If the cloud were a city, each service provider would feel like a different district. One is built for speed, another for scale, another for innovation, and another for security and privacy.
Today, more than 90 percent of organisations rely on cloud infrastructure to run their operations. The question is no longer whether to use the cloud. The real question is which provider aligns best with your goals.
This guide explores the 11 leading cloud providers in 2025, what they offer, and what makes each one stand out.
Amazon Web Services (AWS)
Amazon Web Services remains the largest cloud provider with an estimated 30 percent market share in Q2 2025.
Key capabilities
Hundreds of services covering compute, storage, AI and machine learning, analytics, and serverless computing
Extensive global network with more than 100 Availability Zones
Strong cost management tools such as Cost Explorer and Savings Plans

Why it matters
AWS represents the highest standard of cloud scalability and reliability. It is often the first platform developers choose when building modern applications.
Microsoft Azure
Microsoft Azure holds about 20 percent of the global cloud market.
Key capabilities
Deep integration with Microsoft 365, Active Directory, and enterprise software
Comprehensive hybrid cloud tools such as Azure Arc and Azure Stack
Strong compliance support and global data sovereignty options

Why it matters
Azure is the preferred platform for enterprises modernising legacy systems within Microsoft environments.

1. Google Cloud Platform (GCP)
   Google Cloud has approximately 13 percent market share and is known for its data-driven innovation.
   Key capabilities
   BigQuery and Looker for industry-leading analytics
   Advanced AI and machine learning tools including Vertex AI and TensorFlow
   Long-standing commitment to sustainability with 100 percent carbon-neutral operations since 2017

Why it matters
GCP powers many of the world’s most data-intensive workloads with advanced analytics and AI capabilities.

1. Alibaba Cloud
   Alibaba Cloud holds around 4 percent global market share and leads the Asia-Pacific cloud market.
   Key capabilities
   Strong presence in e-commerce, logistics, and financial services
   Data centres across more than 25 countries
   Localised compliance and billing for APAC businesses

Why it matters
Alibaba Cloud is a strong choice for companies expanding throughout the Asia-Pacific region.

1. Oracle Cloud Infrastructure (OCI)
   Oracle Cloud has about 3 percent market share and is particularly strong among enterprises that rely on Oracle databases.
   Key capabilities
   High-performance computing for analytics and transactional workloads
   Autonomous Database for automated management and patching
   One of the lowest outbound data transfer costs available

Why it matters
OCI is built for performance, cost efficiency, and enterprise-grade database workloads.

1. IBM Cloud
   IBM Cloud focuses on hybrid cloud and regulated industries.
   Key capabilities
   Watson AI for improved automation and insights
   Integration across mainframe, hybrid, and public cloud environments
   Government-level encryption and compliance controls

Why it matters
IBM Cloud connects traditional enterprise systems with modern cloud agility.

1. Salesforce Cloud
   Salesforce is the global leader in SaaS and CRM solutions.
   Key capabilities
   End-to-end CRM, analytics, and marketing automation tools
   AI-driven personalisation with Einstein GPT
   A large AppExchange ecosystem with more than 7,000 integrations

Why it matters
Salesforce unifies customer data and interactions across the entire business ecosystem.

1. VMware Cloud
   VMware Cloud supports businesses migrating workloads without needing to re-architect them.
   Key capabilities
   Native integration with AWS, Azure, and Google Cloud
   Consistent operations across on-premise and public environments
   Built-in tools for performance monitoring and cost optimisation

Why it matters
VMware Cloud provides one of the easiest paths to hybrid and multi-cloud adoption.

1. DigitalOcean
   DigitalOcean is designed for simplicity and developer friendliness.
   Key capabilities
   Fast provisioning for compute, databases, and Kubernetes
   Predictable flat pricing without hidden fees
   Strong developer community and API-driven workflows

Why it matters
DigitalOcean delivers reliable cloud services with straightforward pricing suited for startups and small businesses.

1. Tencent Cloud
   Tencent Cloud is a major provider in Asia with increasing global influence.
   Key capabilities
   Expertise in gaming, live streaming, and media workloads
   Advanced edge computing and real-time data delivery
   Expanding data centre presence in North America and Europe

Why it matters
Tencent Cloud supports some of the largest gaming and media platforms worldwide.

