Mode Analytics vs Hex AI Notebooks: A Practical Comparison for Data Teams

Choose Mode Analytics if your team is SQL-centric with structured reporting needs and stakeholder-facing dashboards. Choose Hex AI Notebooks if you need complex Python workflows, AI-assisted analysis throughout, and the ability to publish interactive data apps. This comparison covers query handling, AI integration, collaboration features, and performance differences between both platforms.

What is Mode Analytics?

Mode Analytics is a collaborative analytics platform that combines SQL queries, Python/R notebooks, and interactive visualizations in a single workspace. Originally focused on SQL-first analysis, Mode provides a structured environment where analysts can write queries, build reports, and share findings with stakeholders.

The platform organizes work into three main components: SQL reports, Python/R notebooks, and dashboards. SQL queries serve as the foundation, with results flowing into visualizations or passing data to code cells for further analysis.

What is Hex AI Notebooks?

Hex AI Notebooks is a modern notebook environment that combines SQL, Python, and visual analytics with AI-powered features. Hex takes a notebook-first approach, treating each analysis as a “project” that can include multiple cells, visualizations, and even applications.

Hex distinguishes itself through its “magic” commands, real-time collaboration, and the ability to publish interactive data apps directly from notebooks. The platform integrates AI assistance directly into the workflow, helping with code generation and debugging.

Query Language and Data Handling

Mode Analytics SQL Approach

Mode uses a SQL-first methodology where queries are first-class citizens. You build analyses by writing SQL that pulls data from connected warehouses, then visualize results or pass them to Python/R cells.

-- Mode Analytics SQL Example
SELECT 
    date_trunc('month', order_date) as month,
    category,
    COUNT(DISTINCT customer_id) as unique_customers,
    SUM(revenue) as total_revenue
FROM orders
WHERE order_date >= '2025-01-01'
GROUP BY 1, 2
ORDER BY 1, 2

Results can be visualized directly in Mode’s report builder or passed to a Python cell:

# Mode Analytics Python Cell
import pandas as pd
import matplotlib.pyplot as plt

# Access query results from previous SQL cell
results = _  # Mode passes SQL results as DataFrame

monthly_trend = results.groupby('month')['total_revenue'].sum()
monthly_trend.plot(kind='bar', title='Monthly Revenue Trend')
plt.tight_layout()

Hex AI Notebooks Approach

Hex uses a more fluid approach where SQL, Python, and markdown coexist in the same notebook. You can write SQL cells alongside Python code without explicit data passing—Hex handles the data flow automatically.

# Hex AI Notebooks - Using SQL directly in Python cells
from hextools import sql

# Direct SQL query that returns a DataFrame
df = sql("""
    SELECT 
        date_trunc('month', order_date) as month,
        category,
        COUNT(DISTINCT customer_id) as unique_customers,
        SUM(revenue) as total_revenue
    FROM orders
    WHERE order_date >= '2025-01-01'
    GROUP BY 1, 2
    ORDER BY 1, 2
""")

# Immediate Python analysis
df.groupby('month')['total_revenue'].sum().plot(kind='bar')

Hex also supports “app mode” where you can create interactive parameter-driven visualizations:

# Hex interactive parameters
from hextools import param

category_filter = param("Select Category", 
                       options=df['category'].unique(),
                       default='Electronics')

filtered = df[df['category'] == category_filter]
filtered.plot(x='month', y='total_revenue')

AI Integration and Assistance

Mode’s AI Features

Mode has integrated AI capabilities focused on query assistance and report generation. The AI can help suggest SQL optimizations, generate visualizations, and assist with Python analysis code.

# Mode AI-assisted Python analysis
# Type "/" to access AI commands
# AI can suggest: visualizations, transformations, statistical tests

Hex’s AI Features

Hex provides deeper AI integration. The AI assistant can generate complete SQL queries from natural language, write Python transformation code, debug errors in cells, and suggest visualizations based on data shape.

# Hex AI - Generate analysis from description
# Type "/ai" and describe what you want
# Example: "Create a cohort analysis showing retention by signup month"
# AI generates the complete SQL and Python code

Collaboration and Publishing

Mode Collaboration

Mode excels at structured reporting. You create reports with defined visualizations, arrange them on a canvas, and share via links or embedded in dashboards. The review workflow includes comments and version history.

# Mode API - Schedule report delivery
mode publish report_id=abc123 \
  --schedule "0 9 * * 1" \
  --recipients analyst@company.com \
  --format pdf

Hex Publishing

Hex offers more flexible publishing options. You can publish notebooks as static reports, interactive data apps, or API endpoints. The “app” feature allows creating parameter-driven interfaces:

# Hex - Create an interactive app from notebook
# User selects parameters (date range, filters)
# Notebook re-runs with new parameters
# Published as shareable web app

Performance and Scalability

Both platforms run queries directly against your data warehouse, so performance depends on your underlying data infrastructure. Key differences:

When to Choose Each Platform

Choose Mode Analytics if your team is SQL-centric with straightforward reporting needs, you need structured report layouts with consistent branding, stakeholder-facing dashboards are a primary use case, or your workflow follows query → visualize → share.

Choose Hex AI Notebooks if your analysis involves complex Python workflows, you need AI assistance throughout the analysis, interactive parameter-driven apps are valuable, or your workflow is exploratory with iterative refinement.

Practical Example: Customer Churn Analysis

Here’s how each platform handles a typical customer churn analysis:

Mode approach:

1. Write SQL to pull customer metrics from warehouse
2. Pass to Python cell for churn scoring calculation
3. Create visualizations in Mode’s builder
4. Build dashboard with multiple charts

Hex approach:

1. Write SQL cell with customer data
2. Write Python cell for scoring (can use sklearn)
3. Immediately visualize with pandas/matplotlib
4. Add interactive parameter for threshold adjustment
5. Publish as app for stakeholders to explore

Many organizations use both—Mode for formal reporting and Hex for ad-hoc analysis and data apps.

Related Reading

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