Datalore

Datalore is a collaborative data science platform for exploring, analyzing, visualizing and sharing data. It offers Jupyter-compatible notebooks with coding assistance for Python, Kotlin, Scala and R, plus native SQL cells with syntax help and completion. Query results can move into pandas DataFrames for Python work. Teams can edit code together in real time and share notebooks with view or edit access. Notebooks can be presented as interactive data stories or shared as static or 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. Cloud costs $35 a month. On-Premises deployment uses Docker Compose or Kubernetes on 64-bit Linux and does not support ARM, macOS or Windows hosts.

Datalore combines notebooks, collaboration, reporting and data connections, with both cloud and self-hosted options. The free plan has defined usage limits, while On-Premises deployment is limited to 64-bit Linux hosts.

Datalore suits people and teams working with notebooks, databases and shared data reports. Its On-Premises option may fit organizations that require private or air-gapped environments and custom identity providers or SSO.

Datalore is a collaborative data science platform built around Jupyter-compatible notebooks. It suits teams that work across code, databases and shared analysis. Its central appeal is combining those workflows with reporting and cloud or self-hosted deployment.

Overview

Datalore brings data exploration, analysis, visualization and sharing into a common workspace. Real-time collaboration and report publishing make it a stronger fit for teams than for someone seeking only a personal notebook. The choice between cloud and On-Premises deployment also gives organizations with different hosting needs a way to use the same platform.

Key features

Notebooks support Python, Kotlin, Scala and R, with coding assistance. Native SQL cells add syntax highlighting and completion, and can pass query results into pandas DataFrames. That handoff is useful when analysis moves between database queries and Python. Supported connections include Amazon Redshift, Azure SQL Database, MariaDB, MySQL, Oracle, PostgreSQL and Snowflake.

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

People can edit code together in real time and share notebooks with view or edit access. A notebook can also become an interactive data story and be shared as a static or interactive report. Datalore AI can assist with Python, SQL and R code, explain findings and help fix errors; it is most relevant to teams that want these aids within their analysis workflow.

Version history and GPU compute extend the notebook environment. Supported formats include .ipynb, .datalore, .zip, .py, .kts, .scala, .r and .html, giving users multiple ways to work with or share notebooks.

Pricing

Datalore uses a freemium model, with a free plan and a 14-day trial. Cloud Free costs nothing 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. Those caps make it suitable for modest or exploratory work, but teams with sustained compute needs or multiple workspaces will likely outgrow it.

Cloud costs $35 a month, billed monthly. Each team member receives 750 CPU L machine hours, 6 Datalore credits and 20 GB of cloud storage. The per-member allocation makes it the more practical cloud option for teams needing more compute and storage than the free tier, though the free plan's single workspace and report are not its main selling point.

On-Premises has custom pricing. It offers a self-hosted or dedicated JetBrains-managed instance, unlimited free viewers, advanced team management, SSO options and audit logs. It is aimed at organizations that need control over deployment or enterprise administration, rather than individual users seeking the lowest-cost notebook.

Platforms

Datalore is available through the web and API, with Linux and self-hosted options. On-Premises can be deployed with Docker Compose or Kubernetes on 64-bit Linux. It does not support ARM, macOS or Windows hosts, a material constraint for organizations whose infrastructure depends on those systems. 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 and SSO. The plan includes enterprise-grade customer support and solution architect consultations.

Who it's for

Datalore is a strong fit for data teams that need collaborative notebooks, SQL-to-Python analysis, report sharing and connections to common databases and cloud storage. The free tier can serve small or occasional workloads, while Cloud adds per-member compute and storage allowances. Organizations with deployment or identity requirements may find On-Premises more suitable, provided they run supported Linux hosts. It is less compelling for solo users who need only a lightweight notebook or for teams whose host infrastructure is ARM, macOS or Windows.

Pros and cons

• Pros: Jupyter-compatible notebooks combine four coding languages with native SQL cells and database connections, supporting analysis across query and code workflows.
• Pros: Real-time editing, view or edit sharing, and static or interactive reports support collaboration from notebook work through presentation.
• Pros: Cloud and self-hosted deployment options, including air-gapped support, serve teams with different hosting and security needs.
• Cons: Cloud Free is limited to one workspace, one interactive report, two parallel notebooks, 120 CPU S machine hours and 10 GB of storage.
• Cons: On-Premises is restricted to 64-bit Linux hosts, excluding ARM, macOS and Windows deployments.
• Cons: The Cloud plan's $35 monthly price is per team member, so its listed compute, credits and storage are also allocated per member.

Alternatives

Pluto.jl is worth considering for readers who want a free and open-source option, with support across Android, Linux, macOS, web and Windows.

Apache Zeppelin is a free, open-source alternative under the Apache License, Version 2.0, with Linux, macOS, self-hosted and web availability.

Hex may suit readers looking for a freemium web and API option; its free Community plan includes any data source, all cell types, small compute and an agent trial, while Professional costs $36 a month, billed per editor.

marimo offers a free, permissively licensed open-source notebook, with web, self-hosted and desktop platform options.

Nextjournal has a free plan with unlimited private notebooks and data and secrets management, alongside limited concurrent and entry-level compute resources.

Paperspace Notebooks may appeal to readers prioritizing free CPU and GPU machines; its free plan includes 5 GB storage, with those machines limited to notebooks in private workspaces.

Saturn Cloud is another option, with a Hosted plan at $0.09 a month, billed pay-as-you-go hourly, including 16 GB memory, 4 vCPUs and 0 vGPUs; its Enterprise plan has custom pricing.

Deepnote has a free plan billed forever, with up to three editors, up to five projects and unlimited Basic machines with 5 GB of storage.

Verdict

Choose Datalore if your team needs collaborative notebooks that connect SQL, code and data sources, then turn analysis into shareable reports. Its mix of cloud and self-hosted deployment is the main reason to choose it; the free tier's firm usage caps and On-Premises Linux restriction are the main reasons to look elsewhere.