Deepnote

Name: Deepnote
Brand: Deepnote
Rating: 4.5 (381 reviews)

Data workspace where agents and humans work together.

Est 2019|Valuation $100M|Raised $23.8M

LinkedIn 13K|Web traffic 149K|FTEs 33|HQ San Francisco, California, US

MarketsSelf-Service Analytics

Published Sep 26, 2026

01Common complaints

1. You primarily need an enterprise lakehouse platform rather than a collaborative notebook workspace. - For large-scale data engineering, streaming, and production ML pipelines, Databricks or similar lakehouse platforms may be a better match. 2. You require clearly documented on-prem/self-hosted deployments and deep control of infrastructure. - Some competitors explicitly document on-prem options; Deepnote’s public materials emphasize enterprise deployments but may require contractual arrangements for fully self-hosted deployments. 3. You have extremely large datasets and strict performance requirements. - Public reviews note performance degradation with larger datasets; a POC is essential before committing. 4. You need advanced stakeholder-facing app experiences as the primary output at scale. - Products focused on analytics app publishing may be stronger for polished, production-grade apps and governance at higher price tiers. 5. Budget constraints with many editors. - Per-editor pricing can become expensive at scale; alternatives include managed Jupyter with lower compute cost but higher ops burden or selective-seat strategies.

Deal disqualifiers (poor fit)

Teams that require lakehouse-first capabilities for very large-scale pipelines where Databricks or similar platforms are the organizational standard.
Organizations that require fully self-hosted or on-prem notebooks without custom contracting and clear deployment terms.
Teams with strict performance SLAs for very large datasets without conducting a proof-of-concept to validate scaling.

Known limitations from reviews and community feedback

AI assistant may lose context across a notebook and generate excessive or unhelpful code blocks.
Performance can degrade with larger datasets or more complex notebooks.
Anecdotal reports of GPU provisioning or machine startup delays on lower-tier plans were observed in community discussions.

Vendor-acknowledged limitations and roadmap considerations

A single authoritative public roadmap with dated commitments was not located in the sources reviewed. Buyers should rely on contractual terms and documentation for commitments rather than public roadmap statements.

API and productionization constraints

Deepnote provides features to schedule and run notebooks and to expose notebook functionality, but details about API limits, SLAs, and production hardening vary by plan and should be validated during technical evaluation.

02Why they buy

Strengths that appear validated by user feedback

Real-time collaboration is frequently cited as a differentiator compared with classic notebook tools.
Python and SQL interoperability and convenient context switching are noted positively in reviews.
The managed execution environment reduces time spent on local environment setup and administration compared with self-hosted notebook infrastructure.

Differentiation likely driven by marketing (needs validation)

Some vendor marketing claims about rapid capability improvements or broad adoption should be treated as vendor statements and validated through POC and reference checks.

Areas where competitors have clear advantages

Databricks: broader distributed processing and enterprise platform breadth for large-scale data engineering.
Hex: stronger app publishing workflows and stakeholder-facing artefact tooling in some plans.
Datalore: clearer on-prem options and JetBrains ecosystem alignment.

03Field reports

Review platform coverage (availability)

G2: Available; shows a rating and review count at the time of crawl.
Gartner Peer Insights: Product listing exists; review volume varied at the time of crawl.
TrustRadius: No directly verifiable TrustRadius page was retrieved in the sources available to this report; third-party aggregators claim coverage but direct verification was not possible here.

Positive customer sentiment (examples)

From public review excerpts and community discussions: - Reviewers report that Deepnote enables analysts and data scientists to focus on analysis rather than managing collaborative development environments. - Users cite effective AI features and smooth Python–SQL integration, noting the ease of moving between SQL and Python and AI-assisted graph generation. - Community discussion notes practical use for sharing and editing notebooks collaboratively and running Python on server-backed compute. Vendor-selected testimonials emphasize faster time to visualizations and quicker setup and collaboration; such vendor-supplied quotes should be treated as marketing-selected examples rather than independent samples.

