Success Stories
Glass Health — Secure & Cost-Efficient Medical AI Infrastructure
Glass Health builds AI-powered tools for clinical reasoning, medical insights, and decision support. Their products work directly with sensitive medical data, which places strict requirements on data privacy, compliance, and cost predictability.
Initially, Glass Health relied on Gemini as a cloud-based AI model to power parts of their medical workflows.
Problem
Using cloud AI for medical data poses HIPAA/GDPR compliance risks and high costs.
As Glass Health scaled, two critical issues emerged:
At this point, Glass Health needed a solution that offered full data isolation, regulatory alignment, and cost control.
Solution
Glass Health partnered with GPU.Business to design and deploy a private medical LLM infrastructure.
Together with GPU.Business, we delivered an end-to-end solution:
Results
After migrating with GPU.Business, Glass Health achieved:
The entire migration — from architecture design to production deployment — was completed in 3 weeks.
Why GPU.Business
Glass Health chose GPU.Business because we provide:
By working with GPU.Business, Glass Health transformed their AI stack into a secure, compliant, and scalable medical AI platform.
Conclusion
For medical AI companies like Glass Health, cloud AI solutions are often not enough. Regulatory pressure, privacy requirements, and cost control demand a different approach.
By deploying a private medical LLM with GPU.Business, Glass Health gained compliance confidence, infrastructure control, and long-term cost efficiency — without sacrificing AI performance.
GPU.Business makes private medical AI practical, fast, and production-ready.
Glass Health — Secure & Cost-Efficient Medical AI Infrastructure
Integration time: 3 weeks
Problem: Using cloud AI for medical data poses HIPAA/GDPR compliance risks and high costs
Solution: Deploying your own medical LLM keeps data private and reduces costs
AI model before: Gemini
AI model after: BioGPT / BioMedLM
Glass Health builds AI-powered tools for clinical reasoning, medical insights, and decision support. Their products work directly with sensitive medical data, which places strict requirements on data privacy, compliance, and cost predictability.
Initially, Glass Health relied on Gemini as a cloud-based AI model to power parts of their medical workflows.
Problem
Using cloud AI for medical data poses HIPAA/GDPR compliance risks and high costs.
As Glass Health scaled, two critical issues emerged:
- Regulatory risk
- Processing medical data through third-party cloud AI created compliance challenges with HIPAA and GDPR, especially around data residency, auditability, and third-party access.
- Unpredictable and growing costs
- Cloud AI pricing was usage-based, making monthly expenses difficult to forecast as medical data volumes increased.
- Limited control over the model
- Gemini could not be fully adapted or audited for Glass Health’s specific medical workflows and internal standards.
At this point, Glass Health needed a solution that offered full data isolation, regulatory alignment, and cost control.
Solution
Glass Health partnered with GPU.Business to design and deploy a private medical LLM infrastructure.
Together with GPU.Business, we delivered an end-to-end solution:
- Private GPU Infrastructure
- We deployed a dedicated virtual GPU server on Nebius (https://nebius.com), ensuring full isolation of medical data and eliminating third-party data exposure.
- Open Medical LLM Deployment
- Instead of Gemini, we implemented BioGPT and BioMedLM — open-source models purpose-built for medical and biomedical text understanding.
- Compliance-First Architecture
- With guidance from GPU.Business, the system was designed to align with HIPAA and GDPR principles, keeping all sensitive data within a controlled environment.
- Operational Optimization
- GPU.Business handled model setup, optimization, and inference workflows, ensuring stable performance and predictable infrastructure costs.
Results
After migrating with GPU.Business, Glass Health achieved:
- Full data privacy — medical data never leaves the private GPU environment
- Reduced operational risk related to HIPAA/GDPR compliance
- Lower and predictable AI costs compared to cloud-based AI pricing
- Greater control and transparency over medical AI behavior
The entire migration — from architecture design to production deployment — was completed in 3 weeks.
Why GPU.Business
Glass Health chose GPU.Business because we provide:
- Proven expertise in medical AI and LLM infrastructure
- Secure GPU-based private deployments
- End-to-end ownership: from model selection to production optimization
- A clear alternative to costly and risky cloud AI dependencies
By working with GPU.Business, Glass Health transformed their AI stack into a secure, compliant, and scalable medical AI platform.
Conclusion
For medical AI companies like Glass Health, cloud AI solutions are often not enough. Regulatory pressure, privacy requirements, and cost control demand a different approach.
By deploying a private medical LLM with GPU.Business, Glass Health gained compliance confidence, infrastructure control, and long-term cost efficiency — without sacrificing AI performance.
GPU.Business makes private medical AI practical, fast, and production-ready.
Cut AI costs by 60–80% ⚡
For startups & businesses.
No rewrites. No performance loss.