40% Cost Reduction and 3x Faster Releases for AI Analytics Infrastructure on Azure

40% Cost Reduction and 3x Faster Releases for AI Analytics Infrastructure on Azure
We rebuilt the cloud architecture for an enterprise AI and analytics provider by replacing idle virtual machines with managed Azure services, adding automated pipelines, and hardening security. The result: 40% lower infrastructure spend, significantly faster deployment cycles, and a compliant, production-grade platform ready for scale.
COST SAVINGS
60% reduction in monthly cloud spend
DATA VOLUME MIGRATED
10TB across 400M individual files
GLOBAL DELIVERY
<300ms latency worldwide via Azure CDN
The existing infrastructure was expensive, static, and too slow to support real-time analytics at enterprise scale.
Overprovisioned VMs with poor resource efficiency
Dozens of underutilized virtual machines increased costs without improving performance or flexibility.
Release cycles blocked by manual workflows
With no automation in place, even basic changes involved multiple manual steps and environment drift.
Security and compliance coverage gaps
Sensitive data had to meet growing compliance requirements, but the legacy stack lacked proper controls and monitoring.
We transitioned to a serverless-first model with built-in automation, monitoring, and secure access control.
Infrastructure redesign using managed Azure services
We migrated from VMs to Azure Functions, Container Instances, and App Services for dynamic scaling and better cost control.
Automated deployment pipelines via Azure DevOps
End-to-end automation reduced errors and manual labor, while supporting faster testing and more frequent releases.
Enterprise-grade security tooling
Implemented Key Vault for secrets, RBAC for access control, and Azure Monitor for full observability and compliance tracking.
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Technologies we used
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Microsoft Azure
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Azure DevOps
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Azure Functions
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Azure App Services
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Azure Key Vault
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Azure Container Instances
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Azure Monitor
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Docker
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PostgreSQL