ThoughtSpot is an analytics platform built around natural language search instead of notebooks or queries. Business users type questions in plain English, and the platform returns charts and data tables.
One thing I liked when I tested ThoughtSpot for quick metric checks was how well the search interface worked for standard business questions like revenue trends or user counts. The AI understood common business terms without training, and the visualization suggestions matched the question type.
The platform required admin setup to map data sources and define relationships, but end users could ask questions without SQL knowledge once configured. The focus on search made it less flexible than notebooks for complex analysis or custom transformations.
Why it beats Hex
Search interface: Business users get answers without learning SQL or notebooks
Fast answers: Type a question and get charts in seconds
Lower training requirements: Minimal onboarding for non-technical users
Pros
Easy for business users to adopt
Fast answers to common questions
Good visualization suggestions
Cons
Limited flexibility for complex analysis
Requires admin setup and data modeling
Pricing
ThoughtSpot starts at $25 per user per month.
Bottom line
How I tested these Hex competitors
I ran each platform through the same data analysis workflows to see how it handled real notebook work. I used datasets that ranged from a few thousand rows to several million, tested collaborative features with teammates, and tracked how quickly I could move from raw data to shareable reports. This gave me a clear view of which platforms delivered on their promises and which ones complicated the workflow.
Here's what I focused on during testing:
Setup speed: How long it took to connect data sources, configure environments, and run the first query.
Query performance: Response times for SQL and Python operations on datasets between 100K and 2M rows.
Collaboration tools: Real-time editing, commenting, version control, and how well multiple users could work without conflicts.
Cost transparency: Whether pricing was predictable or if compute charges and seat costs created budget surprises.
Interface usability: How much time I spent managing infrastructure versus doing actual analysis.
Sharing workflows: Ease of exporting results, scheduling reports, and giving non-technical users access to insights.
How to choose the right Hex competitor
Picking a Hex competitor depends on your team's technical skills and the type of analysis work you do most often. Choose:
Julius if you want natural language queries that return charts without writing code or managing notebooks.
Deepnote if your team needs real-time collaborative editing with strong version control for shared projects.
Observable if you build interactive visualizations in JavaScript and want reactive cells that update automatically.
JupyterLab if you need complete control over your environment without vendor lock-in or licensing costs.
Databricks Notebooks if you process massive datasets and need native Spark integration for distributed computing.
Mode if your workflow centers on SQL queries with occasional Python analysis and scheduled business reports.
Count if you prefer visual canvas layouts over linear notebooks for organizing complex analysis projects.
Google Colab if you need free GPU access for machine learning experiments without local hardware.
Retool if you want to build polished internal dashboards and admin tools instead of exploratory notebooks.
Saturn Cloud if you need to scale Python or R workloads to larger compute resources without infrastructure work.
ThoughtSpot if non-technical users need search-based analytics without learning SQL or notebooks.
My final verdict
During testing, I saw clear patterns in how notebook platforms handle different workloads. Deepnote and Observable delivered strong collaboration features, while JupyterLab and Databricks Notebooks handled technical depth and scale better. Mode and Count focused on specific workflows like SQL reporting or visual organization, and Google Colab gave teams free compute for experimentation.
Julius bridges the gap between natural language tools like ThoughtSpot and technical notebooks like Deepnote. You get direct answers through conversational queries, but you also see the analysis steps in a Notebook view that you can review, edit, and reuse.
I've found this dual approach works better when teams have both business users who want quick charts and technical users who need to verify the logic or build on earlier work, without forcing everyone into separate tools or workflows.
Want a Hex alternative without notebook complexity? Try Julius
When you compare Hex competitors, the biggest difference often comes down to cost predictability and setup speed. Julius eliminates per-seat pricing and compute overages with flat monthly rates, and you get charts from natural language questions without configuring notebook environments or managing infrastructure.
Here’s how Julius helps:
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
Catch outliers early: Julius highlights suspicious values and metrics that throw off your results, so you can make confident business decisions based on clean and trustworthy data.
Recurring summaries: Schedule analyses like weekly revenue or delivery time at the 95th percentile and receive them automatically by email or Slack.
Smarter over time: With each query, Julius gets better at understanding how your connected data is organized. It learns where to find the right tables and relationships, so it can return answers more quickly and with better accuracy.
One-click sharing: Turn a thread of analysis into a PDF report you can pass along without extra formatting.
Direct connections: Link your databases and files so results come from live data, not stale spreadsheets.
Frequently asked questions
Is Hex good for non-technical users?
Hex doesn’t work well for non-technical users because creating an analysis requires SQL or Python knowledge. Business users can view shared dashboards, but can’t build their own queries without coding skills. Tools like Julius or ThoughtSpot work better for non-technical teams since they accept plain English questions instead of code.
Can Hex handle real-time data analysis?
Yes, Hex connects to live databases like Postgres, Snowflake, or BigQuery, but you have to re-run queries manually to see fresh data. Dashboards won't refresh on their own, so it works for quick checks when you need them, but not for constant monitoring the way dedicated real-time tools do.
