BrainCrew — Cloud Migration from Azure to AWS & LLMOps Modernization
Customer Overview
BrainCrew, a specialized provider of customized AI solutions and education services, had built its flagship AI agent management platform, WeAgent, on Azure OpenAI Service and Azure PaaS. As the platform scaled, the company faced critical technical and business constraints that threatened continued growth.
Challenges
The Windows-oriented Azure App Service environment was incompatible with BrainCrew's AI engines, which required high-performance Linux kernel tuning, causing persistent runtime errors and blocking OS-level performance optimization.
Simultaneously, Azure VM disk I/O bandwidth throttling forced BrainCrew to over-provision high-spec instances to sustain vector DB and analytics DB workloads, driving infrastructure costs unnecessarily high.
Most critically, deep dependency on Azure OpenAI Service created vendor lock-in that delayed adoption of frontier LLMs such as Claude 3.5 by up to three months, directly undermining the company's ability to deliver cutting-edge AI capabilities to its customers.
Partner Solution and AWS Services Utilized
NXT Cloud led BrainCrew through a structured cloud migration and modernization engagement following an Assess–Migrate–Modernize framework.
Assess Phase
NXT Cloud conducted a comprehensive analysis of BrainCrew's Azure resource utilization to identify cost inefficiencies and redundant licensing. A proof-of-concept was performed to validate the stability and feasibility of an Amazon Bedrock-based AI model architecture before full migration commenced.
Migrate Phase
AWS Database Migration Service (DMS) was utilized to perform real-time data replication, enabling the migration of large-scale datasets while limiting service downtime to under one hour. Existing Azure VM workloads were re-platformed onto AWS Graviton3 instances, delivering significantly improved price-performance and eliminating the underlying compute bottlenecks inherited from the previous environment.
Modernize Phase
NXT Cloud established a production-grade LLMOps environment on Amazon Bedrock, enabling BrainCrew to manage multiple AI models—including Claude 3.5—through a unified API. A Langfuse-based observability pipeline was integrated to provide full operational visibility while maintaining data sovereignty.
Amazon EBS gp3 storage was adopted to decouple I/O performance from instance size, allowing independent throughput control and permanently resolving the throttling issues experienced on Azure.
Deployment pipelines were automated to reduce manual operational overhead. Database workloads were migrated to Amazon Aurora with Read Replica Auto Scaling enabled, replacing the previous proprietary Azure SQL licensing.
Infrastructure was codified using Terraform to align with global multi-cloud IaC standards and ensure reusability for future migrations. Security posture was modernized through granular AWS IAM policy management, replacing the legacy Azure-based security constraints with AWS security best practices.
Results and Quantitative Outcomes
| Metric | Result |
|---|---|
| Monthly Infrastructure TCO | 40% reduction via Azure SQL/PaaS elimination and Graviton3 adoption |
| I/O Throughput | 3x improvement through EBS gp3 adoption; I/O throttling permanently resolved |
| New AI Model Deployment Lead Time | Reduced from 3 months → 1 week via Amazon Bedrock |
| Database CPU Utilization (peak traffic) | Maintained below 50% via Aurora Read Replica Auto Scaling |
| Service Downtime During Migration | Reduced to under 1 hour via AWS DMS real-time replication |
Conclusion
By migrating to AWS with NXT Cloud as its transformation partner, BrainCrew successfully eliminated vendor lock-in, resolved longstanding infrastructure performance constraints, and established a scalable, cost-efficient LLMOps foundation capable of supporting rapid AI model adoption at production scale.