Infrastructure-as-Code for AI Deployments
Why "it works on my laptop" isn't an acceptable AI deployment strategy.
The Problem with AI Prototypes
Most enterprise AI projects start as prototypes: a data scientist runs a notebook, gets promising results, and the team decides to "productionize" it. The problem? That notebook has dozens of implicit dependencies, hardcoded paths, and manual configuration steps that nobody documented.
When the original developer leaves, the entire system becomes a black box. When a security patch is needed, nobody knows which containers to rebuild. When the CEO asks "can we deploy this in our EU region?", the answer is "maybe, in 6 months."
IaC for AI: The Core Principles
Infrastructure-as-Code (IaC) treats your entire deployment — compute, networking, storage, model serving, vector databases — as version-controlled code. Every configuration decision is explicit, reviewable, and reproducible.
- Declarative definitions: Describe WHAT you want, not HOW to build it. The tooling handles provisioning order and dependency resolution.
- Version control: Every infrastructure change goes through the same code review process as application code.
- Idempotency: Running the same configuration twice produces the same result — no drift, no surprises.
- Environment parity: Dev, staging, and production use the same templates with different parameters.
What We Codify
For every AI deployment, our IaC templates cover: VPC and subnet configuration, security groups and network policies, GPU instance provisioning and auto-scaling, model serving container definitions, vector database cluster setup, load balancer and SSL termination, monitoring dashboards and alerts, backup schedules and retention policies, and IAM roles with least-privilege access.
The Payoff
With IaC, we can spin up a complete AI environment in under 30 minutes. Need a second region for disaster recovery? Apply the same template with different region parameters. Need to rebuild after a security incident? Destroy and recreate the entire stack from code. New client with identical architecture requirements? Fork the template and customize.
This isn't about being "DevOps-cool." It's about making AI deployments that are auditable, reproducible, and recoverable — the same standards we hold for every other piece of critical business infrastructure.