Cluster architecture
Production-grade clusters with the security, networking and multi-tenancy AI platforms require.
Engineering Excellence
Kubernetes & Containers
Run inference, agents, and data services on Kubernetes with autoscaling, isolation, and observability.
- 70%+GPU utilization on tuned clusters
- 99.95%workload availability
- 45%typical compute cost reduction
Capabilities
Kubernetes tuned for AI workloads
Container platforms that scale AI workloads with confidence and efficiency.
GPU orchestration
Scheduling, sharing and autoscaling that squeeze real utilization from scarce accelerators.
GitOps operations
Declarative cluster and workload management with drift detection and instant rollback.
Cost and capacity engineering
Bin-packing, spot strategies and right-sizing that cut the compute bill.
How it works
The runtime layer for serious AI platforms
Model serving, agents, vector stores and pipelines all land on Kubernetes eventually. We make that landing reliable, secure and affordable.
GPU efficiency
Stop paying for idle accelerators
Time-slicing, MIG partitioning, priority-based scheduling and inference-aware autoscaling — the techniques that take GPU utilization from embarrassing to efficient.
- GPU sharing and partitioning for mixed workloads
- Queue-based scheduling with preemption for priority jobs
- Utilization dashboards per team and workload
Private VPC
GPU poolAutoscaleMulti-AZ
AWSAzureGCPOn-prem
Reliability
Clusters that fail gracefully
Pod disruption budgets, topology spread, health-gated rollouts and tested failover — engineered so node failures and upgrades never become user-facing incidents.
- Zero-downtime upgrades rehearsed, not hoped for
- Multi-AZ topology with tested failure modes
- Golden signals wired to actionable alerts
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Developer experience
Platform, not puzzle
GitOps-driven deployments, namespace-as-a-service, sane defaults and paved-path manifests — teams ship to Kubernetes without becoming Kubernetes experts.
- Self-serve namespaces with quotas and policies
- ArgoCD-managed deployments with drift detection
- Templates that make the secure path the easy path
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
Use cases
Where kubernetes & containers delivers value
Model serving platforms
vLLM and Triton inference at scale with GPU autoscaling.
Agent runtime infrastructure
Long-running, bursty agent workloads scheduled efficiently.
ML training clusters
Batch and distributed training with fair-share scheduling.
Platform consolidation
Sprawling VMs and one-off servers unified onto governed clusters.
Hybrid and on-prem AI
Consistent orchestration across cloud and data-center GPUs.
Cost recovery programs
Utilization and bin-packing fixes that show up on the invoice.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Same GPUs, same models — utilization went from 18% to 74% after the scheduling rebuild. That difference was seven figures a year we were simply burning.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Kubernetes
- EKS
- GKE
- AKS
- vLLM
- Triton
- ArgoCD
- Helm
- Karpenter
- Prometheus
- Grafana
- Istio
FAQ
Common questions
Is Kubernetes overkill for our AI workloads?
Sometimes — small teams with a handful of services may not need it yet. But once you're serving models, running agents and scaling pipelines, orchestration pays for itself. We'll tell you honestly which side of that line you're on.
How do you actually raise GPU utilization?
Measurement first, then scheduling: workload profiling, GPU sharing for small models, batch queues for training, autoscaling tuned to real traffic. Most clusters we audit start below 25% utilization; tuned ones run above 70%.
Managed Kubernetes or self-hosted?
Managed (EKS/GKE/AKS) unless you have strong on-prem or sovereignty reasons. Your differentiation lives above the control plane, not in operating etcd.
Can you take over an existing cluster mess?
Yes — assessment, stabilization, then incremental refactoring to GitOps and paved paths. No big-bang migrations that risk production.
Next step
Get serious about your runtime
We'll audit your clusters and GPU utilization — the findings usually pay for the engagement.