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Datalore

jetbrains.com

From $29.17/mo Free plan WebLinuxAPISelf-hosted
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Spec score 9.2 #1 of 32 in Data Science Platforms

What it is

Datalore is a collaborative data science platform for exploring, analyzing, visualizing and sharing data. Its Jupyter-compatible notebooks provide coding assistance for Python, Kotlin, Scala and R, with native SQL cells that can transfer query results into pandas DataFrames. Teams can edit code together in real time and share notebooks with view or edit access. Notebooks can become data stories or static and interactive reports. Datalore AI can help write Python, SQL and R code, describe findings and fix errors. Database connections include Amazon Redshift, Azure SQL Database, MariaDB, MySQL, Oracle, PostgreSQL and Snowflake; AWS S3 and Google Cloud Storage buckets can be mounted as notebook folders. The free plan includes 120 CPU S machine hours and 10 GB of cloud storage. The Cloud plan costs $35 a month, billed monthly. On-Premises deployment runs on 64-bit Linux using Docker Compose or Kubernetes, but not on ARM, macOS or Windows hosts. JetBrains states that Datalore deployments are SOC 2 Type II certified and GDPR compliant.

Spec sheet

What it does
Datalore is a collaborative web data analytics platform for exploring, analyzing, visualizing and sharing data.Read 15 Oct 2026 · jetbrains.com
Purpose
Datalore is a collaborative data science platform for exploring, analyzing, visualizing, and sharing data.Read 30 Sep 2026 · jetbrains.com
Deployment
Datalore On-Premises supports Docker Compose or Kubernetes deployment on 64-bit Linux and does not support ARM, macOS, or Windows hosts.Read 30 Sep 2026 · jetbrains.com
Free plan
YesRead 20 Sep 2026 · jetbrains.com
Support
The On-Premises plan includes enterprise-grade customer support and solution architect consultations.Read 30 Sep 2026 · jetbrains.com
Headquarters
Amsterdam, NetherlandsRead 28 Sep 2026 · jetbrains.com
Founded
2000Read 28 Sep 2026 · jetbrains.com
AI
Datalore AI assists with Python, SQL and R code, explanations and error fixing.Read 15 Oct 2026 · jetbrains.com
API authentication
Run API requests use bearer-token authentication and identify notebooks by owner ID and notebook ID.Read 15 Oct 2026 · jetbrains.com
Collaboration
Real-time notebook collaboration, link or email sharing, access levels and shared workspaces are supported.Read 15 Oct 2026 · jetbrains.com
Company founders and year
Crunchbase names Eugene Belyaev, Sergey Dmitriev and Valentin Kipiatkov as founders and dates JetBrains to 2000.Read 15 Oct 2026 · crunchbase.com
Company legal name
Crunchbase lists the maker as JetBrains s.r.o., a private active company.Read 15 Oct 2026 · crunchbase.com
Company scale
Crunchbase lists JetBrains at 1,001–5,000 employees and headquartered in Amsterdam, Netherlands.Read 15 Oct 2026 · crunchbase.com
Data integrations
The product advertises connections including AWS S3, Google Cloud, PostgreSQL, Snowflake, MySQL and other databases.Read 15 Oct 2026 · jetbrains.com
Database integrations
Datalore supports database connections including Amazon Redshift, Azure SQL Database, MariaDB, MySQL, Oracle, PostgreSQL, and Snowflake.Read 30 Sep 2026 · jetbrains.com
Documentation depth
The help center includes feature guides, installation, administration, security, API tokens, Run API, agent skills and release notes.Read 15 Oct 2026 · jetbrains.com
Enterprise security
Datalore On-Premises supports private or air-gapped environments, custom identity providers, and SSO.Read 30 Sep 2026 · jetbrains.com
Free-plan limits
The Free plan includes unlimited notebooks, two notebooks running in parallel, one workspace, one interactive report, 120 CPU S machine hours, and 10 GB of cloud storage.Read 30 Sep 2026 · jetbrains.com
Git integration
Git repositories can provide collaborative Python modules, scripts and pip-compatible packages inside notebooks.Read 15 Oct 2026 · jetbrains.com
Hardware guidance
The documented minimum server is four CPU cores and 4 GB RAM, with a suggested computation formula of 4 × (users plus one) GB RAM.Read 15 Oct 2026 · jetbrains.com
