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Cloud Infrastructure Insights

Expert tutorials, benchmarks, and guides on GPU servers, AI deployment, VPS hosting, and cloud computing.

Marcus Chen - Senior Cloud Infrastructure Engineer
Written by our expert

Marcus Chen

Senior Cloud Infrastructure Engineer & AI Systems Architect

10+ years of experience in GPU computing, AI deployment, and enterprise hosting. Former NVIDIA and AWS engineer. Stanford M.S. in Computer Science. I help businesses deploy AI models and optimize cloud infrastructure.

1041+ Articles
10+ Years Exp.
50+ AI Deployments
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Monitoring and testing cloud scalability under real-world - dashboard showing autoscaling metrics and real user traffic patterns Servers
Marcus Chen
12 min read

This case study explores Monitoring and testing cloud scalability under real-world conditions for an AI-heavy SaaS product. It walks through the challenge, the architecture and load-testing approach, the monitoring stack, and how AWS, Azure, and GCP behaved under stress, with practical lessons for designing scalable, cost-efficient cloud systems.

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Scaling stateful databases and storage on cloud platforms - diagram of scalable database and storage architecture for UAE cloud regions Servers
Marcus Chen
14 min read

Scaling stateful databases and storage on cloud platforms is uniquely challenging in the UAE and Middle East, where PDPL, data residency, and AI growth intersect. This article explains patterns, platform choices, and regional constraints so you can design elastic, compliant architectures that scale without losing performance or control.

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Cost optimization strategies for highly scalable cloud - conceptual diagram of scalable cloud architecture with autoscaling and cost controls Servers
Marcus Chen
11 min read

This pricing guide explains Cost optimization strategies for highly scalable cloud with concrete cost ranges, autoscaling patterns, and architecture choices. You will see how AWS, Azure, and GCP behave at scale, what really drives your bill, and how to design elastic, AI-ready architectures that stay affordable.

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Comparing AWS vs Azure vs GCP scaling limits and quotas - cloud providers scalability comparison diagram Servers
Marcus Chen
15 min read

Comparing AWS vs Azure vs GCP scaling limits and quotas is critical if you run elastic, high-traffic, or AI-heavy workloads. This in-depth comparison breaks down service quotas, autoscaling behavior, soft vs hard limits, and real-world pros and cons so you can choose the right cloud for long-term scalability.

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How to design cloud architectures for elastic scaling - diagram of autoscaling services, databases, and load balancers in a modern cloud setup Servers
Marcus Chen
12 min read

This guide explains how to design cloud architectures for elastic scaling step by step, from stateless services and autoscaling to stateful data, quotas, and real-world load tests. You will learn concrete patterns to keep costs under control while scaling AI and GPU workloads across AWS, Azure, and GCP.

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Cloud autoscaling strategies for AI and GPU workloads - diagram of elastic GPU clusters scaling with traffic and costs breakdown Servers
Marcus Chen
11 min read

Cloud autoscaling strategies for AI and GPU workloads are critical because GPUs are 10–20 times more expensive than CPUs and highly bursty. This guide explains practical autoscaling patterns, real-world pricing ranges on AWS, Azure, and GCP, and how to design elastic, cost-optimized GPU architectures for AI training and inference.

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ARM Server Viability for LLM Workloads - Benchmark chart showing Graviton4 outperforming x86 in tokens per second (112 characters) Servers
Marcus Chen
6 min read

ARM Server Viability for LLM Workloads is gaining traction as data centers prioritize power efficiency. This guide tackles common challenges like software compatibility and delivers actionable solutions with real benchmarks. Learn how to deploy LLMs on ARM for lower TCO without sacrificing performance.

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