"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
- Rob Machado
AI EngineeringAI Infrastructure & MLOps
Modern AI Infrastructure for Confident AI Growth
Build a secure, scalable AI infrastructure that keeps your AI initiatives running efficiently. Our AI Infrastructure & MLOps experts design cloud-native environments, automate model deployment, monitor performance, and optimize GPU, Kubernetes, and data pipelines for enterprise-scale AI. Whether you're launching generative AI, predictive analytics, or intelligent automation, you can hire AI engineers from our team to accelerate delivery, reduce operational complexity, and maximize ROI with reliable, production-ready AI capabilities.
Infrastructure Capabilities
MLOps & AI Infrastructure
From GPU provisioning to MLOps consulting and model serving, we build and operate the infrastructure layer production 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 Agile Infoways for AI Infrastructure
We've designed machine learning infrastructure serving billions of inferences monthly for companies from Series B to Fortune 500.
Our Capability
Infrastructure Stack
Industry-leading tools for LLM inference, model deployment, monitoring, and 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 Built for Every Industry
Every industry re-imagined with our enterprise AI services. We are reinventing every industry and creating a unique solution that moves our clients forward and disrupts the market.
Before You Build
Evaluating MLOps & Infrastructure
What teams ask before standing up production AI infrastructure — MLOps vs DevOps, what you need, model drift, cost, governance, and team.
8 questions answered
01
What is MLOps, and how is it different from DevOps?
MLOps is the practice of deploying, monitoring, and continuously improving machine-learning models in production. It extends DevOps but adds what models need: data and feature versioning, automated retraining, drift detection, and model-specific CI/CD. DevOps ships code that behaves predictably; MLOps manages models that decay as data changes. It builds on solid DevOps foundations.
02
What AI infrastructure do we actually need to run AI in production?
Production AI infrastructure has four layers: compute (GPU or cloud accelerators), model serving for low-latency inference, an MLOps pipeline for deployment and retraining, and observability for performance, cost, and drift. Underneath it all sits reliable data engineering — without clean, served data, the rest can't deliver accurate results. We design and operate the whole stack.
03
How do you keep models from degrading after they’re deployed?
Models degrade as real-world data drifts from what they trained on, so we monitor predictions continuously, alert on accuracy and data drift, and trigger automated retraining when quality slips. Shadow deployments and canary releases catch regressions before users feel them. This keeps your custom AI models accurate in production long after launch.
04
How do you control GPU and cloud costs for AI?
AI cost control comes from right-sizing compute, not just buying more GPUs. We use autoscaling, spot and reserved instances, model quantization and batching, and caching to cut inference spend — often 40–60% — while holding latency targets. Continuous cost monitoring flags waste early. Our DevOps engineers tune the infrastructure so performance and budget both hold.
05
How do you handle AI governance and compliance?
AI governance means knowing what every model does, on what data, and with what controls. We add model registries, audit logs, access controls, bias and quality checks, and documentation aligned to frameworks like the EU AI Act, SOC 2, and HIPAA. Governance is planned through AI strategy consulting, then enforced in the infrastructure layer.
06
Should we build an internal MLOps team or outsource it?
Build internal MLOps when AI is core to your product and you have steady, long-term model volume; outsource or augment when you need senior expertise fast, lack production-MLOps experience, or want to ship before hiring catches up. Many teams do both — a small core plus dedicated AI/ML engineers who stand up the platform and transfer knowledge.
07
Do you offer MLOps consulting and platform setup?
Yes. We offer MLOps consulting and platform setup for teams that need architecture, tooling, automation, governance, and rollout support without guessing their way into production. That can include cluster design, CI/CD for models, observability, and team enablement, often as part of broader engineering services delivery.
08
What is model serving, and how do you keep LLM inference fast?
Model serving is the runtime layer that exposes trained models for real-time or batch predictions in production. We keep LLM inference fast through batching, quantisation, cache strategy, GPU right-sizing, and tuned serving stacks such as vLLM or Triton, often alongside workflow-aware AI automation that reduces unnecessary calls.
Client Stories
From Vision to Verified Results
Behind every number - 40% faster deliveries, 60% less admin workload, 50% quicker data processing - is a client who trusted us with a real business challenge. These aren't just demos. They're live products, running at scale sustainably, delivering results clients can measure.
"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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