Licence
The On-Premises agreement says JetBrains owns Datalore intellectual property and grants a limited subscription right, indicating proprietary software.Read 15 Oct 2026 · jetbrains.com
Limit: learning curve and resource use
A Capterra reviewer calls the product heavy on a laptop and says its learning curve is steep.Read 15 Oct 2026 · capterra.com
Limit: missing integrations and browser support
A Capterra reviewer requested MongoDB connectivity and better iPad, Safari and S3 setup support.Read 15 Oct 2026 · capterra.com
Limit: MLOps
A Gartner reviewer praises the product but says its MLOps capabilities are currently limited.Read 15 Oct 2026 · gartner.com
Limit: startup performance
A G2 reviewer reports that provided machines can be slow and initialization can sometimes be buggy or lengthy.Read 15 Oct 2026 · g2.com
Limit: storage navigation and Python updates
Gartner reviewers criticize deep cloud-storage navigation, slow Python-version updates and the lack of selective-cell reruns or regular dashboard refresh.Read 15 Oct 2026 · gartner.com
Notable news: SOC 2
JetBrains announced on February 26, 2024 that Datalore was included in its SOC 2 audit report.Read 15 Oct 2026 · blog.jetbrains.com
Notebook languages
Its Jupyter-compatible notebooks support Python, Kotlin, Scala and R with coding assistance.Read 15 Oct 2026 · jetbrains.com
Notebook support
Datalore provides Jupyter-compatible notebooks with coding assistance for Python, Kotlin, Scala, and R.Read 30 Sep 2026 · jetbrains.com
Praise: coding and repositories
G2 reviewers praise IDE-like type hinting and completion plus convenient Git and SSH-key repository integration.Read 15 Oct 2026 · g2.com
Praise: support and collaboration
Gartner reviewers repeatedly praise responsive support, live cooperation, easy report creation and straightforward environment setup.Read 15 Oct 2026 · gartner.com
Praise: usability
Capterra reviewers describe the environment as intuitive, easy to start, useful for scheduling and effective for sharing and collaboration.Read 15 Oct 2026 · capterra.com
Public API
The Run API triggers notebook execution, accepts interactive-control parameters and returns run status and artifacts.Read 15 Oct 2026 · jetbrains.com
Rating on Capterra
4.5 out of 5 from 8 reviewsRead 15 Oct 2026 · capterra.com
Rating on G2
4.8 out of 5 from 6 reviewsRead 15 Oct 2026 · g2.com
Rating on Gartner Peer Insights
4.6 out of 5 from 5 reviewsRead 15 Oct 2026 · gartner.com
Rating on Software Advice
4.5 out of 5 from 8 reviewsRead 15 Oct 2026 · softwareadvice.com
Recent feature
The 2026.1 release introduced Data Explorer cells for filtering, transforming and charting dataframes without extra code.Read 15 Oct 2026 · jetbrains.com
Recent release
Datalore On-Premises 2026.3 added workspace-scoped API tokens, OAuth database connections and live plan configuration changes.Read 15 Oct 2026 · jetbrains.com
Reporting
Notebook cells can be turned into interactive or static reports with code hidden from viewers.Read 15 Oct 2026 · jetbrains.com
Scheduling
Notebook schedules can run hourly, daily, weekly or monthly, including multiple or parameterized schedules.Read 15 Oct 2026 · jetbrains.com
Security and compliance
JetBrains states that Datalore is GDPR compliant and covered by its SOC 2 Type II audit report.Read 15 Oct 2026 · blog.jetbrains.com
Self-hosting
On-Premises can run in a customer-controlled cloud, datacenter or hybrid environment using Docker or Kubernetes.Read 15 Oct 2026 · jetbrains.com
SQL and databases
Users can connect databases, browse schemas and run native SQL cells whose results continue into Python analysis.Read 15 Oct 2026 · jetbrains.com
SQL cells
Native SQL cells provide syntax highlighting and completion, and transfer query results into pandas DataFrames for Python work.Read 30 Sep 2026 · jetbrains.com
Storage integrations
Datalore can mount AWS S3 and Google Cloud Storage buckets as folders in notebooks.Read 30 Sep 2026 · jetbrains.com
System requirements
On-Premises supports 64-bit Linux hosts; ARM, macOS and Windows hosts are unsupported, with Docker Compose or Kubernetes deployment options.Read 15 Oct 2026 · jetbrains.com

