Deepnote Review: The Ultimate Collaborative Data Science Notebook

About Deepnote

Deepnote is a state-of-the-art, cloud-based collaborative notebook designed to streamline data science and analytics workflows. Its primary purpose is to empower teams to work together seamlessly on data projects, enabling both technical and non-technical users to access, analyze, and interpret data efficiently.

Primary Purpose

Deepnote serves as an AI-powered analytics and data science notebook, aimed at simplifying the data analysis process for teams. By integrating advanced AI capabilities, Deepnote enables users to conduct data queries, analyses, and interpretations without requiring extensive coding skills. Its innovative features include:

• AI Copilot: An integrated AI service that offers code suggestions and predictive typing to enhance coding efficiency and reduce errors.
• Multi-language Support: Users can work with various programming languages, such as Python, SQL, and R, within a single interface, allowing for seamless transitions and comprehensive data manipulation.
• Interactive Dashboards and Data Apps: Deepnote turns notebooks into powerful data applications and visual dashboards, making data insights accessible and actionable.

Target Audience

Deepnote is primarily targeted at data professionals, including data analysts, data scientists, and business intelligence teams. However, it also caters to non-technical users, such as sales representatives and managers, who wish to engage with data without the barrier of complex coding. The platform promotes a collaborative culture where individuals from diverse backgrounds can contribute to data projects, enhancing overall productivity and innovation.

Problems It Solves

Deepnote addresses several critical challenges faced by data teams:

1. Complexity of Data Analysis: Traditional data analysis tools often require extensive coding knowledge and are prone to errors, particularly when dealing with unfamiliar data structures. Deepnote’s AI Copilot mitigates this issue by providing intelligent code suggestions, allowing users to focus on insights rather than syntax.
2. Collaboration Barriers: Many data tools do not facilitate easy collaboration among team members. Deepnote’s design fosters a collaborative environment where users can share notebooks and insights in real-time, ensuring that everyone is aligned and informed.
3. Accessibility of Data Insights: Non-technical team members often struggle to access and interpret data. With Deepnote’s intuitive interface and AI-driven analyses, even those without a coding background can derive valuable insights, empowering decision-making across the organization.
4. Integration with Data Sources: Deepnote supports a wide range of data sources, including Snowflake, BigQuery, and various CSV files, simplifying the process of data integration and analysis. This flexibility allows teams to work with their preferred data stacks seamlessly.

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Exploring Deepnote: Usability and Features

Deepnote is revolutionizing the way data science and analytics are approached through its cloud-based collaborative notebook environment. Designed for both novice and experienced data professionals, Deepnote emphasizes usability and productivity with a range of impactful features.

Usability

One of the standout aspects of Deepnote is its commitment to user-friendliness. The platform is fully browser-based, which eliminates the need for complex installations and setups that often hinder new users. Students and educators can dive right into data science without the steep learning curve associated with traditional data environments. The interface is intuitive, allowing users to easily navigate through various functionalities, collaborate in real-time, and visualize data effortlessly.

Deepnote also provides a free Education plan for students, teachers, and researchers, making it accessible for those looking to enhance their data science skills without financial barriers. This plan includes most features found in the Team plan, which supports collaborative learning and project development.

Key Features

1. Collaborative Environment: Deepnote encourages teamwork by allowing multiple users to work on the same notebook simultaneously. This feature is particularly beneficial in educational settings and team projects, where collaboration is essential for success.
2. AI-Powered Assistance: Leveraging AI capabilities, Deepnote incorporates Deepnote AI, which aids users by offering suggestions and generating code based on plain English instructions. This functionality allows users, regardless of their coding proficiency, to achieve complex analytical tasks quickly and effectively.
3. Versatile Data Integration: Deepnote seamlessly connects with a variety of data sources, including Snowflake, BigQuery, and CSV files, making it versatile for data scientists who work with diverse datasets. This feature allows for smooth data importing and manipulation, helping users focus on analysis rather than data wrangling.
4. Interactive Visualizations: Users can create interactive visualizations and dashboards without extensive coding knowledge. This no-code approach empowers users to present their findings compellingly and understandably, enhancing the storytelling aspect of data analysis.
5. Notebook as a Knowledge Base: Deepnote notebooks serve not just as workspaces but also as repositories of knowledge. Users can document their processes, share insights, and store data-related information all in one place, facilitating better knowledge retention and collaboration.
6. Support for Multiple Programming Languages: Deepnote supports Python, SQL, and R, enabling data professionals to utilize their preferred programming languages in a single, integrated environment. Users can switch between languages as needed, making the platform adaptable to various project requirements.

