The volume of medical imaging data is growing exponentially. With this surge comes the urgent need to annotate data accurately and efficiently to develop reliable AI tools in diagnostics, pathology, radiology, and beyond. But there’s one critical decision healthcare organizations must make before annotation even begins: How Should You Securely Host Your Medical Data?
Whether you’re a hospital system safeguarding patient records or a med-tech company scaling AI operations, choosing between on-premises, hybrid, and private cloud deployments can significantly impact your workflow, compliance posture, and scalability.
In this blog, we’ll break down the pros and cons of each model through the lens of healthcare AI workflows and explain how Ango Hub supports all three to meet your operational and regulatory needs.
Why Deployment Model Choices Matter More in Healthcare AI
In medicine, data isn’t just data, it’s protected health information (PHI). This raises the bar for compliance, access control, and security:
- Regulations like HIPAA and GDPR strictly govern how medical data is stored, accessed, and shared.
- Medical image formats such as DICOM and NIfTI require specialized handling.
- Cross-functional collaboration is key, as radiologists, ML engineers, and annotators all need access, often in different geographies.
- Downtime can be costly, not just financially but in terms of delayed innovation in diagnostics and treatment.
To meet these high standards, choosing the right deployment model is critical. Let’s discuss the three most common approaches for hosting and managing sensitive medical data.
Deployment Models for Medical Data Annotation
On-Prem Deployment
On-premises (on-prem) deployment means all software and data live within your organization’s servers or infrastructure, physically located on-site or in a private data center.
Use Case:
A national hospital network uses Ango Hub on-prem to annotate radiology scans in a tightly secured internal system. This setup allows them to fully comply with internal data governance policies while keeping PHI behind hospital firewalls.
Hybrid Deployment
A hybrid deployment uses a mix of local and cloud resources. Data may stay on-prem for compliance, while labeling tools, AI models, or project management interfaces are hosted in the cloud.
Use Case:
A medical AI startup labels pathology slides on-site to protect patient data but uses Ango Hub’s cloud-based workflow tools for annotator assignments, QA management, and model validation, allowing distributed teams to work in parallel.
Private Cloud Deployment
A private cloud is a dedicated cloud infrastructure, either hosted by a third-party provider or built internally, that serves only one organization. Unlike the public cloud, resources are not shared.
Use Case:
A global med-tech company deploys Ango Hub on a private Azure instance to meet data residency laws across regions. They benefit from cloud scalability while ensuring that their AI development complies with both U.S. and EU privacy requirements.
| Deployment Type | Pros | Cons |
|---|---|---|
| On-Premise | – Maximum control over data – Air-gapped option for high-security needs – Aligns with strict hospital or research IT policies |
– High infrastructure & maintenance costs – Slower scalability – Less flexibility for remote teams |
| Hybrid | – Balanced control and flexibility – Remote collaboration enabled – Gradual cloud adoption |
– More complex IT setup – Potential data sync/integration challenges |
| Private | – High security and isolation – Customizable – Easier regulatory compliance than public cloud |
– Higher costs – Needs IT resources to manage |
Comparison Table: On-Prem vs. Hybrid vs. Private Cloud
| Feature | On-Prem | Hybrid | Private Cloud |
|---|---|---|---|
| Data Control | Highest | High | High |
| Scalability | Low | High | High |
| Deployment Complexity | High | High | Medium |
| Regulatory Compliance | Strong | Strong | Strong |
| Operational Flexibility | Low | High | High |
| Remote Collaboration | Limited | Supported | Supported |
| Cost | High Capital Expenditure | Medium Capital + Operational Expenditure | Medium to High Operational Expenditure |
How to Choose the Right Model for Medical Workflows
Your ideal deployment model depends on your:
- Compliance needs (HIPAA, GDPR, etc.)
- Data sensitivity and access control policies
- Team distribution (on-site vs. remote)
- Scalability goals for AI model training
- IT infrastructure maturity
Healthcare enterprises with strict control mandates may lean toward on-prem. Startups looking to scale fast often prefer a hybrid. And global med-techs needing a balance between agility and compliance may opt for a private cloud.
How Ango Hub Supports All Three
Whether you are a hospital, AI lab, or med-tech innovator, iMerit Ango Hub offers deployment flexibility tailored to your needs:
- On-prem: Fully isolated deployment with support for internal servers and air-gapped environments.
- Hybrid: Retain control of data while using Ango Hub’s cloud-native tools for workflow management.
- Private Cloud: Deploy in your secure cloud environment (e.g., AWS, Azure, GCP) with full compliance support.
Additional Benefits
- Medical format support: Native handling of DICOM, NIfTI, and other specialized medical data types.
- Compliance-ready: Built to support HIPAA, GDPR, and other health data regulations.
Built for Compliance: iMerit’s Healthcare-Grade Standards
Medical data annotation is not just about accuracy, it’s about accountability. iMerit ensures that Ango Hub operates within secure, regulation-ready frameworks trusted by leading healthcare organizations:
- HIPAA-compliant workflows to securely manage PHI
- GDPR-ready infrastructure for clients operating in Europe
- SOC 2 Type II certified operations ensuring security, availability, and privacy
- Custom data governance and access controls, tailored for each client’s compliance needs
- Audit-friendly environments with clear traceability across annotation pipelines
Whether you’re labeling radiology images, pathology slides, or clinical notes, iMerit and Ango Hub provide the secure, auditable, and scalable foundation your team needs to stay compliant and stay ahead.
Final Thoughts: Secure Infrastructure, Smarter Annotation
Choosing the right deployment model isn’t just a technical decision but also a strategic decision. It determines how fast you scale, how securely you operate, and how effectively your AI systems can learn from sensitive medical data. Ango Hub empowers healthcare and AI teams with the flexibility to work where and how they need to, without compromising on compliance, collaboration, or performance.
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Connect with us to deploy Ango Hub securely, flexibly, and exactly where your data needs to be.
