INFRAHUB FOR AI DATA CENTERS

Design your AI data center once. Deploy it over and over.

AI data centers are some of the largest, most capital-intensive infrastructure investments in the world. Get to ROI faster with Infrahub.

Deploy at speed

Build every layer of the fabric from your design in a single run. Pods and racks generate in parallel.

Consistent every time

Every site is generated the same way from the same validated design. One-offs and drift don't accumulate across sites.

Day Two included

Design intent is stored with the infrastructure it produces. Operate and expand with the same automation you deployed with.

Model anything, no plugins required

Infrahub’s flexible schema models everything in your data center, from EVPN/VXLAN overlays, BGP underlays, and physical cabling to optical interconnects, GPU cluster topologies, and backend fabrics like InfiniBand and RoCEv2.

Define your own design hierarchy: Your schema reflects how your organization builds, not how a vendor assumes you build.
Cover the full stack: Front-end and backend fabrics, optical interconnects, and GPU topologies are first-class objects.
Query relationship data: Traverse pods, racks, BGP sessions, VNIs, and cabling without stitching separate tools together.

Explore a reference design that models a full fabric →

Deploy repeatably with speed and precision

Infrahub Generators are modular. Each Generator builds one layer: a fabric, pod, or rack.

One layer at a time: Each one is small enough to understand, test, and debug in isolation.
Parallel execution at every layer: A fabric with 8 pods and 32 racks per pod generates all at once, not sequentially.
Surgical Day Two changes: Add a spine to a pod and only that pod’s Generator reruns. No blast radius.

Learn more about how Infrahub Generators work →

Evolve without disruption

Your AI data center won’t look the same in a few months. Infrahub stores design intent as a living data model so changes are absorbed rather than rebuilt.

Your schema grows with you: Add attributes, relationships, or object types without breaking existing automation.
Only affected objects change: Update a design parameter and Infrahub reruns only what’s downstream.
Adapt as standards shift: New GPU generations or fabric types need only a schema update, not a rebuild.

Why precise changes matter for Day Two operations →

"Every engineer, despite having config templates, creates one-offs or slight differences depending on who did the copy and paste. My biggest problems are config drift and speed of deployment. This year we're scoped to deploy 40 data center buildings, and we can't do that without Infrahub."

— Principal Network Engineer, Hyperscale Colocation Provider

AI DATA CENTER REFERENCE DESIGN

Start from a working build

See how Infrahub delivers on an AI data center build, or shorten your own implementation by starting from a working one. Run the reference design, read the code, and adapt it to your design and vendors.

What's included

A complete, working example of AI data center automation: the design model, the automation that builds every layer of the fabric, and the configuration output for Cisco, Arista, and Dell—ready to run and adapt.

Get it on GitHub

Read the docs

This is a reference implementation of the automation, not a certified network design.