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

01Competitive Landscape

Narrative analysis

Deepnote vs Databricks - Vendor-authored comparisons frame Databricks as better suited for very large-scale distributed processing and lakehouse architectures, while positioning Deepnote as oriented toward collaboration and insight generation. Treat vendor-authored comparisons as inherently biased; however, market segmentation is consistent with Databricks being a lakehouse-first platform and Deepnote focusing on collaborative notebook workflows. Deepnote vs Hex - Both use per-editor pricing and emphasize notebook-to-artifact workflows. Published pricing suggests differences at the Team tier; feature parity depends on governance, compute needs, and app-publishing requirements. Deepnote vs Datalore - Datalore documents on-prem/self-hosting options and a JetBrains ecosystem alignment that may appeal to regulated customers. Deepnote emphasizes enterprise deployments and custom contracting for some enterprise needs; public documentation on fully self-hosted models was less explicit in the sources reviewed.

	Deepnote	Hex	JetBrains Datalore
Starting Price	Free tier; published per-editor and team pricing available from the vendor	Community/free option; published professional per-editor pricing available from the vendor	Published per-user/team pricing available from the vendor; public references note lower annual rates versus monthly in some cases
Suitable For	Collaborative notebooks mixing SQL/Python/R; teams that need sharing and managed compute	Notebook and data apps for stakeholder consumption; teams focused on published analytics artifacts	Teams that prefer JetBrains ecosystem and seek collaborative notebooks/reports with on-prem deployment options
Key Strength	Real-time collaboration and integrated scheduling and warehouse integrations	App/report publishing and stakeholder-facing analytics workflows	Cloud and on-prem deployment options; integration with JetBrains tooling
Key Weakness	AI context limitations; performance concerns on large datasets; enterprise governance features may require Enterprise-tier offerings	Higher listed Team-tier pricing in some plans; may be heavier than necessary for simple notebook use cases	Pricing and credit models can be complex; may be less product-led for analytics app publishing

01Competitive Landscape

Narrative analysis

Deepnote vs Databricks

Vendor-authored comparisons frame Databricks as better suited for very large-scale distributed processing and lakehouse architectures, while positioning Deepnote as oriented toward collaboration and insight generation. Treat vendor-authored comparisons as inherently biased; however, market segmentation is consistent with Databricks being a lakehouse-first platform and Deepnote focusing on collaborative notebook workflows.

Deepnote vs Hex

Both use per-editor pricing and emphasize notebook-to-artifact workflows. Published pricing suggests differences at the Team tier; feature parity depends on governance, compute needs, and app-publishing requirements.

Deepnote vs Datalore

Datalore documents on-prem/self-hosting options and a JetBrains ecosystem alignment that may appeal to regulated customers. Deepnote emphasizes enterprise deployments and custom contracting for some enterprise needs; public documentation on fully self-hosted models was less explicit in the sources reviewed.

	Deepnote	Hex	JetBrains Datalore
Starting Price	Free tier; published per-editor and team pricing available from the vendor	Community/free option; published professional per-editor pricing available from the vendor	Published per-user/team pricing available from the vendor; public references note lower annual rates versus monthly in some cases
Suitable For	Collaborative notebooks mixing SQL/Python/R; teams that need sharing and managed compute	Notebook and data apps for stakeholder consumption; teams focused on published analytics artifacts	Teams that prefer JetBrains ecosystem and seek collaborative notebooks/reports with on-prem deployment options
Key Strength	Real-time collaboration and integrated scheduling and warehouse integrations	App/report publishing and stakeholder-facing analytics workflows	Cloud and on-prem deployment options; integration with JetBrains tooling
Key Weakness	AI context limitations; performance concerns on large datasets; enterprise governance features may require Enterprise-tier offerings	Higher listed Team-tier pricing in some plans; may be heavier than necessary for simple notebook use cases	Pricing and credit models can be complex; may be less product-led for analytics app publishing