AI & MLOps Infrastructure

AI infrastructure that actually works

Your model works in the notebook. Now it needs to work in production — at scale, with governance, without burning through your GPU budget.

We build and operate the GPU-enabled Kubernetes platforms that take AI from experiment to production.

Talk to an engineer See the $340K savings story
$340K
Monthly GPU savings achieved
94%
GPU utilization (from 40%)
3x
Faster model training cycles
90%
Faster inference deployment

The model isn't the hard part

Your data scientists built something remarkable in a Jupyter notebook. Now someone has to make it run in production — reliably, at scale, within budget, with governance. That's where most AI initiatives stall.

87%

of ML models never reach production

60%

of ML team time spent on infrastructure

40%

average GPU utilization (industry)

The Platform

Production ML infrastructure, not science projects

We build the complete MLOps platform — from data pipeline to production inference — on Kubernetes infrastructure designed for GPU workloads.

01

Data Pipeline

High-throughput data ingestion and preprocessing at scale. We architect pipelines that handle petabytes without breaking your budget.

Distributed data processingFeature storesData versioningPipeline orchestration
02

Training Infrastructure

GPU-optimized Kubernetes clusters designed for ML training workloads. Multi-node distributed training, checkpointing, and experiment tracking.

Multi-GPU schedulingDistributed trainingExperiment trackingModel registry
03

Model Serving

Production inference at scale with KServe, Triton, or custom serving solutions. Auto-scaling, A/B testing, and canary deployments included.

KServe / TritonAuto-scaling inferenceA/B testingModel monitoring
04

Operations & Governance

The part everyone forgets: monitoring, alerting, cost controls, and compliance. Your AI platform needs operational discipline, not just notebooks.

Model monitoringDrift detectionCost controlsCompliance automation
GPU Optimization

Stop wasting money on idle GPUs

GPU infrastructure is expensive. Most organizations run at 40% utilization or less. We fix that.

Problem

40% average GPU utilization

Solution

Right-sized scheduling and bin-packing

Outcome

94% utilization achieved

Problem

$1.2M/month GPU spend

Solution

Spot instances + preemption handling

Outcome

$340K/month savings

Problem

3-day training jobs failing

Solution

Checkpointing + distributed training

Outcome

4-hour training cycles

Problem

Weeks to deploy a model

Solution

GitOps + KServe automation

Outcome

Same-day deployment

Case Study

Series C AI company cuts GPU costs by $340K/month

Technology

The Challenge

A Series C AI company was spending $1.2M/month on GPU infrastructure with only 40% average utilization. Training jobs took 3 days and frequently failed. Model deployment required manual intervention and took weeks.

Our Approach

  • Audited GPU scheduling and identified utilization bottlenecks
  • Implemented GPU bin-packing and fractional GPU allocation
  • Deployed spot instance strategy with graceful preemption handling
  • Built GitOps pipeline for model deployment with KServe
  • Implemented distributed training with automatic checkpointing

Results

$340K/mo

GPU cost savings

94%

GPU utilization

3x

Faster training

Same-day

Model deployment

FAQ

Frequently asked questions

Your data scientists are experts at building models — not at running Kubernetes, optimizing GPU scheduling, or building production serving infrastructure. We handle the platform so they can focus on the science. Most ML teams we work with spend 60%+ of their time on infrastructure. We flip that ratio.
MLOps adds model-specific concerns: experiment tracking, model versioning, feature stores, training infrastructure, serving infrastructure, model monitoring, and drift detection. A DevOps team that hasn't worked with ML workloads will miss critical requirements around GPU scheduling, data pipelines, and model lifecycle management.
Yes. We work with MLflow, Kubeflow, Ray, Weights & Biases, SageMaker, Vertex AI, and most major ML platforms. We're not here to replace your tools — we're here to make them work together in production.
GPU costs are the #1 concern for every AI team we work with. We implement: right-sized GPU allocation (most teams over-provision), bin-packing to maximize utilization, spot/preemptible instances with graceful handling, time-boxing for training jobs, and idle detection with automatic scale-down. Typical savings: 30-50% of GPU spend.
We've deployed Kubernetes on on-prem NVIDIA DGX clusters, custom GPU servers, and hybrid environments. On-prem adds complexity around GPU drivers, networking, and storage — but it's often the right choice for cost or data sovereignty reasons. We'll help you decide and implement either path.
GPU cost optimization typically shows results in 2-4 weeks — it's often quick wins around scheduling and right-sizing. Full MLOps platform buildout is typically 8-12 weeks to production-ready state. We deliver incrementally, so you're seeing value throughout the engagement.
No. We build the infrastructure that trains models. We're platform engineers, not data scientists. We'll make your ML team dramatically more productive, but we won't build your models for you.

Technology Partners

AWS Microsoft Azure Google Cloud Red Hat Sysdig Tigera DigitalOcean Dynatrace Rafay NVIDIA Kubecost
Proof Point

AI/ML infrastructure at scale

AI/ML Series C

$340K/mo GPU savings for a Series C AI platform

GPU utilization jumped from 40% to 94% after we deployed scheduling policies, spot instance strategies, and real-time cost visibility across their Kubernetes clusters.

Read the full case study →
Insights

Related Reading

Scaling Cloud Native Apps Without Wasting Money

Right-size infrastructure and auto-scale workloads to optimize cost and performance.

Monitoring Cloud Native Apps: Practical Guide

Monitor ML inference endpoints and GPU workloads with cloud-native observability tools.

Kubernetes Networking: Services, CNI & Mesh

Understand Kubernetes networking fundamentals for high-throughput ML training and serving.

Ready to make AI operational?

Whether you're planning GPU infrastructure, stabilizing Kubernetes, or moving AI workloads into production — we'll assess where you are and what it takes to get there.

Schedule an Infrastructure Assessment Call Us Directly

US-based team · All US citizens · Continental United States only