Cloud Technology Insights & Resources | EaseCloud Blog

Master AWS Cost Optimization for Startups

Cost Optimization

Master AWS Cost Optimization for Startups

Master AWS cost optimization for startups with proven strategies for EC2, Lambda, S3, and RDS. Reduce cloud spending by 30-40% while maintaining performance and reliability.

Select the Optimal OCI GPU Shape for LLMs

Select the Optimal OCI GPU Shape for LLMs

Select optimal OCI GPU shapes for LLM deployment. Compare A10, A100, H100 performance benchmarks, costs, and ROI. Data-driven recommendations for 7B to 175B models

Connect LLMs Directly to Oracle Database

Connect LLMs Directly to Oracle Database

Integrate Oracle Autonomous Database with LLM deployments for SQL-based inference, vector search, and RAG patterns. Reduce latency 40-60% with native integration.

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.