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Why Choose Deepnote Over Competitors?

In today’s rapidly evolving landscape of data science and analytics, choosing the right platform can significantly impact your productivity and collaboration. Among the many options available, Deepnote emerges as a standout choice for professionals seeking a modern, collaborative data workspace.

Key Advantages

1. Seamless Collaboration and Real-Time Editing: Deepnote is built for collaboration. Unlike traditional notebooks, it allows multiple users to work on the same project simultaneously, making it easier for teams to share insights and iterate quickly.
2. AI-Powered Features: Deepnote includes intelligent code completion and an AI assistant that generates and fixes code as needed, boosting productivity and lowering the barrier for less experienced coders.
3. Jupyter Compatibility: Fully compatible with Jupyter notebooks, users can import existing projects with ease.
4. Enhanced Data Integration: The platform connects with various data sources, enabling users to work with data wherever it resides.
5. User-Friendly Interface: Deepnote’s intuitive interface simplifies navigation for all users.
6. Powerful Visualization Tools: Robust interactive dashboards make sharing insights effortless.
7. Cloud-Based Accessibility: Users can access their work from anywhere, fostering mobility and better collaboration.
8. Ideal for Machine Learning Projects: Its advanced features make it perfect for research and experimentation in machine learning and AI.

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Unlocking the Power of Deepnote: Ideal User Groups and Use Cases

User Group	Description	Use Cases
Beginners	Ideal for individuals new to data analysis and programming.	Learning data science basics, exploring datasets, and getting hands-on coding experience.
Data Analysts	Suited for data analysts seeking collaborative tools for data exploration and reporting.	Creating dashboards, performing exploratory data analysis, and generating business insights.
Data Scientists	Perfect for data scientists who need advanced tools for machine learning and data manipulation.	Building predictive models, conducting data experiments, and visualizing complex datasets.
Students	Useful for students working on group projects or research, providing an accessible platform to learn.	Collaborative projects, coursework, and experimenting with data in a guided manner.
Small to Medium Businesses (SMBs)	Great for SMBs that require a cost-effective solution for data analytics and team collaboration.	Streamlining data processes, reporting, and enhancing team productivity without heavy investment.
Enterprises	Beneficial for larger organizations needing robust data analysis capabilities and team collaboration.	Scaling data operations, integrating with existing data infrastructures, and ensuring data governance.

Features

A B Testing for product insights

Deepnote’s A/B testing functionality allows teams to evaluate the effectiveness of different product variations through a structured approach. Businesses can create experiments by dividing their user base into different groups, each exposed to a distinct variant of a webpage or app feature. Key metrics tracked during A/B testing include:

• Conversion Rate: Measures the percentage of visitors who take a desired action, such as signing up or making a purchase.
• Retention Rate: Assesses how many users return after their initial interaction with the product.

Deepnote’s A/B Testing template simplifies the process of running and analyzing tests, providing easy visualization of key metrics. By integrating significance testing, teams can make informed decisions based on statistically significant results, ensuring that product enhancements are backed by solid data.

Monitoring machine learning models

For businesses utilizing machine learning, Deepnote provides essential tools for monitoring model performance post-deployment. Key metrics that can be continuously tracked include:

• Accuracy: The proportion of true results among the total number of cases examined.
• Precision and Recall: Metrics that help evaluate the quality of the model’s predictions.
• F1-score: A balance between precision and recall, useful for assessing the model’s accuracy.
• AUC-ROC: A metric that evaluates the model’s performance across different thresholds.

Deepnote emphasizes the importance of monitoring for data drift and concept drift, which can impact model accuracy over time. It also allows tracking of latency (time taken to make predictions) and throughput (number of predictions over time), ensuring that the system meets performance requirements.

