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 is free. 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 35.00 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 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.

	Datalore	NextLeet Playground
Free plan	Yes	Yes
Free trial	Yes
Paid from		Free
Platforms	api, Linux, self-hosted, Web
Free plan	Yes	Yes
Supported languages	Python, SQL, R, Scala, Kotlin
Execution model	hybrid
Real-time collaboration	Yes
Version history	Yes
GPU compute	Yes
Notebook formats	.ipynb, .datalore, .zip, .py, .kts, .scala, .r, .html
Deployment options		none

Datalore vs NextLeet Playground

Datalore

NextLeet Playground