AI Infrastructure
Build infrastructure your AI workloads can actually scale on.
AI workloads break the assumptions most cloud platforms were designed around. We design and build GPU-aware, Kubernetes-native infrastructure for training, inference and everything between.
- 01Developer
- 02Model / Application
- 03Model Serving
- 04Kubernetes
- 05GPU Scheduler
- 06GPU Compute
- 07Observability
The problem
What's actually going wrong.
GPUs sit idle while jobs queue
Expensive accelerators are allocated statically, fragmented across teams, and rarely shared well. Utilization is invisible until the bill arrives.
Inference is bolted on
Models are served from ad-hoc containers without autoscaling, batching, canary rollouts or latency budgets.
Scheduling was built for web apps
Default Kubernetes scheduling doesn't understand gang scheduling, queues, quotas or topology-aware GPU placement.
No observability for models
Teams can see CPU and memory, but not GPU saturation, token throughput, queue depth or per-model cost.
What we do
AI Infrastructure capabilities.
GPU infrastructure
Node pool design, accelerator selection, MIG/time-slicing strategies, driver and device-plugin lifecycle.
GPU scheduling & queuing
Batch and gang scheduling with Kueue or Volcano, fair-share quotas and priority classes across teams.
AI workloads on Kubernetes
Training jobs, distributed workloads and pipelines running reliably on shared clusters.
Model serving & inference
Serving stacks with autoscaling, request batching, rollout strategies and latency SLOs.
Workload orchestration
Pipelines and job orchestration that move models from experimentation into repeatable production runs.
AI observability
GPU telemetry, inference latency, throughput and cost attribution wired into your existing stack.
Cloud AI platforms
Pragmatic integration of managed services such as SageMaker or Vertex AI where they fit the problem.
AI infrastructure optimization
Right-sizing, spot/preemptible strategies and utilization improvements for compute-heavy workloads.
Technical capabilities
Technologies and practices.
Examples of tools we work with. Listed as capabilities, not certifications or partnerships.
- Kubernetes
- Kubeflow
- Volcano
- Kueue
- NVIDIA GPU Operator
- KServe
- Ray
- SageMaker
- Vertex AI
- Prometheus
- Terraform
Outcomes
What changes.
Shared, scheduled GPU capacity
Teams draw from governed queues instead of hoarding dedicated nodes.
Production-grade inference
Models served with autoscaling, safe rollouts and latency objectives.
Visible AI cost
Compute spend attributable to models, teams and workloads.
How we engage
Assessment → Architecture → Implementation → Enablement.
- 01
Discover
Understand the architecture, constraints and engineering bottlenecks.
- 02
Architect
Define the target architecture and implementation roadmap.
- 03
Build
Implement infrastructure, automation, platforms and reliability improvements.
- 04
Enable
Transfer knowledge and create sustainable engineering practices.
FAQ
Common questions.
Related insights
Further reading.
Building Production-Ready AI Infrastructure
Training and inference place very different demands on infrastructure. A practical view of what production-ready means.
ReadKubernetes for AI and ML Workloads
When Kubernetes is the right substrate for AI workloads, and what you need to add to make it work.
ReadGPU Infrastructure and Scheduling
Gang scheduling, queues, quotas and topology: making expensive accelerators actually get used.
ReadRelated services
Often combined with.
MLOps
Move machine learning from experimentation to production.
ML platforms, pipelines, deployment, model serving, monitoring and production ML infrastructure.
Explore MLOpsKubernetes
Kubernetes without the operational chaos.
Cluster design, production readiness, security, scaling, GitOps and workload optimization.
Explore KubernetesCloud Cost Optimization & FinOps
Make cloud costs visible, measurable and actionable.
Cloud cost analysis, Kubernetes and AI cost optimization, architecture optimization and governance.
Explore Cloud Cost Optimization & FinOpsTalk to an Infrastructure Expert.
Tell us what you're building and where infrastructure is getting in the way. You'll talk directly to a senior engineer.