1. Huawei Cloud
   Huawei Cloud has expanded significantly across Asia, the Middle East, and Africa.
   Key capabilities
   Strong support for AI, IoT, and 5G-integrated infrastructure
   Competitive pricing for compute and data services
   More than 85 Availability Zones across 30 regions

Why it matters
Huawei Cloud increases cloud accessibility in emerging markets through affordability and regional reach.
Comparison summary of the top 11 cloud providers
Below is a clean and concise comparison in point form, replacing the table:
AWS
Strength: Largest service catalog and global reliability
Best for: Enterprises and startups
Unique advantage: Leading scalability and ecosystem depth

Microsoft Azure
Strength: Enterprise and hybrid cloud integration
Best for: Organisations using the Microsoft stack
Unique advantage: Seamless Microsoft environment

Google Cloud
Strength: AI and analytics
Best for: Data-focused businesses
Unique advantage: BigQuery and Vertex AI

IBM Cloud
Strength: Hybrid cloud and compliance
Best for: Regulated industries
Unique advantage: Watson AI and enterprise-grade security

Oracle Cloud
Strength: Database and analytics performance
Best for: Enterprise database workloads
Unique advantage: Autonomous Database technology

Alibaba Cloud
Strength: APAC presence and cost efficiency
Best for: Businesses expanding into Asia
Unique advantage: Regional market dominance

Salesforce Cloud
Strength: CRM and SaaS capabilities
Best for: Sales and marketing teams
Unique advantage: Unified customer experience platform

VMware Cloud
Strength: Virtualisation and hybrid operations
Best for: Enterprises migrating existing workloads
Unique advantage: Smooth on-premise to cloud transition

DigitalOcean
Strength: Simplicity and affordability
Best for: Startups and small businesses
Unique advantage: Developer-friendly experience

Tencent Cloud
Strength: Gaming and media optimisation
Best for: Real-time entertainment workloads
Unique advantage: High-performance delivery

Huawei Cloud
Strength: Global hybrid cloud and affordability
Best for: Emerging markets
Unique advantage: Cost-effective global scaling

Conclusion
Every cloud provider serves a different purpose. Some are built for scale, others for flexibility, performance, or cost efficiency. AWS offers the widest range of services, Google Cloud leads in data intelligence, and DigitalOcean stands out for simplicity. The best cloud platform is the one that aligns with your business model, technical needs, and long-term strategy.
Whether you are building an AI-driven application or scaling a growing SaaS product, understanding these providers will help you make informed decisions that support both performance and growth.

Every full-stack developer has been there: you are planning a new
feature, sketching out database tables, and spending half your time
looking up ORM syntax for many-to-many relationships or writing join
tables by hand.
I wanted a tool where I could visually draw my relational tables,
connect the columns, and immediately get clean, copy-pasteable code for
the three targets I use most: PostgreSQL SQL, Prisma ORM, and
Drizzle ORM.
So, I built SchemaDraw(https://schemadraw.netlify.app).
—

### The Architecture: Designing for ₹0 Scale

As an indie hacker, I wanted to launch a tool that could support

thousands of developers without leaving me with a heavy server bill.

To achieve this, SchemaDraw is built as a **100% client-side Single

Page Application (SPA)** using React and Vite:

1. **Client-side Compilation:** The engines that transform your

visual nodes into SQL or TypeScript are entirely written in JavaScript,
running in the user’s browser.
2. Local Storage Persistence: Your work auto-saves locally so
refreshing the tab won’t lose your schema.
3. LZ-Compressed URL Sharing: Instead of saving diagrams to a
server database, SchemaDraw compresses the entire canvas layout and
schema structure into a URL hash component. You can copy the share link
and send it to a teammate—the app reads the hash and redraws the exact
layout with zero server storage costs.
—
### Key Features Built for Developers

* **Protruding Target Handles:** Most ERD tools place connection dots

inside nodes, leading to click collisions. SchemaDraw places handles -
5px outside the borders, lighting up green on valid targets to make
drawing links effortless.
* Interactive Presentation Lock: Toggle read-only mode to safely
present or inspect diagrams without making accidental changes.
* Grid Auto-Layout: Got a messy canvas? One click aligns all your
tables into a clean, alphabetical column-row grid.
* Cohesive Theme Engine: A single click swaps between a clean
Light Slate and a cohesive Pitch-Black interface.

---

### Check it out!

SchemaDraw is open, free, and requires no accounts or signups.

👉 **Try it here:** 

  
  


I’d love to hear your thoughts! What features, databases, or ORM

compilers would make this a permanent part of your bootstrapping
toolkit? Let me know in the comments below!

Last week, security researcher Joernchen published a clever RCE in Claude Code 2.1.118. I spent Saturday reproducing it from the advisory to understand the pattern. The bug is fixed now, but the parsing anti-pattern behind it is everywhere in AI developer tools.