Plans and prices

Cloud Free Free 120 CPU S machine hours · 10 GB cloud storage · 2 notebooks in parallel · 1 workspace · 1 interactive shareable report Read 30 Sep 2026 · jetbrains.com
Cloud $35 a month monthly 750 CPU L machine hours per team member · 6 Datalore credits per team member · 20 GB cloud storage per team member Read 30 Sep 2026 · jetbrains.com
Cloud Free Free Unlimited notebooks · two parallel notebooks · one shared workspace · one interactive report · notebook export · Notebook API · 120 CPU S machine hours · 10 GB storage Read 15 Oct 2026 · jetbrains.com
Cloud annual $29.17 a month annually Same Cloud features; price shown per user per month Read 15 Oct 2026 · jetbrains.com
Cloud $35 a month monthly Datalore AI · unlimited parallel notebooks · real-time collaboration · version history · unlimited database connections and SQL queries · unlimited reports and workspaces · scheduled notebooks · team management · 750 CPU L hours per team member · 6 credits per member · 20 GB storage Read 15 Oct 2026 · jetbrains.com
Cloud $350.04 a year $350.04 per year (= $29.17/month, billed annually) 750 CPU L machine hours per team member · 6 Datalore credits per team member · 20 GB cloud storage per team member Read 30 Sep 2026 · jetbrains.com
On-Premises Priced on request Self-hosted or JetBrains-managed dedicated instance · unlimited viewers · advanced team management · custom environments and plans · SSO · audit logs · enterprise support · full control of computation and storage Read 15 Oct 2026 · jetbrains.com

Every Datalore plan and price →

Strong points and limits

Strong points

  • Supports Python, Kotlin, Scala and R notebooks.
  • Enables real-time code collaboration and notebook sharing.
  • Connects to listed databases and cloud storage buckets.
  • Free plan includes 120 CPU S machine hours.
  • On-Premises supports private or air-gapped environments.

Limits

  • Cloud costs $35 a month.
  • On-Premises hosts must use 64-bit Linux.
  • On-Premises does not support ARM, macOS or Windows hosts.
  • Free storage is limited to 10 GB.

Who it suits

Datalore suits data science teams that want shared notebooks, database connections and report publishing. Its On-Premises option may suit organizations needing private or air-gapped deployment and SSO.

Datalore in depth

Read the full write-up

Overview

Datalore is a collaborative data science platform built around Jupyter-compatible notebooks. It is a strong fit for teams that combine coding, database queries and shared analysis. Its main advantage is bringing those tasks and report sharing together; the choice between cloud and self-hosted deployment adds flexibility, but the free tier has defined compute and storage ceilings.

Key features

Notebooks support Python, Kotlin, Scala and R, with coding assistance, while native SQL cells offer syntax highlighting and completion. SQL results can flow into pandas DataFrames for Python analysis, a useful bridge for work that moves between querying a database and exploring its results in code. Supported connections include Amazon Redshift, Azure SQL Database, MariaDB, MySQL, Oracle, PostgreSQL and Snowflake.

Teams can mount AWS S3 and Google Cloud Storage buckets as notebook folders, and clone or pull public and private Git repositories as workspace or notebook resources. These connections keep data and code sources close to the analysis, though they do not replace the underlying storage or version-control services.

Real-time collaboration supports shared notebooks with view or edit access. Notebooks can become interactive data stories and be shared as static or interactive reports, which makes Datalore useful when analysis needs to travel beyond its authors. Version history, GPU compute and support for.ipynb,.datalore,.zip,.py,.kts,.scala,.r and.html files broaden the notebook workflow.

Datalore AI can help write Python, SQL and R code, describe findings and fix errors. That is relevant to teams seeking assistance during analysis; it does not remove the need to review generated code and conclusions.

Pricing

Cloud Free costs nothing. It includes unlimited notebooks, two notebooks running in parallel, one workspace, one interactive shareable report, 120 CPU S machine hours and 10 GB of cloud storage. This is a substantial starting allowance for individuals or small teams evaluating the workflow, but parallel runs, storage and compute are clear constraints for sustained or larger workloads.

Cloud costs $35 a month, billed monthly. Each team member gets 750 CPU L machine hours, 6 Datalore credits and 20 GB of cloud storage. The per-member allocation suits teams that need more compute and storage than the free tier provides; the monthly price and team-member-based quotas matter when estimating costs for a growing group.

On-Premises has custom pricing and offers either a self-hosted or dedicated JetBrains-managed instance, unlimited free viewers, advanced team management, SSO options and audit logs. It is the enterprise-oriented choice for teams needing deployment control or broader access without viewer seats adding cost. A 14-day trial is available.

Platforms

Datalore is available on the web and supports API, Linux and self-hosted deployment. Its hybrid execution model offers flexibility across environments. On-Premises runs on 64-bit Linux using Docker Compose or Kubernetes, but does not support ARM, macOS or Windows hosts, so organizations with those server environments cannot host it there.

JetBrains states that Datalore deployments are SOC 2 Type II certified and GDPR compliant. On-Premises adds support for private or air-gapped environments, custom identity providers and SSO, plus enterprise-grade customer support and solution architect consultations.

Who it's for

Datalore suits data science teams that want coding, SQL access, live collaboration and shareable reporting in one platform. It is especially compelling where analysis draws on supported databases or cloud object storage, or where enterprise deployment and identity controls matter. A solo user seeking a lightweight notebook or a team whose workloads exceed the free quotas but do not justify the paid tier should weigh those limits before committing.

Pros and cons

Pros: SQL cells pass query results into pandas DataFrames, keeping database exploration and Python analysis connected.

Pros: Real-time collaboration and static or interactive report sharing support both team work and distribution of results.

Pros: On-Premises offers air-gapped deployment, SSO options, audit logs and unlimited free viewers for organizations with stricter access needs.

Cons: Free compute, storage, parallel notebook and workspace allowances are capped, which can restrict ongoing team use.

Cons: On-Premises hosting requires 64-bit Linux and excludes ARM, macOS and Windows hosts.

Cons: Cloud quotas are allocated per team member, so the monthly cost and included capacity need to be considered together as a team grows.

Alternatives

Deepnote is worth considering for teams that want a free tier with up to three editors and five projects, or need its broader platform coverage, including desktop operating systems and extensions.

marimo is a better fit when free, permissively licensed open-source notebook software is the priority.

Paperspace Notebooks may suit users who specifically want free CPU and GPU machines, with 5 GB of included storage.

Apache Zeppelin is a free, open-source alternative under the Apache License, Version 2.0.

Hex offers a Community plan with data-source connections, all cell types, small compute and an agent trial; consider it if those inclusions better match your starting needs.

Nextjournal offers unlimited private notebooks on its free plan, with limited concurrent compute resources.

Pluto.jl is a free, open-source choice under the MIT license.

SageMath is a free, open-source option that can be installed on unlimited computers and downloaded or redistributed.

Browse more options in Notebook Software, Data Science Platforms and Coding Playgrounds.

Verdict

Choose Datalore if your data team needs notebooks that connect SQL and Python work, support live collaboration and turn analysis into shareable reports. The free tier is a useful way to begin, while the paid cloud and On-Premises options serve heavier workloads and enterprise requirements. Look elsewhere if the free quotas are too restrictive or your self-hosted environment is not 64-bit Linux.

Verdict

Datalore brings notebooks, collaboration, database access and report sharing together, with both cloud and self-hosted deployment options. Compare the free-plan capacity and On-Premises host requirements with your team's needs.

Where Specifiction places it

Alternatives to DataloreAll alternatives →

Hex From $36/mo · Free plan 8.0 Visit