Collaborative knowledge management

Deepnote fosters a collaborative environment where all team members can access and utilize data insights effectively. Notebooks serve as a central knowledge base, allowing teams to document data assets and track their lineage. Key features include:

• Workspaces: Enable teams to control access to projects and customize workflows according to specific roles, promoting a culture of collaboration.
• Data Lineage and Cataloging: Help organizations discover and map data flowing through their pipelines, ensuring consistent metrics definitions across analyses.

This collaborative structure empowers all employees, regardless of their technical background, to become self-sufficient data analysts, leveraging Deepnote’s tools for business intelligence and exploratory analysis.

Overall, Deepnote’s reporting capabilities, from A/B testing to machine learning monitoring and collaborative knowledge management, provide businesses with the tools necessary to make data-driven decisions effectively and efficiently. By utilizing these features, companies can enhance their reporting practices, drive better outcomes, and maintain a competitive edge in their respective markets.

Location

Locations and Branches

Location Type	Address	Country
Headquarters	575 Market Street, Office 412, 4th Floor, San Francisco, California, 94105	United States
Branch	Prague (specific address not provided)	Czech Republic

History and Team

Year Founded

Deepnote was founded in 2019.

Number of Employees

Deepnote currently has approximately 15-20 employees (as of the latest data).

Team

Name	Role
Jakub Jurovych	Co-Founder and CEO
Petr Fejfar	Co-Founder and CTO
Petr Burian	Co-Founder and VP of Engineering

Deepnote was established in 2019 in San Francisco, California. The company is recognized for its collaborative data science notebook, which enhances the productivity of data science teams. The founding team includes Jakub Jurovych, Petr Fejfar, and Petr Burian, each contributing their expertise to the company’s growth and success.

Pricing

Pricing Plans

Plan	Cost	Editors	Viewers	Projects	Deepnote AI Features	Other Features
Free	Free forever	Up to 3	Unlimited	Up to 5	AI code completion, 100 calls per month for Auto AI, Generate, Edit, Explain code	7-day revision history, Unlimited Basic machines (5 GB RAM, 2 vCPU)
Pro	$9 per editor/month (billed yearly)	1	Unlimited	Unlimited	Unlimited Deepnote AI, Background execution, Scheduled notebooks	$65 worth of CPU and GPU every month, More powerful machines, 7-day revision history
Team	$39 per editor/month (billed yearly)	Unlimited	Unlimited	Unlimited	Unlimited Deepnote AI, Background execution, Scheduled notebooks	$280 worth of CPU and $65 worth of GPU every month, 30-day revision history, Premium integrations
Enterprise	Custom (Talk to us)	Unlimited	Unlimited	Unlimited	All features in Team plus Unlimited AI features	Custom contract, Priority support, Dedicated success manager, Permission groups, SSO, Audit logs, Single-tenancy

Key Features Across All Plans:

• Collaboration: Real-time collaboration, commenting, public projects.
• Machines: Unlimited Basic machines, pay-as-you-go options, and custom machines.
• Security: SOC 2 Type II, encrypted data, HIPAA compliance, SSO options.
• Integrations: Various integrations with databases, file storage, and collaboration tools.

Funding and market

Deepnote Business Overview

Industry

Deepnote operates in the Data Science and Collaboration Software industry. It focuses on providing a cloud-based platform that allows teams and individuals to collaborate on data science projects using interactive programming notebooks.

Market

Deepnote’s market is primarily within the data science collaboration tools segment, which is part of the broader data analytics market.

• Market Size: The global data analytics market was valued at approximately $274 billion in 2020 and is projected to grow at a compound annual growth rate (CAGR) of 30% from 2021 to 2028.
• Market Share: While specific market share figures for Deepnote are not publicly available, it is positioned among other tools like Jupyter Notebooks, Google Colab, and Microsoft Azure Notebooks, competing for users in the data science community.

