Platform engineering for AI workloads.
Give product teams a reliable path to ship AI without waiting on infrastructure. We build the cloud, Kubernetes and GPU platform behind it, with security and cost controls your team can operate.
Outcomes for your business
First tangible output: Infrastructure as code
The short answer
What is platform engineering for AI?
Platform engineering for AI is building the shared infrastructure that AI applications run on: cloud accounts and networking, Kubernetes clusters, GPU capacity for training and inference, deployment pipelines, observability and cost controls. It gives product teams a paved road so they ship AI features without rebuilding infrastructure each time.
Sound familiar?
You need this if…
What we do
Scope of the work.
Cloud foundations
AWS and Azure landing zones with identity, networking and guardrails.
Kubernetes platforms
Production clusters with autoscaling, policies and self-service for teams.
GPU capacity
Training and inference capacity sized, scheduled and shared across workloads.
Infrastructure as code
Every environment defined in code, reviewed and reproducible.
Cost controls
Tagging, budgets, autoscaling and right-sizing so spend tracks value.
Observability
Metrics, logs and traces across clusters, models and pipelines.
Deliverables
What you walk away with.
Everything is yours to keep, whether you build with us or on your own.
Infrastructure as code
Your platform, defined and versioned in your repositories.
Production clusters
Kubernetes and GPU capacity running your workloads.
Deployment pipelines
A standard path from commit to production.
Cost and health dashboards
Spend by team and workload, plus platform health.
Platform guide
How teams onboard, deploy and get support.
How it works
Four steps, one team.
Assess
Review current infrastructure, workloads, costs and pain points.
Design
Define the platform, from accounts and clusters to GPU strategy.
Build
Stand it up in code, migrate a first workload and prove it.
Scale
Onboard teams, tune costs and hand over operations.
Powered by our platform CloudTeams™
Build work runs on one record
Platform engineering: AWS, Azure, K8s & GPU runs on the CloudTeams™ Platform, the AI-powered platform we built for firms that sell expertise. Mach is its first customer.
- Projects, milestones and hours tracked on one record
- Timesheets, expenses and invoices tied to the work
- AI-assisted workflows with a person approving anything that ships
Keep exploring
Related services.
Target-State Architecture: AI, Cloud & GPU
We design the architecture your AI roadmap needs, from data and retrieval to models, agents, cloud and GPU capacity, and we design it knowing we may be the ones who build it.
Explore →AI-First Solution Development
We build AI products, agents and features that survive contact with real users: retrieval that finds the right context, evals that catch regressions, and guardrails that keep outputs safe.
Explore →Project Pods & Staff Augmentation
An architect-led engineering pod with clear ownership of milestones, quality and handoff to your team.
Explore →Field Notes
Go deeper.
Practical playbooks from the team that does this work.
From AI Pilot to Production: The 30-Day Engineering Playbook
Why AI pilots stall and how to ship them: a week-by-week playbook covering evals, guardrails, integration and agentic-first engineering.
Read the field note →Playbooks for teams that ship.
Practical guides on AI architecture, agentic-first engineering, and technical go-to-market.
Browse all field notes →FAQ
Questions about platform engineering for AI
Straight answers about platform engineering: AWS, Azure, K8s & GPU. Still curious? Ask us on a free call.
Do you work on AWS, Azure or both?
Both, plus Kubernetes and specialty GPU providers. We build on whatever mix fits your workloads and contracts.
How do you reduce GPU costs?
Right-sizing, autoscaling, scheduling, sharing capacity across workloads, and using the right instance types for training versus inference.
Do we need Kubernetes?
Not always. We recommend it when you have multiple services and teams. For simpler needs, managed services may be the better answer.
Will our team be able to run it?
Yes. Everything is in code, documented, and we train your engineers before handing over operations.
Can you support the platform long term?
Yes. Some clients keep us on for platform operations or as a project pod.
Project inquiry
Tell us about your project.
Tell us where you are and what you need. We will tell you honestly whether platform engineering: AWS, Azure, K8s & GPU is the right next step.
- 01Tell us where you are headedA two-minute form. Share the outcome you want and any constraints.
- 0245-minute discovery callWe listen first. No pitch deck, no sales script.
- 03Your readout within one weekWhere the gaps are and which practice will move the needle fastest.
Ready to choose a time? Schedule your free consultation →
Tell us about platform engineering for AI
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