R&D environments in life sciences organizations are fundamentally different from traditional enterprise application stacks.
They require:
- Strict environment isolation
- High-compute experimental workloads
- Controlled promotion pipelines
- Regulatory-grade audit logging
- Secure hybrid access for global research teams
In this post, I'll Walk through how we architected a multi-environment DevOps platform on AWS for a global life sciences research organization, implementing:
- Four isolated VPC environments (Dev, QA, Prod, R&D)
- AWS Transit Gateway for controlled inter-VPC routing
- Site-to-Site VPN for secure hybrid connectivity
- ECS-based container orchestration
- Dedicated EC2 workloads for research flexibility
- Centralized CI/CD using CodePipeline
- End-to-end encryption and audit logging
The result was a scalable, secure, and research-ready cloud platform with ~80% reduction in manual deployment effort.
The Technical Challenge
The organization's R&D systems were constrained by:
- Manual deployment processes
- Inconsistent release management
- Limited separation between environments
- No centralized CI/CD
- Need to support customer-managed container workloads
- Requirement for secure on-premises connectivity
- Compliance-sensitive data handling
For research workloads—especially in pharmaceutical and biotech domains—environment leakage between Dev, QA, Prod, and Research is unacceptable.
The goal was to design a fully isolated, auditable, multi-environment cloud architecture that supported both structured application workloads and experimental research containers.
Multi-VPC Architecture Design
The architecture consisted of:
- Dev VPC
- QA VPC
- Prod VPC
- R&D (R-Search) VPC
Each environment:
- Deployed across multiple Availability Zones
- Configured with isolated subnets
- Enforced strict security group segmentation
Why Separate VPCs Instead of Logical Segmentation?
While subnet isolation could have been used within a single VPC, separate VPCs provide:
- Stronger blast radius containment
- Clearer compliance boundaries
- Independent routing control
- Environment-specific security policies
- Reduced misconfiguration risk
For regulated research workloads, physical VPC separation provides higher operational assurance.
Inter-VPC Communication via AWS Transit Gateway
To allow controlled communication between environments, we implemented:
- AWS Transit Gateway (TGW) as the central routing hub
Benefits:
- Simplified routing management
- Centralized network governance
- Scalable VPC attachment model
- Controlled cross-environment communication
Transit Gateway allowed:
- Dev to QA promotion workflows
- Shared services communication (logging, monitoring)
- Strict route table controls to prevent unnecessary exposure
Secure Hybrid Connectivity
Research teams and developers required on-prem access to AWS workloads. We implemented:
- Site-to-Site VPN Gateway
- Encrypted IPSec tunnels
- Controlled routing via Transit Gateway
This enabled:
- Seamless hybrid operations
- Secure private connectivity
- No public exposure of backend systems
For life sciences R&D, hybrid connectivity is often mandatory due to lab-based systems and compliance constraints.
Compute Layer Design
The architecture differentiated between:
Standard Application Workloads (Dev/QA/Prod)
- Amazon ECS (EC2 launch type)
- Auto Scaling Groups
- Application Load Balancers
- Dedicated EC2-based database servers
Why ECS (EC2 launch type) instead of Fargate?
- Greater control over instance configuration
- Performance tuning flexibility
- Custom compliance agents installed on hosts
- Cost predictability for long-running workloads
Research Workloads (R-Search VPC)
The R&D environment required:
- EC2-based compute
- Customer-managed containers
- Flexible experimentation capabilities
- High-compute workloads
Unlike production workloads, research teams required the ability to:
- Test experimental container configurations
- Run custom compute workloads outside managed orchestration
- Adjust runtime parameters freely
The R-Search VPC provided controlled freedom without impacting production systems.
CI/CD Standardization Across Environments
One of the most impactful improvements was implementing centralized CI/CD.
Pipeline Flow
- Code committed to GitHub
- CodePipeline triggers
- CodeBuild:
- Builds container images
- Runs automated checks
- Pushes to Amazon ECR
- CodeDeploy:
- Deploys to ECS in Dev
- Promotes to QA
- Promotes to Prod
Benefits:
- Controlled environment promotion
- Reduced manual intervention (~80% reduction)
- Repeatable deployments
- Improved release consistency
Database Strategy
Each environment included:
- Dedicated EC2-based database servers
Why not Amazon RDS?
In this specific R&D use case:
- Fine-grained database control was required
- Custom extensions and tuning were necessary
- Compliance-related logging agents needed OS-level access
While RDS is generally recommended, certain research workloads justify EC2-hosted databases for deeper configurability.
Monitoring, Logging & Observability
A multi-layered observability stack was implemented:
Amazon CloudWatch
- ECS metrics
- EC2 health
- Custom application metrics
AWS SNS
- Alert notifications
- Incident escalation
AWS CloudTrail
- Complete API activity capture
- Audit trail for compliance
External Monitoring (Site24x7)
- Uptime validation
- Global availability checks
This ensured both internal infrastructure visibility and external service health monitoring.
Security & Encryption Controls
Security was enforced at multiple layers:
Encryption
- AWS KMS for encryption at rest
- Encrypted volumes
- Secure VPN tunnels
Identity & Access
- IAM role-based access
- Environment-specific IAM policies
Edge Protection
- AWS WAF in front of public endpoints
Audit Compliance
- CloudTrail logs stored securely
- Activity traceability across environments
This design ensured regulatory readiness for life sciences workloads.
Quantitative Results
| Area | Result |
|---|---|
| Deployment Automation | ~80% reduction in manual steps |
| Environment Isolation | Full separation of Dev, QA, Prod, R&D |
| Hybrid Connectivity | Secure on-prem to AWS access |
| Security | Full encryption + CloudTrail audit logging |
| Operational Agility | Flexible support for experimental workloads |
Beyond metrics, the largest impact was organizational:
- Researchers gained flexibility without compromising production stability
- DevOps teams achieved repeatable environment promotion
- Security teams gained full visibility into activity
Architectural Lessons Learned
1. Separate VPCs Reduce Risk in Regulated Industries
Environment isolation must be enforced at the network boundary.
2. Transit Gateway Simplifies Multi-VPC Governance
Centralized routing improves visibility and control.
3. Research Workloads Require Flexibility
Not all compute should be fully managed — controlled EC2 workloads are sometimes necessary.
4. CI/CD Is Essential for Environment Consistency
Manual promotion processes are error-prone and non-compliant.
5. Hybrid Connectivity Must Be Designed Securely
VPN + route controls prevent public exposure.
Final Thoughts
Modernizing R&D infrastructure is not just about moving workloads to AWS — it is about:
- Designing environment isolation
- Enforcing compliance controls
- Enabling flexible experimentation
- Standardizing release pipelines
- Securing hybrid connectivity
By implementing a multi-VPC architecture interconnected via Transit Gateway, supported by CI/CD automation and layered security, we delivered a secure, scalable, research-ready cloud foundation.
For AWS practitioners, this case demonstrates how:
Network isolation + DevOps standardization + hybrid connectivity can enable regulated R&D workloads to operate securely and efficiently in the cloud.
Author
Milan Rathod
AWS Project Manager
AeonX Digital Technology Limited