Cloud Technology Insights & Resources | EaseCloud Blog

Automating Cloud-Native Deployments with CI/CD

Automating Cloud-Native Deployments with CI/CD

Learn how to automate cloud-native deployments with CI/CD, GitOps, and progressive delivery on Kubernetes—secure, scalable, and production-ready.

OCI vs AWS vs Azure: Real Cost Comparison

OCI vs AWS vs Azure: Real Cost Comparison

Compare Oracle Cloud, AWS, and Azure costs for LLM deployments. Detailed analysis shows OCI 40-70% savings on A100 GPUs plus scenarios where AWS delivers better value.

Deploy Production LLMs on OKE Kubernetes

Deploy Production LLMs on OKE Kubernetes

Deploy LLMs on Oracle Kubernetes Engine with GPU support. Complete guide covers OKE cluster setup, GPU nodes, vLLM deployments, auto-scaling, and monitoring patterns.

Maintain 99.9% Uptime with GCP Monitoring

Maintain 99.9% Uptime with GCP Monitoring

Monitor LLM deployments with Google Cloud Operations for reliability and performance. Track metrics, set up alerts, and debug production issues with unified observability across Vertex AI, GKE, and Cloud Run.

Scale Llama 4 Across Multiple Cloud Regions

Scale Llama 4 Across Multiple Cloud Regions

Deploy Llama 4 across AWS, Azure, and GCP for global reach. Multi-cloud architecture guide covering load balancing, failover, cost optimization, and auto-scaling.

Deploy Mixtral 8x7B on Google Vertex AI

Deploy Mixtral 8x7B on Google Vertex AI

Deploy Mixtral 8x7B on Google Cloud Vertex AI for production inference. Leverage the mixture-of-experts architecture for cost-effective, scalable serving with 32K context windows.

Auto-Scale GPU Workloads on GKE Clusters

Auto-Scale GPU Workloads on GKE Clusters

Configure Google Kubernetes Engine GPU autoscaling for production LLM deployments. Set up dynamic scaling, optimize costs with spot VMs, and maintain performance through intelligent autoscaling policies.

Cut Costs 85% with Open Source GPT Models

Cut Costs 85% with Open Source GPT Models

Deploy open-source GPT models across AWS, GCP, and Azure. Production guide covering GPT-J, GPT-NeoX, MPT-30B deployment, optimization, and cost savings up to 85%.

Run GLM-4 for Chinese Enterprise Applications

Run GLM-4 for Chinese Enterprise Applications

Deploy GLM-4 for enterprise Chinese applications. Production guide covering cloud deployment, fine-tuning, enterprise integration, and cost optimization strategies.

Serve Gemma Serverless on Google Cloud Run

Serve Gemma Serverless on Google Cloud Run

Deploy Google's Gemma model on Cloud Run for serverless, auto-scaling LLM inference with pay-per-request pricing and zero idle costs.

Run LLM Inference Directly from BigQuery

Run LLM Inference Directly from BigQuery

Integrate Vertex AI LLMs with BigQuery for SQL-based inference, enabling petabyte-scale text processing without data movement or complex pipelines.

Maximize LLM Throughput with Google TPU v5p

Maximize LLM Throughput with Google TPU v5p

Optimize LLM deployments with Google Cloud TPUs for superior cost-performance. Configure TPU v5p for production inference with 2-3x better efficiency than equivalent GPU configurations.

Deploy DeepSeek R1 for Math and Coding Tasks

Deploy DeepSeek R1 for Math and Coding Tasks

Deploy DeepSeek R1 reasoning model for math, coding, and problem-solving. Production setup guide covering AWS, GCP, Azure deployment, optimization, and integration.

Qwen vs DeepSeek vs GLM: Model Comparison

Qwen vs DeepSeek vs GLM: Model Comparison

Compare Qwen, DeepSeek, and GLM Chinese language models. Performance benchmarks, deployment costs, use case recommendations, and cloud platform selection guide.

Increase Throughput 2-3x with vLLM Serving

Increase Throughput 2-3x with vLLM Serving

Increase LLM throughput 2–3x with vLLM serving. Learn PagedAttention, continuous batching, AWQ quantization, and production deployment on NVIDIA GPUs.

Optimize GPUs 40% Faster with TensorRT-LLM

Optimize GPUs 40% Faster with TensorRT-LLM

Boost LLM inference 20–40% with TensorRT-LLM. Learn model conversion, INT8/FP8 quantization, and production deployment for NVIDIA GPUs.

Deploy LLMs Locally on Any Laptop with Ollama

Deploy LLMs Locally on Any Laptop with Ollama

Deploy LLMs locally with Ollama in under 60 seconds. Run 100+ models on laptops or GPUs, use OpenAI-compatible APIs, and keep data private.

Run 70B Models on Consumer GPUs with Quantization

Run 70B Models on Consumer GPUs with Quantization

llamacpp enables efficient LLM inference on consumer hardware, letting you run Llama 2 70B on 24GB GPUs via Q4_K_M quantization and hybrid CPU/GPU offloading, achieving 70% model size reduction, fast CPU performance, and portable, production-ready deployments.

Deploy Self-Hosted LLMs on Kubernetes Clusters

Deploy Self-Hosted LLMs on Kubernetes Clusters

Deploy LLMs on on-premise Kubernetes with k3s or kubeadm, leveraging NVIDIA GPU Operator, auto-scaling, and optimized storage to achieve full control, data sovereignty, and cloud-competitive costs while efficiently running models from 7B to 70B parameters.

Choose the Right GPU for Your LLM Deployment

Choose the Right GPU for Your LLM Deployment

This guide shows how choosing the right bare-metal hardware for LLM inference—RTX 4090 to H100—can optimize VRAM, bandwidth, and throughput, delivering up to 60% lower costs than cloud GPUs with payback in just 4–8 months.

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.