Introduction
BeHooked is a fast-growing technology company that develops advanced AI and machine learning solutions for content generation and model training. The company relied on RunPod to manage GPU workloads, batch processing, and containerized deployments.
As project complexity increased, BeHooked needed a cloud platform that could deliver better scalability, security, and performance while maintaining operational efficiency. To meet these goals, BeHooked partnered with TeleGlobal, to migrate and modernize its workloads on AWS.
The Need for Change
BeHooked’s RunPod environment performed well initially, but over time the company encountered growing limitations. These included difficulty scaling GPU workloads, limited visibility into performance metrics, and challenges maintaining cost efficiency as usage increased.
The company required:
- Scalable GPU infrastructure to support deep learning and inference tasks
- Unified monitoring and observability
- Stronger access controls and encryption standards
- Automated orchestration for containers and serverless applications
- Minimal downtime during migration
BeHooked wanted a secure, high-performing AWS environment that could keep up with its rapid innovation cycle.
Partnering with TeleGlobal International
TeleGlobal International created a comprehensive migration plan to move all RunPod workloads to AWS. The strategy was designed to enable high availability, simplified management, and cost optimization without disrupting BeHooked’s ongoing operations.
The engagement focused on four key goals:
- Establish a secure and scalable AWS infrastructure.
- Optimize GPU and ML workloads for performance and cost.
- Improve visibility through monitoring and automation.
- Strengthen the overall security and compliance posture.
The Migration Journey
The project was delivered in five structured phases over six weeks, ensuring zero data loss and minimal service interruption.
1. Assessment and Preparation
TeleGlobal analyzed BeHooked’s RunPod workloads, GPU configurations, and container environments. Dependencies and network structures were documented to define the optimal AWS architecture.
2. AWS Environment Design
A dedicated Virtual Private Cloud (VPC) was configured in the Mumbai region with subnets, NAT gateways, and route tables. Security Groups and firewall rules were applied to ensure controlled access between services.
3. Compute and Storage Provisioning
- Deployed multiple EC2 instance types (including g4dn, g5, inf1, and p3) for GPU and ML workloads.
- Set up Amazon EKS for containerized workloads and orchestration.
- Configured AWS Lambda functions for serverless components.
- Migrated datasets and volumes to Amazon S3 and EBS gp3 with automated backups.
4. Security and Monitoring Setup
- Enabled IAM, KMS, and Secrets Manager for identity and encryption management.
- Activated CloudWatch and CloudTrail for centralized monitoring and audit logging.
- Configured AWS WAF and Security Hub for real-time threat detection and compliance reporting.
5. Migration Execution and Cutover
- Executed phased migration of workloads with replication and validation at each step.
- Performed DNS and CloudFront cutover for global content delivery.
- Completed final testing, validation, and documentation before handover.
AWS Services Implemented
| Category | AWS Services | Purpose |
| Compute | EC2 (GPU/ML), Lambda | High-performance computing and serverless functions |
| Containers | Amazon EKS | Container orchestration and workload management |
| Storage | Amazon S3, EBS | Secure object and block storage for datasets and models |
| Network | VPC, CloudFront, API Gateway | Secure connectivity and global access |
| Security | IAM, KMS, Secrets Manager, WAF, Security Hub | Access control and threat protection |
| Monitoring | CloudWatch, CloudTrail | Real-time monitoring, logging, and compliance tracking |
Key Achievements
- Successful migration of all GPU and ML workloads from RunPod to AWS.
- Improved GPU utilization and faster model training cycles.
- Centralized monitoring and automation through CloudWatch dashboards.
- Strengthened security with IAM and encryption best practices.
- Reduced operational overhead through managed AWS services.
- Cost optimization through reserved instances and workload right-sizing.
Results and Impact
After the migration, BeHooked saw measurable improvements across performance and operations:
- 30% faster ML model training using AWS GPU infrastructure.
- 60% increase in visibility through unified monitoring and reporting.
- 40% lower operational costs by leveraging automation and cost control tools.
- Simplified deployment processes with EKS and Lambda automation.
The new AWS environment supports BeHooked’s growing AI workloads with stronger reliability, scalability, and security.
Conclusion
With the support of TeleGlobal International, BeHooked successfully modernized its infrastructure and migrated from RunPod to AWS. The new AWS-based architecture delivers faster performance, stronger security, and improved cost efficiency.
BeHooked now operates on a flexible, future-ready cloud foundation that supports its mission to innovate faster in the fields of AI and machine learning.