Cloud Infrastructure

The essence of cloud is simple: turn compute, storage, and network into utilities you consume on demand.No hardware to buy, no procurement delays, no self-managed ops.

This model created three main service forms:

SaaS (Software as a Service) - no installation, just open the browser. Google Docs, Notion, Figma. Servers, databases, ops are all managed by the provider. Users focus on the business.

MaaS (Model as a Service) - AI models delivered as APIs. Calling the OpenAI API is using MaaS. No GPU purchase, no training, no inference deployment - world-class AI with a few lines of code.

These services share one thing: they are nearly impossible to replicate locally.

• On-demand compute: spin up a server in minutes, release when done. Procurement from months to minutes.
• Pay-as-you-go: pay for what you use, no idle waste. Entry barrier from thousands to pennies.
• Global reach: deploy across dozens of regions. Users served from the nearest datacenter.
• Managed services: databases, message queues, big data analytics - ready out of the box.

Many think cloud is about saving money. That view is too shallow.What makes cloud truly irreplaceable: some things are physically impossible on-premises.

• Physical coverage: deploying in 20 regions means 20 datacenters. Not a budget issue - physically impossible. One deployment, globally available.
• Scale compute: AI training needs hundreds to thousands of GPUs. Building that on-prem takes months and millions. Cloud compresses it to minutes.
• Elastic scaling: traffic spikes 100x in seconds. On-prem cannot handle that. Cloud auto-scales in seconds.
• Compliance: SOC 2, GDPR, HIPAA - earning these takes years. Cloud providers already have hundreds of certifications customers inherit.

SaaS and MaaS are cloud-native by nature. SaaS needs 24/7 global availability; MaaS runs on massive GPU inference clusters. Neither works on-premises.

AI automation tools follow a classic local-to-cloud migration path.

The AutoApply series is an AI Agent built on Playwright and Selenium for automated information filtering and submission. Currently running locally, it requires the machine to stay on, the network to stay stable, and cannot run concurrently. When a script can only process a limited number of tasks per day, its ceiling is the ceiling of local hardware.

AI Harness Engineering works through multi-model collaboration — calling Claude, GPT, DeepSeek and others simultaneously, orchestrating task logic through prompt engineering. This workflow currently runs in a local terminal, still essentially a single-machine tool.

Migrating to the cloud brings structural changes:

• AutoApply packaged as a Docker image, deployed to a cloud server running 24/7, unaffected by local device status
• Multiple instances run in parallel, increasing throughput by an order of magnitude
• AI inference gateway schedules multiple models, enabling auto-routing, fault-tolerant retries, and usage tracking
• From "a script for personal use" to "a callable API service" — a step toward MaaS

SaaS and MaaS business models are inherently cloud-native. To build a subscription-based AI service, the cloud is the only option.

Blockchain projects are decentralized in concept but heavily depend on centralized cloud infrastructure. This is not a contradiction — it is the optimal solution under physical constraints.

Running a full node locally proves this point: a home PC takes days to sync the Ethereum ledger, and sustained operation consumes bandwidth and storage beyond most personal environments. Industry data shows about one-third of all Ethereum nodes are hosted on AWS — not because projects cut corners, but because personal network and hardware simply cannot handle it.

Web3 reliance on cloud goes beyond running nodes:

• NFT Minting: traffic spikes from zero to tens of thousands of concurrent requests at launch. Local servers cannot handle that instantaneous pressure
• DeFi Protocols: require 24/7 high-availability architecture and cross-region disaster recovery
• On-Chain Data Analysis: blockchain data grows by terabytes, analysis must be done in the cloud
• Exchanges: global users need low-latency access, requiring multi-region deployment

Direction: standardize Web3 infrastructure deployment — one-click blockchain node setup, auto-monitoring, and fault recovery using Docker and automation scripts. Meanwhile, build a complete data pipeline from node sync to data cleaning, storage, and visualization.

Looking at cloud from a business perspective makes the value clearer than from the technical side:

• Change Cost Structure: fixed asset investment becomes on-demand operational expense. More flexible capital allocation, less cash flow pressure.
• Compress Delivery Cycles: infrastructure is no longer a delivery bottleneck. From new service launch to global deployment, time goes from months to days.
• Free Up Team Capacity: infrastructure ops is often the highest hidden cost. Outsource it to cloud providers so the team can focus on core business.
• Expand Market Reach: global deployment lets small teams serve users worldwide. Market boundaries are no longer limited by infrastructure.
• SaaS/MaaS Business Model: subscription services and usage-based pricing naturally depend on cloud infrastructure. If the business direction is to provide callable services, cloud is the only viable path.

Technical capability, business judgment, market understanding — three directions that reinforce each other. Making cloud work requires both technical understanding and business language to translate it into decisions.