Overview

Datalore brings data exploration, analysis, visualization, and sharing into a shared notebook environment. Real-time editing and view-or-edit sharing make it a stronger fit for collaborative projects than for people who simply want a local coding surface. Interactive data stories and static or interactive reports also give teams a way to distribute results without making every reader an editor.

Key features

Jupyter-compatible notebooks support Python, Kotlin, Scala, and R. Native SQL cells include syntax highlighting and completion, and can send query results into pandas DataFrames for Python work. That makes Datalore useful when an analysis moves between database queries and code rather than staying in one language. Supported 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, while public or private Git repositories can be cloned and pulled for use as workspace or notebook resources. These links help keep data and code close to analysis, though teams still need their underlying storage and repository services.

Datalore AI can assist with Python, SQL, and R code, explain findings, and help correct errors. Its value is greatest for users who want assistance in the analysis workflow, not as a replacement for reviewing code or validating results. Version history, GPU compute, hybrid execution, and support for .ipynb, .datalore, .zip, .py, .kts, .scala, .r, and .html add flexibility for different workflows.

For organizations that need deployment control, On-Premises can run through Docker Compose or Kubernetes on 64-bit Linux. It does not support ARM, macOS, or Windows hosts, which is a meaningful constraint for teams without Linux infrastructure. 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, audit logs, and advanced team management.

Pricing

Cloud Free costs 0.00 USD per free and 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. It is a credible starting point for light analysis, but the compute cap and single workspace limit make it less suitable for a team with sustained or parallel workloads.

Cloud costs 35.00 USD per 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 allows; the free plan gives up that higher allocation and its per-member scaling.

On-Premises has custom pricing and can be a self-hosted or dedicated JetBrains-managed instance. It includes unlimited free viewers, enterprise-grade customer support, and solution architect consultations, alongside its security and management controls. This is the option for organizations prioritizing deployment control and administration rather than a published per-month cloud price. A 14-day trial is available.

Platforms

Datalore is available through the web and API, with Linux and self-hosted deployment options. Its On-Premises host requirements are specifically 64-bit Linux, excluding ARM, macOS, and Windows hosts.

Who it's for

Datalore fits data science teams that need shared notebooks, database access, code assistance, and reports in one workflow. It is also relevant to organizations that require private or air-gapped deployment and SSO. Individuals with modest notebook needs may find the free tier sufficient, but its limited parallel notebooks and compute make it a poor fit for heavier ongoing work.

Pros and cons

Pros: SQL results flow into pandas DataFrames, helping analysts move directly from database queries to Python work.
Pros: Real-time collaboration, permissioned sharing, and report publishing support both team work and distribution to readers.
Pros: Cloud and On-Premises options accommodate teams with different deployment and security needs.
Cons: The free cloud tier has 120 CPU S machine hours, 10 GB storage, one workspace, and two parallel notebooks, limiting room for heavier work.
Cons: On-Premises requires 64-bit Linux hosts and does not support ARM, macOS, or Windows.
Cons: On-Premises pricing is custom, so organizations cannot compare it from a fixed monthly price.

Alternatives

Choose Deepnote if you want a freemium notebook option with a free plan offering up to three editors, up to five projects, and unlimited Basic machines. JupyterLab is the better fit for users who want free, open-source notebook software installed locally with pip. Pick Hex if its Community plan’s connections to any data source, all cell types, small compute, and Hex agent trial better match your starting needs. Nextjournal offers unlimited private notebooks on its free plan, alongside data and secrets management and limited concurrent compute.

Polynote is a free, open-source notebook option distributed as a self-hosted server. Consider Google Colab for a freemium notebook with limited GPU access and no guaranteed compute-unit access on its Colab plan. Observable has a free plan with few tokens per day, 1 GB per month of file attachments, and watermarked embeds. BinderHub is free open-source deployment software when an operator can supply the cloud or hardware infrastructure.

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

Verdict

Datalore is a strong choice for collaborative data teams that need notebooks to connect SQL, code, and shareable reporting, especially when deployment control matters. Its main advantage is bringing those parts into one shared environment; look elsewhere if you need a simple local notebook or if the free plan’s compute and storage caps are too restrictive.

	Datalore	JuliaHub
Free plan	Yes	No
Free trial	Yes	No
Paid from	—	—
Open source	No	No
Platforms	api, Linux, self-hosted, Web	—
Free plan	Yes	—
Real-time collaboration	—	Yes
Private projects	—	Yes
Full-stack runtime	—	Yes
Deployment options	—	full_stack
Sharing controls	—	access_control

Datalore vs JuliaHub

Datalore

JuliaHub