Why Deployment Architecture Matters
Drug discovery generates valuable proprietary data: target hypotheses, molecular designs, binding predictions, structure-activity relationships. How this data is stored, processed, and protected has implications for intellectual property, regulatory compliance, and competitive advantage.
The deployment model of AI drug discovery platforms—where the software runs and where data resides—directly affects data governance capabilities and operational control.
Deployment Options
Cloud-Hosted (Vendor-Managed)
The platform runs in the vendor's cloud infrastructure. Users access it through web interfaces or APIs.
- Fastest deployment: No infrastructure setup required
- Scalability: Vendor manages compute resources
- Updates: Automatic access to latest features
- Data location: Data processed on vendor infrastructure
- Security model: Dependent on vendor security practices
Private Cloud (Customer Account)
The platform runs within the customer's own cloud account (AWS, Azure, GCP).
- Data stays in customer account: Never leaves organizational infrastructure
- Customer-controlled security: Organization sets access controls, encryption, auditing
- Cloud scalability: Access to cloud compute without vendor data custody
- Requires cloud expertise: Organization must manage cloud infrastructure
On-Premise Deployment
The platform runs on hardware within the organization's own data center or facilities.
- Maximum data control: Data never leaves physical premises
- Air-gapped option: Can operate without internet connectivity
- Regulatory alignment: May satisfy strict data residency requirements
- Infrastructure investment: Requires appropriate hardware and IT support
- Longer deployment: More complex setup than cloud options
Comparison Matrix
| Factor | Vendor Cloud | Private Cloud | On-Premise |
|---|---|---|---|
| Time to deploy | Days | Weeks | Weeks-Months |
| Data residency control | Limited | Full (within cloud) | Full (physical) |
| Infrastructure management | Vendor | Customer | Customer |
| Scalability | High | High | Limited by hardware |
| Upfront cost | Low | Medium | High |
| Air-gap capability | No | No | Yes |
There is no universally "best" deployment model. The right choice depends on data sensitivity, regulatory requirements, existing infrastructure, and organizational capabilities.
Key Decision Factors
Data Sensitivity
How sensitive is the research data being processed? Early-stage exploratory work may have different requirements than late-stage programs approaching clinical trials or regulatory filings.
Regulatory Requirements
Some regulatory frameworks impose specific requirements on data handling, residency, or processing location. Organizations in regulated environments should verify deployment models satisfy compliance obligations.
IT Capabilities
Private cloud and on-premise deployments require internal technical capabilities to manage infrastructure. Organizations without dedicated IT resources may prefer vendor-managed options.
Model Training Considerations
Some AI platforms improve their models using customer data. Organizations should understand whether their data contributes to model training and whether this can be excluded contractually.
Total Cost of Ownership
Vendor-hosted services have lower upfront costs but ongoing subscription fees. On-premise deployment has higher upfront investment but may have lower long-term costs for intensive usage. Private cloud sits between these extremes.
Hybrid Approaches
Some organizations adopt hybrid strategies:
- Tiered by sensitivity: Less sensitive work on cloud; highly sensitive programs on-premise
- Phased migration: Start with cloud for evaluation; migrate to private deployment for production
- Workload-specific: Different deployment models for different computational tasks
Questions to Ask Vendors
- What deployment options do you offer?
- Where is data stored and processed geographically?
- Is customer data used for model training? Can this be contractually excluded?
- What security certifications apply to your infrastructure?
- How is data isolated between customers in multi-tenant environments?
- What are the infrastructure requirements for private deployment?
- How do updates and support work for on-premise installations?
Enterprise Deployment Options
Learn about CycloGen's private and on-premise deployment options for pharmaceutical R&D.
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