Funding

Deepnote has successfully raised a total of $23.8 million over two funding rounds. Below is a summary of the funding rounds:

Date	Funding Amount	Round Name	Investors
Jan 31, 2022	$20 million	Series A	Accel, Index Ventures, Credo Ventures, Y Combinator
Feb 06, 2020	$3.8 million	Seed	Accel, Index Ventures, Credo Ventures, Y Combinator

• The most recent funding round was a Series A round on January 31, 2022, where Deepnote raised $20 million.
• Key investors include Accel, Index Ventures, and Y Combinator.

Stocks

As of now, Deepnote is not a publicly traded company and does not have a stock ticker. It remains a private entity and has not yet pursued an initial public offering (IPO).

Sources

• Deepnote Funding & Investors – Tracxn
• Deepnote Stock Price, Funding, Valuation, Revenue & Financials – CB Insights
• Deepnote Company Profile – Crunchbase

Latest news

Latest Updates, News, and Highlights About Deepnote

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Reduced Machine Prices & Faster Notebooks

• Source: Deepnote Changelog – November 30, 2023
• Starting December 1, Deepnote announced a significant reduction in pay-as-you-go rates across all machines, making the platform more affordable for users. This update comes just in time for the holiday season, enabling users to upgrade to more powerful machines without additional financial strain. The update also includes supercharged notebook loading speeds, enhancing the overall user experience.

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Deepnote Comes Out of Beta

• Source: Big Data Wire – May 27, 2022
• Deepnote officially launched version 1.0, making its collaborative data science notebooks available to data teams worldwide. The platform addresses pain points such as collaboration, connectivity, and productivity in traditional data science workflows. Features include real-time collaboration, seamless integrations with tools like Snowflake and BigQuery, and productivity enhancements like autocomplete and scheduling. Companies like Gusto have adopted Deepnote for centralizing analytics efforts.

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Flexible App Layout and New Features

• Source: Deepnote Changelog – February 20, 2025
• Deepnote introduced a redesigned app builder with a drag-and-drop interface, allowing users to create custom layouts independent of notebook structures. This update empowers users to prioritize and present data insights effectively. The Pro Plan for individuals, starting at $9/month, was also unveiled, providing features like unlimited AI functionalities, background code execution, and access to more powerful CPUs/GPUs.

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Acquisition of Hyperquery

• Source: Deepnote Blog – July 29, 2024
• Deepnote announced the acquisition of Hyperquery, a startup specializing in data analytics. This strategic move aims to unify the data analytics market by integrating Hyperquery’s capabilities into Deepnote’s AI-powered data notebook. This acquisition positions Deepnote as a comprehensive solution in the analytics and data science space.

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Beyond AI Chatbots: Engagement With Deepnote AI

• Source: Deepnote Blog – April 3, 2024
• Deepnote shared insights into how they tripled user engagement with their AI-powered features. The platform continues to innovate in leveraging AI for enhanced user productivity and collaboration in data science.

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These updates highlight Deepnote’s consistent focus on innovation, affordability, and enhancing the user experience in the data science and analytics ecosystem. For additional details, please refer to the provided links to the original sources.

Search Trends

Search Volume Analysis for Deepnote

Search Volume Data Table

Keyword	Search Volume	Competition	Competition Index	Low Top of Page Bid	High Top of Page Bid	CPC
Deepnote	2900	LOW	9	5.39	16.08	18.99
Deepnote analytics	None	None	None	None	None	None
Deepnote data science notebook	None	None	None	None	None	None
Deepnote features	None	None	None	None	None	None
Deepnote team collaboration	None	None	None	None	None	None

Reasons Behind the Trend

The search volume of 2900 for the keyword “Deepnote” can be attributed to recent developments and announcements by the company, which have garnered public and industry attention:

1. Acquisition of Hyperquery:
   • Deepnote recently acquired Hyperquery, a startup in the data analytics space, enabling organizations to better utilize AI and data analytics. This announcement on July 29, 2024, was widely covered in the media.
   • Relevant Source: Business Wire – Deepnote Acquires Hyperquery
2. Product Updates:
   • Deepnote introduced new features including:
     • A Pro plan catering to professional and enterprise users.
     • A drag-and-drop interface for app layouts, enhancing user experience.
     • New GPU offerings for computational tasks.
   • Relevant Sources:
     • Deepnote Changelog
     • Deepnote Blog

These key developments have likely driven the heightened search volume and interest in the company.