Negative feedback / complaints (examples)

From public review excerpts and community discussion: - AI limitations: Users report the AI assistant may lose context across a notebook and may not track variables defined in cells that are far apart. - AI output quality: Some reviewers note the AI can generate low-value or excessive blocks of code. - Performance concerns: Users note lag or slowdowns as notebooks grow or datasets scale. - Reliability/compute availability: Community posts include anecdotal reports of GPU provisioning or machine startup delays on lower-tier plans; these are isolated anecdotes and not necessarily representative.

Why customers choose Deepnote over alternatives (patterns)

Common decision drivers cited in reviews and discussions include:

Real-time collaboration and easier sharing compared with classic notebook tools.
Blending SQL and Python workflows in a single environment for warehouse-centric teams.
Reduced environment management burden due to managed execution and curated setups.

Why some customers might leave / churn drivers (inferred from complaints)

Recurring issues that can drive churn include:

AI assistant quality (context loss, noisy or unhelpful code generation).
Performance or scaling limitations on large datasets.
Reliability and compute provisioning issues on lower-tier accounts.

Example case study: VantAI

Deepnote’s customer story for VantAI emphasizes bridging no-code GUI needs for non-coders with full programming for engineers, interactive plotting, connected datasets, and rapid environment setup and use of input widgets. Critical evaluation: - This is a vendor-published case study with a favorable quote from the customer’s leadership; it does not provide independently verified ROI metrics, baseline comparisons, or quantified time-to-value measurements. - The case study provides directional evidence of perceived workflow benefits but should not be treated as a controlled measurement.

General note on methodology gaps

Many notebook vendors publish customer stories that emphasize speed and collaboration. For procurement-grade validation, typical buyer actions include:

Conducting a proof of concept (POC) with representative data sizes and concurrent users.
Requesting a security review package (SOC 2 report, subprocessor list).
Building a cost model that includes expected compute usage and potential overage scenarios.

04Best fit

Company sizes

Inferred fit based on pricing, collaboration emphasis, and review signals:

Small to mid-market data teams (from small groups up through mid-sized teams of analysts/data scientists) that need collaboration and centralized notebooks.
Enterprise customers are a possible fit when security controls and contractual terms are required, though enterprise procurement typically becomes a sales-led process.

Industries / verticals

Evidence suggests adoption in:

Tech-enabled companies using modern cloud data stacks (Snowflake, BigQuery, and similar platforms).
Life sciences and biotech (customers in this sector are referenced in vendor materials).
Education and universities (vendor claims of academic adoption are presented as vendor statements and were not independently verified).

Roles and personas

Decision makers: Head of Data/Analytics, Director of Data Science, Data Platform/Engineering leadership, and Security/IT for compliance reviews.
End users: Analysts (SQL-first), data scientists (Python/R), and analytics engineers.

Qualification criteria (good fit)

Need for real-time collaboration on notebooks.
Multiple stakeholders reviewing analyses using comments, snapshots, and share links.
Warehouse-first environment with frequent SQL usage.
A desire to avoid managing JupyterHub infrastructure.

05The pitch

What the product does (core value proposition)

Deepnote is a cloud-based, collaborative notebook/workspace for data analysis and data science that supports Python, SQL, and R, with an emphasis on real-time collaborative editing, sharing, and converting notebook work into stakeholder-facing artifacts (apps/dashboards) and scheduled/runnable workflows. It is commonly described as an evolution of Jupyter-style notebooks with additional support for SQL, AI features, and concurrent editing, targeting exploratory analysis and lightweight operationalization (scheduling, background runs, deploy-as-API). Key product capabilities highlighted in official materials include: - Real-time collaboration (Google Docs-style co-editing), comments, sharing links, permissions. - Multi-language notebooks (Python/SQL/R) plus no-code visualization. - Integrations with major warehouses and data sources (for example, Snowflake and BigQuery) and many connectors. - Scheduling/background execution, run history/snapshots for audit/debug. - AI-assisted analysis and coding features. - Options for larger-scale compute via Spark/Snowpark integrations. (Official product positioning and feature descriptions: Product overview and feature pages, and vendor documentation.)

