As AI adoption surges across industries, teams are grappling with far more than just choosing the right models—they’re facing mounting complexity around secure data access, developer environment sprawl, and the unpredictable costs of scaling infrastructure.
In a recent joint webinar, OpenMetal’s Todd Robinson and Kasm Technologies’ Emrul Islam explored these challenges head-on, drawing from real-world customer scenarios in healthcare, defense, and research. Kasm’s browser-based, container-native workspace platform and OpenMetal’s scalable cloud and bare metal infrastructure are tackling the practical barriers to secure, efficient, and scalable AI experimentation. Here are some key challenges discussed and how their combined approach could be solving these challenges.
Challenge #1: Secure Collaboration with Sensitive Data
Problem:
AI developers, researchers, and engineers often need access to large, sensitive datasets—whether in healthcare, defense, or financial services. But giving them local access risks compliance violations and data leaks.
Solution:
Kasm Workspaces provides a browser-based container streaming platform where nothing is installed locally, and no data is downloaded. All processing happens inside isolated Docker containers hosted in secure environments. This ensures:
- Sensitive data never leaves the controlled workspace
- No local installs are needed—just a browser
- Compliance with HIPAA, GDPR, and other data regulations is simplified
“You want to give researchers access to data—but not the data itself on their local machines,” said Emrul. “Kasm makes that possible.”
Challenge #2: Infrastructure Fragmentation and Configuration Drift
Problem:
AI workloads are notoriously complex—different team members require different toolkits, libraries, data sources, and GPU access. Managing this at scale often leads to configuration drift, shadow IT, and operational chaos.
Solution:
Kasm containers are pre-configured, lightweight, and customizable via Dockerfiles. Using integration tools like Terraform and Ansible, teams can:
- Deploy standardized environments quickly
- Provide isolated sessions with GPU or CPU access on demand
- Manage everything centrally via the Kasm control plane
On the infrastructure side, OpenMetal offers scalable bare metal and private cloud environments that can be tailored to inference and training workloads—whether on modern CPUs for lightweight tasks or high-end GPUs for large models.
Challenge #3: Visual Debugging and Real-Time AI Interaction
Problem:
Many AI use cases—like reinforcement learning, video analysis, or robotics—require users to interact with visual interfaces. Logs and notebooks alone aren’t enough for debugging these problems.
Solution:
Kasm enables users to run full desktop environments (including graphical apps like Visual Studio, Chrome, and media playback) from within secure containers. Emrul demonstrated training a cheetah in a simulated RL environment, highlighting the value of seeing what the model is doing:
“When I could watch the model get stuck in a corner of a room, I understood instantly what the logs couldn’t tell me.”
Challenge #4: Managing Multi-Tenant AI Environments
Problem:
Running shared GPU infrastructure across multiple researchers or developers often leads to resource contention and poor utilization.
Solution:
Kasm lets admins set GPU allocation rules per workspace. It intelligently schedules workloads on available hosts with appropriate resources. This dynamic session placement ensures:
- GPUs are only used when needed
- Sessions are isolated per user
- Cost and resource utilization stay optimized
OpenMetal complements this with infrastructure that can be scaled, allowing customers to expand or shrink GPU capacity as their project requirements change.
Challenge #5: Data Residency, DLP, and Regulatory Compliance
Problem:
Data sovereignty laws require organizations to keep data within specific regions—and prevent leakage through user actions like copy/paste or screenshots.
Solution:
Kasm provides built-in security controls:
- Disable clipboard access
- Restrict file uploads/downloads
- Configure region-specific data mounts
- Enforce workspace-level access controls
These controls, combined with on-prem or region-specific OpenMetal infrastructure, allow organizations to meet strict compliance frameworks without sacrificing usability.
Challenge #6: Cloud Cost Overruns and Vendor Lock-In
Problem:
Teams often start AI experimentation in hyperscale public clouds. But as workloads stabilize and scale, costs become unsustainable and repatriation becomes painful due to deep integration.
Solution:
OpenMetal enables a cloud-native experience with transparent pricing and ownership-level access. Combined with Kasm, customers can:
- Run workloads in the most cost-effective environment (on-prem or in OpenMetal’s hosted private cloud)
- Maintain a consistent developer experience across locations
- Avoid vendor lock-in by using open-source, container-based technologies
“Public cloud is like fast food,” said Emrul. “It feels easy and cheap until you realize how much you’ve consumed—and how hard it is to unpick.”
AI Enablement, Not Just AI Development
While Kasm isn’t positioning itself as an AI modeling platform, it’s quickly becoming a go-to solution for AI enablement—helping organizations build the secure, flexible environments needed to use AI effectively.
Kasm’s AnythingLLM integration, Windows desktop support, and customizable environments make it easy for even non-technical teams to safely interact with generative AI tools in compliance-friendly ways.
Ready to Build Secure, Scalable AI Workspaces?
Kasm Workspaces is free for up to 5 concurrent users—perfect for testing and small-scale deployments. Pricing for larger deployments is available upon request.
👉 Get started at kasmweb.com
OpenMetal offers fully managed, flexible infrastructure solutions for AI teams—from CPU inference to GPU training clusters.
👉 Contact our team to schedule a consultation.
Together, they deliver a powerful solution stack for organizations that want to explore, build, and scale AI securely and efficiently.
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