Overview

Users can create, edit, and run Python code in a browser, with packages and computing power supplied by the service. That removes the variability of installing Python across different operating systems and laptops, which is useful for learners and teams who need a common starting point. The trade-off is that this workflow depends on Anaconda Cloud rather than a local installation.

Notebook URLs can let recipients open a copy, collaborate with an Anaconda account, or download it. Organizations can disable sharing, in which case a user may receive a 403 error. Anaconda says stored data is encrypted at disk and database levels, while application, database, webhook, and API traffic uses TLS/HTTPS.

Key features

Prepared Python environments

Two preconfigured environments cover different starting points: one uses the latest Anaconda Distribution packages, while the other selects packages for AI workflows. The service also supports environment management, lockfiles, package publishing, and private registries. That combination gives Python teams more than a disposable notebook, though users are choosing from the service's prepared environments rather than describing a broader range of runtime options in these features.

Sharing and publishing

Notebook sharing is practical for handing work to collaborators, who can use an Anaconda account to work on a copy or download it. Panel adds a route from notebook output to a shareable web application. These features support a workflow that extends beyond individual analysis, but sharing depends on organizational policy.

Assistant and data access

Anaconda Assistant for Python can analyze tables and suggest ways to work with data. Data Connectors let users save CSV files on Anaconda Cloud for access through Excel or a notebook. The product page says the Anaconda Cloud account handles API key generation and access-based permissions for the AI Assistant, giving teams a stated permissions mechanism for that feature.

Pricing

The free plan costs 0.00 USD per free and includes 5 GB of cloud storage. Anaconda Core Free also includes thousands of data science, machine learning, and AI packages, Assistant code help, and introductory courses. It is a reasonable entry point for learners or individual users, but the 5 GB cap is a meaningful constraint for growing projects.

Starter costs 15.00 USD per month, billed monthly, with 10 GB storage per user. Anaconda Core Starter is priced at $15 /user/mo and adds a professional development environment, on-demand training courses, and team workspace controls. This tier fits users who need more storage and shared workspace controls without the security and governance package of Business.

Business costs 50.00 USD per month, billed monthly; Anaconda Core Business is priced at $50 /user/mo. It adds automated vulnerability scanning, audit trails and project governance, centralized access management and SSO, threat intelligence, and curated security data. Those controls make it the relevant tier for organizations that need managed access and governance, rather than simply more notebook capacity.

Custom pricing applies to the Custom tier. It offers self-hosting on-premises or in private or public cloud, AI orchestration with built-in observability, advanced multi-team governance, custom integrations, and SLAs. This is aimed at organizations with deployment or integration requirements beyond the standard cloud plans; the terms are arranged through a custom offer.

Platforms

Anaconda Notebooks runs on the web. The browser model avoids local Python installation, while its cloud-based nature makes it a different fit from tools intended to manage Python directly on Linux, macOS, or Windows machines.

Who it's for

Learners and Python or data science users, from beginners to seasoned professionals, can use the service for browser-based coding, prepared environments, and notebook sharing. It is especially suitable for teams that want notebooks, CSV access, and Panel publishing connected within Anaconda Cloud. It is less suitable when an organization prohibits sharing or when a project cannot fit within the storage allowance of its chosen plan.

Pros and cons

Pro: Browser-based JupyterLab with packages and computing power provided by the service avoids local Python setup.
Pro: Two prepared environments, plus environment management and lockfile support, give users a structured start for Python and AI workflows.
Pro: Notebook URLs and Panel publishing support both collaboration and sharing an application built from notebook output.
Pro: Business adds vulnerability scanning, audit trails, centralized access management, and SSO for organizations that need governance controls.
Con: Free storage is capped at 5 GB, and Starter provides 10 GB per user, so storage needs can determine whether the entry plans fit.
Con: An organization can block notebook sharing, limiting an otherwise useful collaboration path.
Con: The workflow is web-based, not a locally installed Python environment.

Alternatives

For a broader scan of adjacent tools, browse Python Package Managers, Data Science Platforms, Artifact repository software, and Coding Playgrounds.

ActiveState Platform is a freemium option with a free organization plan limited to public projects; choose it when that public-project constraint and its API, Linux, macOS, web, and Windows platform coverage fit better.
Hatch is a free, MIT-licensed Python project manager for Linux, macOS, and Windows; choose it for a local project-management tool rather than a hosted notebook workspace.
pip is a free option for Linux, macOS, and Windows; choose it when a free package installer is the need rather than browser-based notebook work.
pip-tools offers free command-line tools installed per virtual environment and requires Python 3.9 or later; choose it for that command-line workflow.
Pixi offers free options for Linux, macOS, and Windows, including an open-source package-hosting plan with a global CDN, 100GB of public packages, and unlimited channel users; choose it when that package-hosting model is a closer fit.
uv is a free option for Linux, macOS, and Windows; choose it when a free tool for those platforms is preferable to a hosted notebook service.
conda-lock is free, open-source software for Linux, macOS, and Windows, installable with pip, conda, mamba, pipx, or condax; choose it when lockfile tooling is the main requirement.
Astral offers free, open-source, permissively licensed tools including Ruff, uv, and ty; choose it for that open-source toolset instead.

Verdict

Choose Anaconda Notebooks if your learners or Python team want JupyterLab in the browser, prepared environments, and a route from notebooks to shared Panel applications. The main reason to choose it is that these pieces sit together in Anaconda Cloud without local Python setup. Look elsewhere if your work needs a local-first workflow, more than the plan's storage allowance, or notebook sharing that organizational policy guarantees.

	Anaconda Notebooks	BaGet
Free plan	Yes	No
Free trial	No	No
Paid from	$15/mo	—
Open source	No	No
Platforms	Web	api, Linux, macOS, self-hosted, Windows
Free plan	Yes	—
Storage limit	10	—
Upstream proxy	—	Yes
Access control	—	basic
Self hosted	—	Yes

Anaconda Notebooks vs BaGet

Anaconda Notebooks

BaGet