Available Today

Access and Security

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• Your Own Usage: View your personal compute/storage usage and remaining budget right inside JupyterLab.
• The Big Picture: Project admins can track the entire team's spending against the budget from the RPAdmin console.

The RPAdmin Console: Self-Service for Admins

RPAdmin is the web console for project managers. Everything listed below is entirely self-service—meaning project admins can do it instantly without submitting a ticket to the platform team.

• Track costs: Monitor compute hours, compute costs, and storage costs for the entire project.
• Set and allocate budgets: Give individual team members specific spending limits. The platform prevents you from allocating more than your project holds. (The project's total is set by the Research Platform team, after the funds reach the Science Cloud.)
• Monitor usage: See exactly which team members are using specific resources, and how much they are using.
• Manage team access: Add members, remove members, or promote members to the project admin role.
• Enable collaboration: Toggle the real-time co-editing feature on for your project.
• Cross-account AWS access: Register your own AWS roles so they are automatically granted to your notebooks at start-up. This lets you securely interact with your own AWS account (such as reading a mission data bucket or starting a high-performance computing cluster) directly from the Research Platform. No access keys or secrets are ever copied into the notebook.
• Audit trail: View a complete history of every account, membership, and budget change, including who made the change and exactly what was altered.

For more details, see RPAdmin — the admin console.

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Future Capabilities

In Active Development

The following features are approved and currently being built. While we don't have release dates yet, they are listed in rough order of how far along they are:

• Faster start-up: Ensuring launch times remain short, even as your personal storage space grows large.
• One-click Hugging Face models: Deploy a public model with a single click and call it directly from your notebook.
• Improved "My Usage" view: See your personal usage and remaining budget immediately on the launch screen.
• Roles in the AI Registry: Granular per-user and per-group permissions, replacing the current single access level for everyone.

Planned Next

• Scored AI tools: Quality and safety scores for every tool in the catalog, complete with a tracked history.
• Plain-language search: Find models, agents, or tools by simply describing what you need in everyday language.
• Budget controls at launch: Display a true remaining balance and prevent new workspaces from starting when project funds run out. Project admins will also have the option to block members who exceed their personal limits, or allow them to continue spending from the general project pool.
• Storage costs per person: View the exact object storage costs for each individual team member.

Being Explored

• Project-defined machine sizes: Project admins can build and maintain a custom list of server types for their specific team, rather than using a single platform-wide list.
• GPU machines: Adding machines equipped with GPUs (Graphics Processing Units), which are specialized chips that significantly speed up AI and image-processing workflows.
• Detailed session metrics: Per-session breakdowns of CPU, memory, and storage usage to help you understand costs and choose the perfect machine size for your next run.
• Review before publish: A draft-and-approve workflow before a new AI asset goes live in the catalog.
• Expanded data browser clouds: Adding support for Google Cloud and Microsoft Azure data to the data browser.

Want the engineering details?

The full catalog—complete with an identifier and detailed status for every feature—is tracked in the Product Roadmap. This page serves as a plain-language summary of Part I of that document.