Available today

Getting in safely

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Available Today

Access and Security

• Single sign-on: Log in with your existing NASA identity. No new passwords to remember.
• Strong authentication: Secure your account with multi-factor authentication (like a PIV badge or code) and passwordless sign-in.
• External collaboration: Partners

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• from universities or other agencies can

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• get access. Their

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• institutions can

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• even connect

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• their own login

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• systems.

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• Built-in access controls: Your workspace only accesses data that you and your project

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• are allowed to see. You

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• never have to manage security keys or secrets.

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• Data encryption: Your data is fully encrypted both when it is stored and when it is moving across the network.

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• Active firewall

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• : A managed web application firewall filters every

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• request to keep the platform secure.

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• True user accounts: You get

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• a unique username and

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• personal file ownership,

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• making file sharing work exactly as expected.
• External cloud access: Project admins can link external cloud roles. Your workspace inherits this access at startup, letting you easily read data from a mission or institution account.

Flexible Workspaces & Computing

• Instant access: Start a workspace on demand immediately—no support tickets or waiting required.
• Flexible sizing: Choose your exact compute power at startup: Standard (4 GB memory

Getting a machine that fits the work

• Start a workspace on demand. No ticket, no waiting.
Pick the size. Standard (4 GB
• / 4 CPU), Large (8 GB / 4 CPU), Extra Large (16 GB / 4 CPU), or
an on-demand node with
• up to 64 GB / 8 CPU on demand.
Software is ready. Earth-
• Pre-installed software: Jump right in with Earth science, Python, and AI
software stacks are pre-installed. Power
• tools already loaded. Advanced users can also bring their own custom container
image. (A container image is
• images (a packaged set of software).
)Many machines for one job. Spread a large analysis over many
• Distributed computing: Spread a single, massive analysis across multiple machines when you need
to
• more power.
• Background
jobs.
• processing: Send
a
• long
notebook
• -running notebooks to
run
• process on
its
• their own.
Your session stays free, and the
• The job keeps running
after
• even if you close
the browser.Capacity follows demand. The platform
• your browser, freeing up your active session.
• Auto-scaling capacity: The platform automatically adds and removes
machines automatically
• computing machines behind the scenes based on user demand.

Storing the science

• Your own space

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• : Your own private storage where files are saved securely and are waiting for you when you return.
• Team space

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• : A collaborative file system for your project

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• team to store and share data, models, and results.
• Project object storage

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• : Private, large-scale cloud storage for your project

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• that you can access directly from your notebook.

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• Integrated data browser

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• : Browse and download files from your project

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• storage—as well as from public AWS science

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• datasets—directly inside JupyterLab.

Integrated AI

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Tools

• AI in the notebook

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• : Chat with AI assistants and run agents directly inside JupyterLab,

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• powered by various model providers.

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• Targeted coding help: Ask the AI to write code, explain complex sections, or fix errors within a specific notebook or a single cell.
• Agents and a secure gateway

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• : Run AI agents on the platform through a single,

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• trusted entry point.

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• For safety, some workflows require human approval before taking action.
• The AI Registry

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• in JupyterLab

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• : Access a hub of NASA-validated AI building

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• blocks—including MCP servers, skills, agents, models, prompts, and harnesses

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• . Use the AgentRegistry panel to search, deploy, and equip your AI assistant with these tools. (Currently rolling out to all projects; See the tool reference.
• Model endpoints on demand

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• : Import and call your own custom models, or

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• connect to managed models, directly from your notebook.

Working together

• Real-time co-editing. Several members can edit the same notebook, code file, or document at the same time, Google-Docs style. Markers show who else is online. Work lands in the shared project space.
• Your project turns it on. A project admin switches collaboration on for the project in the RPAdmin console. It is self-service. No ticket to the platform team.

See Working together on the same notebook.

Seeing what it costs

• Your own usage. You see your own compute and storage usage, and your remaining budget, inside JupyterLab.
• Your project's picture. Your project admin sees the whole project's spend against budget in the RPAdmin console, described next.

The RPAdmin console — self-service for project admins

RPAdmin is the web console for the person who runs a project. Everything in this list is self-service: the project admin does it, with no ticket to the platform team.

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See RPAdmin — the admin console.

Coming

These are agreed and being worked on. No dates. They are listed in rough order of how far along they are.

In active development

• Faster start-up. Keep launch time short even when your home space grows large.
• One-click Hugging Face models. Deploy a public model with one click and call it from your notebook.
• Better "my usage" view. Usage and remaining budget shown on the launch screen as well.
• Roles in the AI Registry. Per-user and per-group permissions, instead of one level for everyone.

Planned next

• Scored AI tools. Every tool in the catalog scored for quality and safety, with history.
• Plain-language search. Find a model, agent, or tool by describing what you need.
• Budget stop at launch. Show a true remaining balance, stop new workspaces when the project's money is used up, and let a project admin choose whether a member who passes their own limit is blocked or may keep spending from the project's pool.
• Storage cost per person. See what each member's object storage costs.

Being explored

• Project-defined machine sizes. A project admin builds and maintains their own list of server types for their team, instead of everyone using one platform-wide list.
• GPU machines. A GPU is a chip that is fast for AI and image work.
• Session detail. Per-session CPU, memory, and storage detail, to understand a cost and size the next run better.
• Review before publish. A draft-and-approve step before an AI asset goes live in the catalog.
• More clouds in the data browser. Google Cloud and Microsoft Azure support.

Want the engineering detail?

The full catalog, with an identifier for every feature and its status, is the Product Roadmap. This page is the plain-language summary of Part I of that file.