Target market and market positioning

Deepnote’s positioning sits between: - Traditional notebooks (Jupyter, Colab) where collaboration/governance/ops are often add-ons. - Notebook-to-app analytics products (for example, Hex) focused on sharing and publishing analyses. - Enterprise lakehouse platforms (for example, Databricks) where notebooks are one surface within a broader data and ML platform. Primary fit signals (based on reviews and product design) include: - Data teams that need collaboration and reproducibility without heavy DevOps. - Analytics teams working directly with warehouses (SQL-first or mixed SQL/Python). - Organizations that need a single environment to iterate, review, and share outputs with stakeholders.

Get a comprehensive analysis of Deepnote including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →

Published Sep 26, 2026

01Common complaints

You primarily need an enterprise lakehouse platform rather than a collaborative notebook workspace.
- For large-scale data engineering, streaming, and production ML pipelines, Databricks or similar lakehouse platforms may be a better match.
You require clearly documented on-prem/self-hosted deployments and deep control of infrastructure.
- Some competitors explicitly document on-prem options; Deepnote’s public materials emphasize enterprise deployments but may require contractual arrangements for fully self-hosted deployments.
You have extremely large datasets and strict performance requirements.
- Public reviews note performance degradation with larger datasets; a POC is essential before committing.
You need advanced stakeholder-facing app experiences as the primary output at scale.
- Products focused on analytics app publishing may be stronger for polished, production-grade apps and governance at higher price tiers.
Budget constraints with many editors.
- Per-editor pricing can become expensive at scale; alternatives include managed Jupyter with lower compute cost but higher ops burden or selective-seat strategies.

Deal disqualifiers (poor fit)

Teams that require lakehouse-first capabilities for very large-scale pipelines where Databricks or similar platforms are the organizational standard.
Organizations that require fully self-hosted or on-prem notebooks without custom contracting and clear deployment terms.
Teams with strict performance SLAs for very large datasets without conducting a proof-of-concept to validate scaling.

Known limitations from reviews and community feedback

AI assistant may lose context across a notebook and generate excessive or unhelpful code blocks.
Performance can degrade with larger datasets or more complex notebooks.
Anecdotal reports of GPU provisioning or machine startup delays on lower-tier plans were observed in community discussions.

Vendor-acknowledged limitations and roadmap considerations

A single authoritative public roadmap with dated commitments was not located in the sources reviewed. Buyers should rely on contractual terms and documentation for commitments rather than public roadmap statements.

API and productionization constraints

Deepnote provides features to schedule and run notebooks and to expose notebook functionality, but details about API limits, SLAs, and production hardening vary by plan and should be validated during technical evaluation.

02Why they buy

Strengths that appear validated by user feedback

Real-time collaboration is frequently cited as a differentiator compared with classic notebook tools.
Python and SQL interoperability and convenient context switching are noted positively in reviews.
The managed execution environment reduces time spent on local environment setup and administration compared with self-hosted notebook infrastructure.

Differentiation likely driven by marketing (needs validation)

Some vendor marketing claims about rapid capability improvements or broad adoption should be treated as vendor statements and validated through POC and reference checks.

Areas where competitors have clear advantages

Databricks: broader distributed processing and enterprise platform breadth for large-scale data engineering.
Hex: stronger app publishing workflows and stakeholder-facing artefact tooling in some plans.
Datalore: clearer on-prem options and JetBrains ecosystem alignment.

03Field reports

Review platform coverage (availability)

G2: Available; shows a rating and review count at the time of crawl.
Gartner Peer Insights: Product listing exists; review volume varied at the time of crawl.
TrustRadius: No directly verifiable TrustRadius page was retrieved in the sources available to this report; third-party aggregators claim coverage but direct verification was not possible here.

Positive customer sentiment (examples)

From public review excerpts and community discussions:

Reviewers report that Deepnote enables analysts and data scientists to focus on analysis rather than managing collaborative development environments.
Users cite effective AI features and smooth Python–SQL integration, noting the ease of moving between SQL and Python and AI-assisted graph generation.
Community discussion notes practical use for sharing and editing notebooks collaboratively and running Python on server-backed compute.

Vendor-selected testimonials emphasize faster time to visualizations and quicker setup and collaboration; such vendor-supplied quotes should be treated as marketing-selected examples rather than independent samples.

