Introducing workspaces for Lambda Cloud

2026-06-02 · Source: The Lambda Deep Learning Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Lambda Cloud has introduced "workspaces," a new feature designed to help teams organize cloud resources, control access, and separate development, staging, and production environments within shared GPU infrastructure. This addresses common issues in flat cloud accounts, such as junior researchers accidentally impacting production or contractors having overly broad access. Workspaces act as logical containers for grouping GPU instances, Kubernetes clusters, and filesystems, serving as the primary unit for resource-level access control. Account administrators can create up to 200 workspaces, each with explicit member assignments, ensuring only authorized individuals can view or launch resources within them. The feature is available now to all Lambda Cloud accounts for free, allowing for better management of growing GPU fleets without relying on manual tagging or separate accounts.

Key takeaway

For MLOps Leads or Platform Engineers managing shared GPU environments, Lambda Cloud "workspaces" offer a critical solution for scaling team access and resource organization. You can now enforce clear boundaries between development, staging, and production, preventing accidental disruptions and ensuring data security. Implement "workspaces" to streamline contractor onboarding/offboarding and integrate access changes with your existing identity management tools, significantly reducing operational overhead and risk.

Key insights

Lambda Cloud "workspaces" provide logical containers for GPU resources, enhancing access control and environment separation.

Principles

• Group resources by project or environment.
• Scope access explicitly per team.
• Eliminate reliance on naming conventions.

Method

Account admins create workspaces and add members; only assigned members see/launch resources. Default workspace is permanent.

In practice

• Separate dev, staging, and production environments.
• Grant project-specific access to contractors.
• Integrate workspace management with onboarding tools.

Topics

• Lambda Cloud
• GPU Resource Management
• Access Control
• MLOps
• Cloud Infrastructure
• Environment Separation

Best for: CTO, VP of Engineering/Data, MLOps Engineer, AI Architect, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Lambda Deep Learning Blog.