Cloud Technology Insights & Resources | EaseCloud Blog

Deploy Microsoft Phi-4 on Azure ML Endpoints

Deploy Microsoft Phi-4 on Azure ML Endpoints

This guide shows how deploying Phi-4 14B on Azure ML delivers near-70B model quality at up to 60% lower cost, using serverless or managed endpoints, INT4 quantization, and batching to achieve high throughput with minimal infrastructure overhead.

Automate LLM Deployments with Azure DevOps

Automate LLM Deployments with Azure DevOps

Automate LLM deployments with Azure DevOps MLOps pipelines using CI/CD, blue-green releases, load testing, and approval gates to cut deployment time by 60%, reduce production errors by 75%, and ship reliable models to production with zero downtime.

Reduce Azure ML Costs by 40-70% in Production

Reduce Azure ML Costs by 40-70% in Production

Learn how to cut Azure ML production costs by 40–70% using reserved and spot instances, autoscaling, storage tiering, budgets, and governance policies to keep LLM deployments performant while preventing cloud spend from spiraling out of control.

Scale LLMs Serverlessly on Container Apps

Scale LLMs Serverlessly on Container Apps

Deploy LLMs on Azure Container Apps with serverless scale-to-zero, KEDA autoscaling, and blue-green deployments to cut costs by up to 80%, eliminate cluster management, and pay only for actual usage in event-driven and variable workloads.

Monitor LLM Performance with Azure Insights

Monitor LLM Performance with Azure Insights

Learn how to monitor production LLM deployments on Azure using Azure Monitor, Log Analytics, and Application Insights to detect issues faster, reduce downtime, track GPU and latency metrics, and maintain reliable, high-performance AI services at scale.

Configure AKS GPU Nodes for LLM Workloads

Configure AKS GPU Nodes for LLM Workloads

Deploy LLMs on Azure Kubernetes Service with native GPU support, dynamic autoscaling, and full Kubernetes control, using V100, A100, or H100 nodes to run custom inference frameworks at scale while optimizing costs with spot and reserved instances.

Deploy Qwen 2.5 with SageMaker Auto-Scaling

Deploy Qwen 2.5 with SageMaker Auto-Scaling

Deploy Qwen 2.5 on SageMaker with auto-scaling and cost optimization. This guide shows you step-by-step deployment for production workloads handling thousands of requests daily.

Achieve 99.99% Uptime with Multi-Region AWS

Achieve 99.99% Uptime with Multi-Region AWS

Deploy LLMs across multiple AWS regions for global reach and high availability. This guide shows you proven patterns for multi-region architectures that deliver <100ms latency worldwide.

Deploy Llama 70B on AWS EC2 P5 Instances

Deploy Llama 70B on AWS EC2 P5 Instances

Deploy Llama 3.3 70B on P5 instances for maximum inference performance. This guide shows you how to leverage H100 GPUs for production-grade LLM serving with optimal throughput.

Cut Inference Costs 40% with AWS Inferentia

Cut Inference Costs 40% with AWS Inferentia

Optimize LLM inference costs with AWS Inferentia2, achieving 40–60% savings versus GPUs through purpose-built AI chips, Neuron SDK compilation, and SageMaker deployment while maintaining high throughput and production-ready performance.

Auto-Scale DeepSeek V3 on AWS ECS Clusters

Auto-Scale DeepSeek V3 on AWS ECS Clusters

Deploy DeepSeek V3’s 671B MoE model on Amazon ECS using multi-GPU containers, auto-scaling, and spot instances to achieve GPT-4–level inference with 40–70% lower costs through flexible, production-ready orchestration.

Track Production LLM Metrics with CloudWatch

Track Production LLM Metrics with CloudWatch

Monitor production LLMs on AWS with CloudWatch to track latency, errors, and GPU health, build dashboards, set alerts, analyze logs, and use X-Ray tracing to detect issues early and maintain reliable, SLA-compliant inference at scale.

44% Better Price-Performance with Oracle Cloud LLMs

44% Better Price-Performance with Oracle Cloud LLMs

Oracle Cloud Infrastructure delivers up to 44% better price-performance for LLMs, with H100 GPUs costing 60–70% less than AWS or Azure. Integrated databases and built-in MLOps enable faster, simpler, and more cost-efficient enterprise AI deployments.

Serve Models on GCP with 91% Cost Savings

Serve Models on GCP with 91% Cost Savings

Deploy open-source LLMs on GCP using Vertex AI, TPUs, Cloud Run, and GKE to reduce ops overhead and cut costs up to 91%, with serverless scaling, high-performance ML infrastructure, and production-ready deployment patterns.

Own Your LLM Stack: On-Premise Deployment

Own Your LLM Stack: On-Premise Deployment

Deploy LLMs on-premises with full data sovereignty, GDPR compliance, sub-20ms latency, and ~30% lower costs using high-performance bare metal inference for European enterprises.

Azure ML: Enterprise LLM Platform Built for Scale

Azure ML: Enterprise LLM Platform Built for Scale

Deploy ML models on Azure with enterprise-grade security and Microsoft-native governance, while reducing costs up to 72% through Reserved Instances, Spot VMs, and predictive autoscaling.

Deploy LLMs on AWS 72% Cheaper in Production

Deploy LLMs on AWS 72% Cheaper in Production

Deploy open-source LLMs on AWS with confidence using the industry’s broadest GPU portfolio and managed services like SageMaker. AWS supports models at any scale while cutting costs up to 72% through Reserved Instances, Spot capacity, and Inferentia2 optimization.

How to Keep Your Business Running During Disasters

The 3 AM Call Nobody Wants

Building Smart Systems with AWS AI

Your Competitors Are Already Using AI (And You're Falling Behind)

How to Modernize Your Legacy Systems Using AWS

The Legacy System Time Bomb

How to Pick the Right AWS Partner (and Avoid Disasters)

The Consulting Horror Story You Need to Hear

AWS CEO Says AI Can't Replace Junior Developers (2025)

AWS CEO Matt Garman explains why replacing junior developers with AI is a mistake. Learn the 3 reasons companies should invest in junior talent, not cut it.

Protecting Your Business in the AWS Cloud

Your Business Is One Breach Away from Disaster

How to Ship Software Faster with AWS DevOps

Why Your Deployments Still Take Forever