Review

Customers

Here is a detailed list of notable companies and organizations that are utilizing Deepnote software, along with examples of how they leverage the platform in their operations:

• SoundCloud: Integrated Deepnote into their workflows, expanding its use from 30 to 100 users within six months, facilitating collaboration among large teams.
• Homa: Describes adopting Deepnote as a “game-changer,” highlighting its AI capabilities in streamlining data processes and enhancing innovation.
• Gusto: Transitioned to Deepnote to eliminate inefficiencies of manual processes and noted improved data integration and collaboration.
• Statsig: Uses Deepnote for collaborative analytics, enabling effective teamwork on data-driven projects.
• Marshmallow: Utilizes Deepnote for creating a collaborative environment where metrics are defined and aligned with immediate feedback.
• DAZN: Leverages Deepnote to improve data analysis capabilities, enhancing team collaboration and insight sharing.
• Floryn: Accelerated their analysis workflows significantly, showcasing Deepnote’s efficiency in turning around results in a day.
• Webflow: Enjoys the flexibility of performing analyses in the programming language of choice, making the platform accessible to team members.
• Ramp: Their Director of Engineering praised Deepnote’s delightful user experience, leading to increased satisfaction and productivity.
• Papaya: Revolutionized their data analysis workflow, delivering insights faster and more collaboratively.
• Glasswall: Highlights the collaborative aspect of Deepnote, enabling free sharing of work and rapid feedback.
• Foodpanda: Uses Deepnote for efficient data analysis, streamlining operations and improving collaborative efforts.
• Hedge Fund: Noted a significant improvement in collaboration on data projects after adopting Deepnote.
• Slido: Appreciates Deepnote’s ability to create a collaborative layer for defining and aligning metrics with immediate feedback.
• ezCater: Facilitates a more integrated and collaborative approach to business intelligence efforts.

Deepnote is widely recognized by these organizations for enhancing collaboration, streamlining workflows, and improving the efficiency of data analysis. For more information, visit Deepnote Customers.

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Alternatives

Below is a comparison table of alternative software options to Deepnote, including their features, pricing, and target audiences:

Software	Features	Pricing	Target Audience
Jupyter Notebook	Open-source, interactive computing, supports multiple languages, extensive library support.	Free	Data scientists, educators, and researchers.
Google Colab	Cloud-based, free GPU access, integrates well with Google Drive, supports Python.	Free (Pro Plan: $9.99/month)	Students, individual developers, and researchers.
Apache Zeppelin	Multi-purpose notebook, supports collaboration, and integrates with big data tools like Spark.	Free (Open-source)	Enterprises working with big data and analytics teams.
Mode Analytics	SQL-based collaborative platform, integrates with Python and R, visualization features.	Starts at $29/user/month	Data analysts and business intelligence teams.
Hex	Modern collaborative data workspace, SQL/Python integration, real-time collaboration, and versioning.	Custom pricing	Data-driven organizations and analytics teams.
Observable	JavaScript-based notebook, focuses on data visualization, real-time collaboration features.	Free (Pro: $15/user/month)	Frontend developers and data visualization teams.
Dataiku	End-to-end data science platform, supports ML and AI models, strong collaboration tools.	Custom pricing	Enterprises and advanced analytics teams.
Kaggle Notebooks	Cloud-based, free GPU/TPU access, integrated with Kaggle datasets, Python/R support.	Free	Data science enthusiasts and competition participants.

Sources: Information was gathered from official pages like Jupyter and Google Colab documentation, among others.

Comparing FlowHunt with Deepnote

FlowHunt and Deepnote share key functionalities such as AI-driven capabilities, collaboration tools, and data integration. While Deepnote uses GPT-4 for code generation and analysis, FlowHunt offers intuitive no-code AI workflows and chatbot building. Users can achieve similar outcomes in FlowHunt by leveraging its modules to automate tasks and integrate knowledge, making it a more accessible and versatile option for both technical and non-technical users.