Datalore

Datalore is a collaborative data science platform built around Jupyter-compatible notebooks, for exploring, analyzing, visualizing, and sharing data. It is best suited to teams that work across code, SQL, and shared reporting. Its integrated workflow is compelling, though the free tier’s compute and storage limits may constrain sustained work.

Overview

Datalore brings notebook work, team collaboration, and report publishing into one environment. Real-time editing and view-or-edit sharing suit teams developing analysis together; interactive and static reports also make it easier to share results beyond notebook users. Individuals who need only 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, and send query results into pandas DataFrames for Python analysis. This is a useful fit for work that moves between database queries and code. 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 into a workspace or notebook. These integrations help keep data and code resources close to analysis, without replacing the underlying storage or version-control services.

Datalore AI can help write Python, SQL, and R code, describe findings, and fix errors. That assistance is relevant to analysts working within those languages, rather than a general-purpose AI feature. Notebooks can become interactive data stories and be shared as static or interactive reports, giving teams a way to distribute findings in addition to editable notebooks. Version history and GPU compute are also supported.

Supported notebook formats include .ipynb, .datalore, .zip, .py, .kts, .scala, .r, and .html. The hybrid execution model gives Datalore flexibility across cloud and self-hosted use. JetBrains states that deployments are SOC 2 Type II certified and GDPR compliant.

Pricing

The Cloud Free plan 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. That is a useful starting point for individuals or small-scale evaluation, but the one-workspace and modest compute and storage allowances limit it for ongoing team workloads.

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 allowances make this the clearer fit for teams that need more compute and storage than the free tier provides; the recurring monthly price is a material consideration as seats grow. A 14-day trial is also available.

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. The plan is aimed at organizations that need deployment control or enterprise administration, rather than teams seeking a low-cost cloud workspace.

Platforms

Datalore is available through the web, API, Linux, and self-hosted deployment. On-Premises runs on 64-bit Linux using Docker Compose or Kubernetes; ARM, macOS, and Windows hosts are not supported. That makes it a poor fit for organizations whose server environment depends on those excluded host platforms. 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 strongest for data science teams that need collaborative notebooks, database access, and report sharing in the same workflow. It is also suited to organizations with private or air-gapped deployment requirements that can meet its Linux host constraints. A solo user who wants a simple free notebook, or a team with limited compute needs but many parallel notebooks, should weigh the free tier's caps carefully.

Pros and cons

• Pros: Real-time collaboration, notebook sharing, and report publishing support both joint analysis and distribution of results.
• Pros: SQL cells, database connections, cloud-storage mounts, and Git access bring common analysis inputs and code resources into the notebook workflow.
• Pros: On-Premises supports private and air-gapped setups, SSO, audit logs, and custom identity providers.
• Cons: Free use is capped at 120 CPU S machine hours, 10 GB of storage, one workspace, and two simultaneous notebooks, which can restrict ongoing or concurrent work.
• Cons: On-Premises requires a 64-bit Linux host and excludes ARM, macOS, and Windows, narrowing deployment options.
• Cons: Cloud costs $35 a month, billed monthly, with an allowance structure that may become significant as a team adds members.

Alternatives

Choose Deepnote if its Free plan's up to 3 editors and up to 5 projects better match a small collaborative setup. Pick JupyterLab for free, open-source notebook software installed locally with pip. Polynote is another free, open-source option distributed as a self-hosted server. Choose BinderHub if you want open-source deployment software and can supply the cloud or hardware infrastructure yourself.

Google Colab may suit users willing to work with no guaranteed access to compute units and limited GPU access on its Colab plan. Hex offers a Community plan with any data-source connections, all cell types, small compute, and a Hex agent trial. Kaggle Notebooks is a free alternative with limited P100 GPU and TPU v3-8 availability, CPU/GPU sessions up to 12 hours, TPU sessions up to 9 hours, and 20 GB autosaved disk. Nextjournal offers unlimited private notebooks and data and secrets management on its Free plan, with limited concurrent compute resources and entry-level compute.

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

Verdict

Choose Datalore if your team wants collaborative notebooks that connect code, SQL, data sources, and shareable reports, or needs a private deployment with enterprise controls. Its main advantage is the breadth of that joined workflow; look elsewhere if the free compute caps, monthly per-member cloud allowances, or Linux-only On-Premises host requirements do not suit your setup.