How much does GPU infrastructure cost for AI?

Costs vary significantly. A single A100 GPU on AWS costs approximately $3-4/hour on-demand. For most enterprise AI workloads, we design architectures that use spot instances, auto-scaling, and model optimization (quantization, distillation) to reduce GPU costs by 60-80% while maintaining performance.

☁️ Cloud Engineering

Cloud AI Infrastructure

Q: What is MLOps and why is it important?

MLOps (Machine Learning Operations) is the practice of deploying, monitoring, and maintaining ML models in production. It bridges the gap between data science experimentation and reliable production systems. Without MLOps, models degrade over time, become impossible to update, and create security vulnerabilities.

Q: Which cloud provider is best for AI workloads?

All three major providers (AWS, GCP, Azure) offer excellent AI infrastructure. AWS SageMaker is best for end-to-end ML pipelines. Google Vertex AI excels at custom model training with TPUs. Azure ML integrates deeply with enterprise Microsoft ecosystems. We are cloud-agnostic and will recommend the platform that best fits your existing stack and budget.

The foundation your AI systems need. We design, deploy, and manage the cloud infrastructure that powers production LLMs, ML pipelines, and real-time AI applications at enterprise scale.

⚙️ Infrastructure Services

Everything Your AI Needs to Run

🔄

MLOps Pipelines

End-to-end ML pipelines with automated training, evaluation, versioning, and deployment. Includes model registries, experiment tracking, and A/B testing infrastructure.

⎈

Kubernetes for AI

GPU-accelerated Kubernetes clusters optimized for AI workloads. Auto-scaling inference servers, batch processing jobs, and always-on model serving with minimal latency.

🎮

GPU Provisioning

Right-sized GPU allocation using A100, H100, and T4 instances across AWS, GCP, and Azure. Spot instance management, reserved capacity planning, and cost optimization strategies.

📊

Monitoring & Observability

Real-time dashboards for model performance, latency, throughput, and drift detection. Proactive alerts when models degrade, with automated retraining triggers.

🔒

Security & Compliance

VPC isolation, encrypted data flows, IAM policies, and audit logging for HIPAA, SOC2, and GDPR compliance. Zero-trust architectures for sensitive AI deployments.

💰

Cost Optimization

Model quantization, distillation, and caching strategies that reduce inference costs by 60-80%. Automated spot instance bidding and reserved capacity management.

☁️ Cloud Platforms

Cloud-Agnostic AI Deployment

We architect on the platform that best fits your regulatory requirements, existing stack, and budget.

🟠

AWS

SageMaker, Bedrock, EC2 P5 instances, Lambda for serverless inference, S3 for data lakes, and EKS for Kubernetes orchestration.

🔵

Google Cloud

Vertex AI, TPU v5e for custom training, Cloud Run for serverless, GKE for Kubernetes, BigQuery for analytics, and Gemini API integration.

🟣

Microsoft Azure

Azure ML, Azure OpenAI Service, AKS for Kubernetes, Cosmos DB for vector search, and deep integration with Microsoft 365 and Dynamics.

❓ FAQ

Frequently Asked Questions

What is MLOps and why is it important?+

MLOps (Machine Learning Operations) bridges the gap between data science experiments and reliable production systems. Without MLOps, models degrade over time, become impossible to update, and create security vulnerabilities. We implement automated pipelines for training, testing, deploying, and monitoring your AI models.

Which cloud provider is best for AI?+

All three major providers offer excellent AI infrastructure. AWS SageMaker is best for end-to-end ML pipelines. Google Vertex AI excels at custom model training with TPUs. Azure ML integrates deeply with Microsoft ecosystems. We are cloud-agnostic and recommend what fits your stack and budget.

How much does GPU infrastructure cost?+

A single A100 GPU on AWS costs approximately $3-4/hour on-demand. For most enterprise AI workloads, we design architectures using spot instances, auto-scaling, and model optimization (quantization, distillation) to reduce GPU costs by 60-80% while maintaining performance.

Can you migrate our existing ML models to the cloud?+

Yes. We handle full migration of on-premise ML workloads to the cloud, including model re-packaging, data pipeline migration, CI/CD setup, and performance benchmarking to ensure zero regression during the transition.