Platform engineers are asking for standardized templates and blueprints to streamline AI deployment and reduce repetitive, manual work—and in fact, platform engineering teams have been creating blueprints for internal and cloud projects for years.
The blueprints, once established, allow them to focus on innovative and unique solutions that each project is facing instead of having to worry about the entire scope.
This is important as a large percentage of projects are standard in nature – for instance, some web servers, app servers, load balancers, application firewalls and rules, roles and permissions, and database servers.
“Recreating this common infrastructure as code and connecting it all is an expensive waste of time and slows down innovation,” says Tim Bruce, solutions architect at Artisan Studios.
He says platform engineers see that there are a lot of AI projects coming in, and these projects change a lot of their existing blueprints and partially because of generative AI, these projects are coming at them faster.
“This leaves little time for platform engineers to build the blueprints to help them move these projects along quickly and securely,” he says. “The blueprints can also help them avoid costly and serious mistakes that can slow down innovation and harm their companies.”
They transform proven architectures, such as a compliant Azure OpenAI deployment or an MLflow-based training pipeline, into reusable, versioned modules. That means faster setup, predictable security posture, and repeatability across environments. Platform engineers can focus on evolving those templates instead of debugging every new AI use case. It’s the difference between artisanal infrastructure and industrial-scale enablement.
In short, templates bring speed through standardization and safety through consistency. They make AI deployment not only faster, but also governable at an enterprise scale.
Derek Ashmore, agentic AI enablement principal at Asperitas, says a strong AI infrastructure template does more than spin up compute, it encodes architecture, compliance, and operational discipline into reusable form.
“At a minimum, it should include standardized network and identity controls, with private endpoints, managed identities, and least-privilege access baked in,” he says.
Add to that data and model governance hooks for integration with cataloging, lineage, and approval workflows for datasets and model versions.
“Cost and resource policies for enforced quotas, tagging for charge-back, and lifecycle rules for expensive GPU or storage resources are also important,” Ashmore says.
A strong template also includes observability and MLOps integration for logging, telemetry, and audit trails wired in from day one and compliance guardrails enforced automatically through Terraform or GitOps pipelines.
“The key is to treat governance as a design primitive, not a bolt-on,” Ashmore says.
When platform teams build templates this way, every new AI workload inherits enterprise security, auditability, and cost efficiency automatically.
“It transforms compliance from a manual checklist into a property of the system itself,” he says.
Bruce says platform engineers who are used to ad hoc should look at what system administrators (SAs) did a long time ago: Become lazy.
“SAs don’t like doing the same thing more than twice. If they run into a problem and see that it will repeat, they automate a solution,” he says.
This “laziness” allows them to focus on the things they really need to focus on, which is enabling the users of those servers to create more business value.
“After all, a valuable system that is down will not create value,” Bruce says. “Platform engineers who aren’t automating should focus on how they can help generate value faster by moving to blueprints and enabling their project teams to create value faster.”
He recommends they start small by automating the parts of projects they enjoy the least and then working their way up from there.
Ashmore says template catalogs and infrastructure blueprints are headed for the same status CI/CD pipelines and IaC repos enjoy today.
“As AI adoption scales, enterprises can’t afford every team hand-crafting GPU environments, data governance controls, or model-serving stacks from scratch,” he says.
Blueprints become the “factory floor” for AI infrastructure: consistent, governed and rapidly deployable.
“They are how platform teams scale without chaos,” Ashmore explains. “AI will accelerate this shift.”