Negative feedback / complaints (examples)

From public review excerpts and community discussion:

AI limitations: Users report the AI assistant may lose context across a notebook and may not track variables defined in cells that are far apart.
AI output quality: Some reviewers note the AI can generate low-value or excessive blocks of code.
Performance concerns: Users note lag or slowdowns as notebooks grow or datasets scale.
Reliability/compute availability: Community posts include anecdotal reports of GPU provisioning or machine startup delays on lower-tier plans; these are isolated anecdotes and not necessarily representative.

Why customers choose Deepnote over alternatives (patterns)

Common decision drivers cited in reviews and discussions include:

Real-time collaboration and easier sharing compared with classic notebook tools.
Blending SQL and Python workflows in a single environment for warehouse-centric teams.
Reduced environment management burden due to managed execution and curated setups.

Why some customers might leave / churn drivers (inferred from complaints)

Recurring issues that can drive churn include:

AI assistant quality (context loss, noisy or unhelpful code generation).
Performance or scaling limitations on large datasets.
Reliability and compute provisioning issues on lower-tier accounts.

Example case study: VantAI

Critical evaluation:

This is a vendor-published case study with a favorable quote from the customer’s leadership; it does not provide independently verified ROI metrics, baseline comparisons, or quantified time-to-value measurements.
The case study provides directional evidence of perceived workflow benefits but should not be treated as a controlled measurement.

General note on methodology gaps

Many notebook vendors publish customer stories that emphasize speed and collaboration. For procurement-grade validation, typical buyer actions include:

Conducting a proof of concept (POC) with representative data sizes and concurrent users.
Requesting a security review package (SOC 2 report, subprocessor list).
Building a cost model that includes expected compute usage and potential overage scenarios.

04Best fit

Company sizes

Inferred fit based on pricing, collaboration emphasis, and review signals:

Small to mid-market data teams (from small groups up through mid-sized teams of analysts/data scientists) that need collaboration and centralized notebooks.
Enterprise customers are a possible fit when security controls and contractual terms are required, though enterprise procurement typically becomes a sales-led process.

Industries / verticals

Evidence suggests adoption in:

Tech-enabled companies using modern cloud data stacks (Snowflake, BigQuery, and similar platforms).
Life sciences and biotech (customers in this sector are referenced in vendor materials).
Education and universities (vendor claims of academic adoption are presented as vendor statements and were not independently verified).

Roles and personas

Decision makers: Head of Data/Analytics, Director of Data Science, Data Platform/Engineering leadership, and Security/IT for compliance reviews.
End users: Analysts (SQL-first), data scientists (Python/R), and analytics engineers.

Qualification criteria (good fit)

Need for real-time collaboration on notebooks.
Multiple stakeholders reviewing analyses using comments, snapshots, and share links.
Warehouse-first environment with frequent SQL usage.
A desire to avoid managing JupyterHub infrastructure.

05The pitch

What the product does (core value proposition)

Key product capabilities highlighted in official materials include:

Real-time collaboration (Google Docs-style co-editing), comments, sharing links, permissions.
Multi-language notebooks (Python/SQL/R) plus no-code visualization.
Integrations with major warehouses and data sources (for example, Snowflake and BigQuery) and many connectors.
Scheduling/background execution, run history/snapshots for audit/debug.
AI-assisted analysis and coding features.
Options for larger-scale compute via Spark/Snowpark integrations.

(Official product positioning and feature descriptions: Product overview and feature pages, and vendor documentation.)

Target market and market positioning

Deepnote’s positioning sits between:

Traditional notebooks (Jupyter, Colab) where collaboration/governance/ops are often add-ons.
Notebook-to-app analytics products (for example, Hex) focused on sharing and publishing analyses.
Enterprise lakehouse platforms (for example, Databricks) where notebooks are one surface within a broader data and ML platform.

Primary fit signals (based on reviews and product design) include:

Data teams that need collaboration and reproducibility without heavy DevOps.
Analytics teams working directly with warehouses (SQL-first or mixed SQL/Python).
Organizations that need a single environment to iterate, review, and share outputs with stakeholders.

Get a comprehensive analysis of Deepnote including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →