Overview

Datalore brings data exploration, analysis, visualization, and sharing into one platform. Real-time notebook collaboration and report publishing make it a practical choice for teams whose work needs to be reviewed or presented, not just run. A solo user who only needs a lightweight notebook may find the team workspace and paid cloud tier unnecessary.

Key features

Jupyter-compatible notebooks support Python, Kotlin, Scala, and R, with coding assistance. Native SQL cells add syntax highlighting and completion, then pass query results into pandas DataFrames for Python analysis. That handoff is useful when a project regularly crosses between database queries and code. Datalore connects to Amazon Redshift, Azure SQL Database, MariaDB, MySQL, Oracle, PostgreSQL, and Snowflake.

AWS S3 and Google Cloud Storage buckets can be mounted as notebook folders, and public or private Git repositories can be cloned and pulled as workspace or notebook resources. These connections keep data and code sources close to analysis, but do not replace the storage or version-control services themselves.

Teams can work on code in real time and share notebooks with view or edit access. Notebooks can also be turned into interactive data stories and shared as static or interactive reports, giving teams a route from analysis to presentation. Datalore AI can help write Python, SQL, and R code, describe findings, and fix errors; it is most relevant to users who want assistance during analysis rather than a separate writing tool.

Version history and GPU compute add useful capabilities for notebook work. Supported formats include .ipynb, .datalore, .zip, .py, .kts, .scala, .r, and .html, which offers ways to move notebooks and outputs beyond Datalore's native format.

Pricing

Datalore uses a freemium model, with a free plan, a 14-day trial, a paid cloud plan, and an On-Premises option with custom pricing.

Plan	Price	What it includes
Cloud Free	0.00 USD per free	Unlimited notebooks, two running in parallel, one workspace, one interactive shareable report, 120 CPU S machine hours, and 10 GB cloud storage.
Cloud	35.00 USD per month, billed monthly	750 CPU L machine hours per team member, 6 Datalore credits per team member, and 20 GB cloud storage per team member.
On-Premises	Custom pricing	Self-hosted or dedicated JetBrains-managed instance, unlimited free viewers, advanced team management, SSO options, and audit logs.

Cloud Free is useful for trying notebook collaboration and reporting, but two concurrent notebooks, one workspace, and 120 CPU S hours constrain sustained or parallel workloads. Cloud raises the compute and storage allowances per team member, making it a better fit for active teams, though its monthly per-member quotas still matter when planning heavier use. On-Premises suits organizations that need private deployment or more administration and identity controls; custom pricing makes it a procurement decision rather than a straightforward low-cost upgrade. The 14-day trial gives prospective users a limited period to assess the paid offering.

Platforms

Datalore is available as a web platform and API, with Linux and self-hosted deployment options. Its execution model is hybrid. On-Premises can be deployed with Docker Compose or Kubernetes on 64-bit Linux hosts; ARM, macOS, and Windows hosts are not supported. That restriction matters for organizations standardizing on those environments. JetBrains states that Datalore deployments are SOC 2 Type II certified and GDPR compliant. On-Premises also supports private or air-gapped environments, custom identity providers, SSO, enterprise-grade customer support, and solution architect consultations.

Who it's for

Datalore is a good fit for data teams that combine notebook coding with SQL, need to connect to common databases or cloud storage, and want to collaborate or publish reports in the same workspace. Organizations needing a private or air-gapped installation have an option, provided they can run it on 64-bit Linux. It is less compelling for individual users seeking an unconstrained free notebook or for teams that need to deploy on macOS, Windows, or ARM hosts.

Pros and cons

Pros: Jupyter-compatible notebooks combine Python, Kotlin, Scala, R, and SQL workflows, including transfer of query results into pandas.
Pros: Real-time collaboration, view or edit sharing, and static or interactive reports cover both teamwork and presentation.
Pros: Database, object-storage, and Git connections put common analysis inputs within notebook workflows.
Cons: The free plan limits parallel execution, workspace count, report count, CPU hours, and storage, so regular workloads may outgrow it.
Cons: On-Premises deployment is limited to 64-bit Linux hosts, excluding ARM, macOS, and Windows.
Cons: The Cloud plan's machine hours, credits, and storage are allocated per team member, so teams must account for those quotas.

Alternatives

Deepnote is worth considering if platform breadth matters: it spans API, extension, Linux, macOS, self-hosted, web, and Windows, and its free plan includes up to three editors and five projects. Hex may suit teams comparing paid notebook platforms; its Community plan is free and its Professional plan is 36.00 USD per month, with data-source connections and multiple cell types in Community. Nextjournal is another freemium web and self-hosted option, with unlimited private notebooks on its free plan, though concurrent compute and entry-level resources are limited.

marimo is a free option with an open-source notebook plan. Paperspace Notebooks offers a free plan with 5 GB storage and free CPU and GPU machines for notebooks in private workspaces. JupyterLab, Apache Zeppelin, and Polynote are alternatives to consider.

For broader browsing, see Notebook Software, Data Science Platforms, and Coding playgrounds.

Verdict

Choose Datalore if your team wants notebooks, database access, collaboration, and report sharing together, with an On-Premises route for private deployments. Its strongest advantage is the connected workflow; its clearest reason to look elsewhere is the free plan's bounded capacity or the Linux-only host requirement for On-Premises.

	Datalore	Kita
Free plan	Yes	Yes
Free trial	Yes	No
Paid from	—	—
Open source	No	No
Platforms	api, Linux, self-hosted, Web	—
Free plan	Yes	Yes
Real-time collaboration	—	Yes

Datalore vs Kita

Datalore

Kita