"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
- Rob Machado
AI EngineeringAI Infrastructure & MLOps
Build the Infrastructure AI at Scale Demands
Design and operate production ML infrastructure — from GPU clusters and model serving to CI/CD pipelines for models — so your AI teams ship faster, more reliably, and at lower cost.
Infrastructure Capabilities
MLOps & AI Infrastructure
From GPU provisioning to production model monitoring, we build and manage the infrastructure layer your AI systems depend on.
Cloud AI Platform Architecture
Design AWS, Azure, or GCP AI infrastructure with auto-scaling GPU clusters, managed training environments, and cost-optimized serving layers.
Model Serving & Inference
Deploy models with sub-100ms latency using vLLM, TensorRT, Triton Inference Server, or managed endpoints — optimized for your throughput requirements.
ML CI/CD Pipelines
Automated pipelines for model training, evaluation, and deployment — so code changes trigger model updates the same way software deploys work.
GPU Cluster Management
Kubernetes-based GPU scheduling with Karpenter, NVIDIA operator, and spot instance optimization for 60–70% infrastructure cost reduction.
Model Monitoring & Drift Detection
Real-time monitoring of prediction quality, data drift, and feature distribution shifts — with automated retraining triggers when models degrade.
AI Security & Compliance
Private model deployments, VPC isolation, model artifact signing, access auditing, and compliance frameworks for regulated industries.
Why Choose Us
Why Agile Infoways for AI Infrastructure
We've designed ML platforms serving billions of inferences monthly for companies from Series B to Fortune 500.
Cost Optimization First
We've reduced client GPU and inference costs by 40–70% through spot instances, quantization, batching, and right-sizing strategies.
99.9% Availability SLAs
Production AI infrastructure with multi-region failover, blue-green deployments, and circuit breakers for zero-downtime model updates.
Platform Engineering Depth
Deep expertise in Kubernetes, Terraform, Helm, Argo Workflows, and Kubeflow — not just AI tools but the infrastructure layer beneath.
Enterprise Compliance
SOC 2, HIPAA, and FedRAMP-aligned AI infrastructure with private endpoints, encryption at rest/transit, and complete audit trails.
Our Capability
Infrastructure Stack
Industry-leading tools for every layer of production AI infrastructure.
vLLM / Triton Server
High-throughput LLM inference with continuous batching, quantization, and GPU memory optimization.
Kubeflow / MLflow
End-to-end ML pipeline orchestration with experiment tracking, model registry, and deployment.
AWS SageMaker / Azure ML
Managed ML platforms for training at scale with spot instance fleets and auto-scaling endpoints.
Evidently / WhyLabs
Production model monitoring with statistical drift detection and automated alerting.
Feature Stores (Feast)
Centralized feature management ensuring training-serving consistency across ML models.
Ray / Dask Distributed
Distributed computing frameworks for large-scale model training and batch inference jobs.
Our ApproachHow We Build
How We Build
AI Platforms
From infrastructure audit through production deployment with cost optimization at every stage.
01
Infrastructure Audit & Design
02
Platform Foundation
03
ML Pipeline & Serving Layer
04
Monitoring, Optimization & Handoff
Step 01
Infrastructure Audit & Design
Review current AI infrastructure, benchmark costs and performance, identify gaps, and design a target architecture aligned to your AI roadmap.
Current state auditCost breakdownGap analysisTarget architecture
Step 02
Platform Foundation
Build Kubernetes clusters with GPU support, set up Terraform IaC, configure networking, security groups, and core platform services.
K8s GPU clusterTerraform IaCNetwork securitySecrets management
Step 03
ML Pipeline & Serving Layer
Implement model training pipelines, experiment tracking, model registry, serving infrastructure, and automated deployment workflows.
Training pipelinesModel registryServing endpointsCI/CD for models
Step 04
Monitoring, Optimization & Handoff
Deploy observability stack, configure drift detection alerts, optimize infrastructure costs, and train your team on ongoing platform management.
Observability dashboardsCost optimizationTeam trainingRunbook documentation
Our ApproachHow We Build
How We Build
AI Platforms
From infrastructure audit through production deployment with cost optimization at every stage.
Step 01
Infrastructure Audit & Design
Review current AI infrastructure, benchmark costs and performance, identify gaps, and design a target architecture aligned to your AI roadmap.
Current state auditCost breakdownGap analysisTarget architecture
Step 02
Platform Foundation
Build Kubernetes clusters with GPU support, set up Terraform IaC, configure networking, security groups, and core platform services.
K8s GPU clusterTerraform IaCNetwork securitySecrets management
Step 03
ML Pipeline & Serving Layer
Implement model training pipelines, experiment tracking, model registry, serving infrastructure, and automated deployment workflows.
Training pipelinesModel registryServing endpointsCI/CD for models
Step 04
Monitoring, Optimization & Handoff
Deploy observability stack, configure drift detection alerts, optimize infrastructure costs, and train your team on ongoing platform management.
Observability dashboardsCost optimizationTeam trainingRunbook documentation
Use Cases
Infrastructure Deployments
Real MLOps platforms delivering scale, reliability, and cost efficiency.
AD
AdTechReal-Time Bidding ML Platform
The Challenge
Ad platform serving 50M predictions/day on outdated infrastructure with 200ms latency and $800K monthly GPU bills.
The Outcome
Rebuilt on vLLM + Kubernetes spot fleet: latency dropped to 18ms P99, infrastructure cost reduced by 65%.
vLLMKubernetesSpot instancesReal-time serving
FI
FintechFraud Detection MLOps Platform
The Challenge
Data science team taking 3 weeks to deploy model updates due to manual deployment process and no staging environment.
The Outcome
Automated ML CI/CD cut deployment time to 4 hours, with shadow mode testing ensuring no regression in fraud detection accuracy.
MLflowKubeflowShadow deploymentDrift monitoring
HE
Healthcare AIHIPAA-Compliant AI Infrastructure
The Challenge
Healthcare AI startup unable to sell to enterprise clients without SOC 2 and HIPAA-compliant infrastructure.
The Outcome
Designed and deployed private VPC AI platform with PHI controls, audit logging, and BAA-compliant architecture — unlocking enterprise sales.
Private VPCHIPAA controlsAudit trailsBAA compliance
E-
E-commerceRecommendation Engine Infrastructure
The Challenge
Recommendation models retrained weekly manually, with no monitoring — silent accuracy degradation went undetected for months.
The Outcome
Automated daily retraining pipeline with drift detection cut model staleness and improved recommendation CTR by 23%.
Feast feature storeEvidently monitoringAuto-retrainingA/B testing infra
Industries
AI Solutions Across Key Verticals
Deep domain expertise meets cutting-edge AI — delivering results where they matter most.
Client Stories
Built With Trust. Proven in Production.
Hear directly from the leaders who partnered with us to ship AI-powered products, modernize platforms, and move faster than they thought possible.
"Great company with great management quality developers were really dedicated to get the job done in a timely cost-effective manner."
- Alexandar Salahsour
"They consistently delivers reliable, high-quality development solutions with exceptional communication, value, and trusted partnership."
- Joe Pellegrino, Jordan Pellegrino
Get In Touch
Let's Build Something Remarkable Together
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