BioEureka, a pioneering microbiology company, is transforming how laboratories, hospitals, and research institutions detect bacterial pathogens. Their SaaS platform uses advanced deep learning models for AI in microbiology, enabling faster diagnostics and better patient outcomes.
However, their growing customer base and reliance on third-party hosting created challenges in scalability, cost optimization, and infrastructure control.
The problem: limited scalability and infrastructure dependence for AI
BioEureka’s microbiology SaaS platform was gaining traction among laboratories and healthcare institutions, but its underlying infrastructure posed significant challenges. Hosted on DigitalOcean and managed by a third-party partner, the system lacked the scalability required to meet growing demand and left BioEureka dependent on external support for critical infrastructure changes.
Managing clusters and databases manually not only increased costs but also limited agility in rolling out updates and enhancements. In addition, the absence of robust, AI-native monitoring and compliance controls created operational risks.
To continue scaling globally and strengthen its leadership in AI powered pathogen detection, BioEureka needed a secure, cost-efficient, and future-ready cloud foundation that would give them full control over their infrastructure.
The solution: Cloud and gen AI migration to AWS
GoML designed and executed a 6-week phased migration, moving BioEureka’s entire technology stack, including its AI/ML services, web applications, and databases, into AWS managed services.
1. Infrastructure Modernization
- Replicated DigitalOcean setup into Amazon EKS
- Established secure networking, IAM roles, and AWS Secrets Manager
2. Data Layer Upgrade
- Migrated databases to Amazon RDS (PostgreSQL)
- Deployed ElastiCache (Redis) for faster queries and caching
3. AI/ML Service Optimization
- Deployed Claude 3.& Sonnet and deep learning models on Amazon SageMaker
- Configured GPU-optimized inference pipelines for pathogen detection
4. Application Migration
- Moved Next.js and React-based apps to AWS
- Re-deployed Python ML services with containerized architecture
5. Security and Monitoring
- Integrated CloudWatch and New Relic for observability
- Secured authentication and APIs with AWS-native identity services
6. Seamless Cutover
- Executed gradual DNS shift (10% → 100%) with DigitalOcean backup in parallel
- Ensured zero downtime cloud migration for clients
The impact: future-ready AI for microbiology innovation
GoML’s AWS migration didn’t just modernize BioEureka’s infrastructure, it created a foundation for scalable AI in microbiology innovation.
- 100% uptime during migration, ensuring zero disruption
- 40% reduction in infrastructure costs with AWS managed services
- 2x faster GPU-optimized inference for pathogen identification models
BioEureka’s platform now scales seamlessly with growing lab adoption, while its AI models run faster, more securely, and at predictable cost.
Lessons for AI innovators
Common pitfalls to avoid
- Relying on third-party hosting that limits scalability and control
- Migrating without a zero-downtime strategy, risking client trust
- Treating infrastructure as static instead of future-ready with MLOps
Advice for healthcare leaders
- Start with a high-impact migration PoC focused on uptime and cost efficiency
- Build a cloud-native foundation that supports future AI in microbiology use cases
- Involve domain SMEs and end users early, not just engineering teams
- Prioritize security, compliance, and monitoring as core design principles
Ready to modernize your AI platform like BioEureka?
GoML helps health tech innovators scale AI in microbiology securely and cost-effectively on AWS.
Reach out today to start your migration journey with GoML.
