Large Language Models (LLMs) are opening powerful new possibilities for AI applications and intelligent agents. But with these opportunities come real infrastructure challenges:
GPU resources are costly and need careful management
Model serving requires scalable, reusable templates
Security around API keys and sensitive data is essential
What’s Important to Know
Create self-service infrastructure templates so AI teams can move quickly
Apply cost controls for GPU workloads to avoid waste
Use proven security patterns for handling sensitive data and API keys
Provide integration approaches for common AI-powered use cases
Create self-service infrastructure templates so AI teams can move quickly
Apply cost controls for GPU workloads to avoid waste
Use proven security patterns for handling sensitive data and API keys
Provide integration approaches for common AI-powered use cases
IaC Best Practices for AI Workloads
To make this work in practice, Infrastructure as Code (IaC) is a must. At CloudOr, we work with both Terraform and Pulumi to design scalable AI-ready platforms. A few best practices include:
Reusable modules – define GPU clusters, model serving endpoints, and monitoring as templates to avoid duplication.
Policy as code – enforce cost limits, access controls, and GPU quotas automatically.
Secrets management – never hardcode API keys or credentials; integrate with a vault or secrets manager.
Automation first – every step, from provisioning GPUs to deploying models, should be automated to reduce manual effort and errors.
Why This Matters
As more teams adopt AI, DevOps and platform engineers must ensure infrastructure is simple to consume, secure by design, and cost-efficient. Tools like Terraform and Pulumi provide the foundation for building repeatable, safe, and scalable environments tailored for AI applications.
If you’d like to connect and discuss AI, DevOps, and cloud platforms, reach me on LinkedIn.