I’ve written a full article here:
https://vechron.com/2026/05/i-reproduced-a-claude-code-rce-the-bug-pattern-is-everywhere/

RAG has become the default answer for giving LLMs access to private knowledge. And for good reason — it works. But after running it in production we kept hitting the same wall. Not retrieval accuracy. The operational tax.

Re-embedding on data changes. Chunking drift. Retrieval misses on edge cases. Pipeline failures at 2am. The vector database that needs babysitting.

So we ran an experiment.

The Hypothesis
What if instead of chunking, embedding, and retrieving — we just loaded the full document into the LLM context, cached the KV state persistently, and reused it across every query?

No retrieval step. No embedding pipeline. No vector database. Just the model with full document context, warm and ready.

How It Works
The core idea is simple. When an LLM processes a prompt it generates a key-value attention cache — the internal representation of everything it has read. Normally this cache is transient. It lives in VRAM during the request and disappears after.
We persist it.
The initialization prompt — your document — gets processed once. The resulting KV cache gets stored externally and indexed to that document. Every subsequent query retrieves that cached state and appends the user query. The model never recomputes the document. Ever.

The math:
KV_init = LLM.prefill(document)
KV_store[document_id] = KV_init

# On every query:
KV_full = KV_store[document_id] + LLM.prefill(query)
output = LLM.decode(KV_full)

What We Found

Answer quality improved.
No retrieval misses are possible when the full document is in context. The model has read everything. It doesn’t guess which chunks are relevant — it knows the whole document. For complex multi-part questions that span different sections this is a significant improvement over chunked retrieval.

Updates became trivial.

Document changes? Re-run the prefill, store the new KV cache. Minutes not hours. No re-embedding pipeline. No re-indexing. No retrieval regression testing. Just regenerate and deploy.

Operational complexity dropped.

No embedding model to maintain. No vector database to monitor. No chunking strategy to tune. No retrieval quality metrics to track. The surface area for things to break quietly got dramatically smaller.
Latency on warm cache is effectively instant.

When the KV state is already loaded the query just appends and generates. No retrieval hop, no context injection latency.
The Honest Tradeoffs

Context window is the ceiling.

Current limit is around 120k tokens — roughly 200-300 pages. Works well for focused documents. For large corpora you need a routing layer to select the right cache per query. You’ve pushed the retrieval problem up one level — instead of retrieving chunks you’re selecting a cache. Simpler problem but not zero.

Cold cache restore adds latency.

The first query after a cache restore pays a latency cost. For strict SLA requirements this matters. Warm cache is instant. Cold restore depends on your infrastructure.

Initial prefill costs more than embedding.

Running a full forward pass on a large document costs more compute than embedding it. The economics work when query volume is high enough to amortize that cost. Low query, high update frequency — RAG still wins.

Where This Wins

This approach is clearly better when:

You have a focused, structured document — legal contract, compliance policy, product manual, technical spec
Query volume is high relative to update frequency
Full context comprehension matters more than breadth
You want to eliminate pipeline maintenance entirely
Privacy matters — no document chunks sent to embedding APIs

Where RAG Still Wins

Very large document collections where context limits apply
Highly dynamic data that changes multiple times per day
When you genuinely don’t know which document is relevant at query time
Low query volume where prefill cost doesn’t amortize

What We’re Building

We’ve been running this in production at InferX as part of our Sovereign Endpoints™ infrastructure. The persistent KV cache layer sits on top of our GPU snapshotting architecture — which is what makes the cold cache restore fast enough to be practical.
We’re now opening a limited beta for teams who want to test this on real workloads. Particularly interested in legal, compliance, finance, and developer tooling use cases.
If you’re running RAG in production and want to run a head-to-head comparison — we’d love to work with you.

Have you ever pushed code to GitHub and wished your application could automatically build and deploy itself without logging into a server or clicking a button in Jenkins? In this article, you’ll learn how to build a complete CI/CD pipeline for a Dockerized Node.js application using Jenkins, starting with manual deployments and progressing to fully automated deployments using GitHub webhooks.

We will cover:

• Creating a Jenkins pipeline
• Building a Docker image
• Deploying a container
• Triggering builds manually
• Triggering builds automatically
• GitHub Personal Access Tokens
• Fine-grained vs Classic Tokens
• Jenkins credentials
• GitHub webhooks
• Required Jenkins plugins
• Common errors and troubleshooting
  The goal is to understand not only how to configure everything but also why each component is needed.

Node.js Application Repo URL- https://github.com/omkarsharma2821/Node.js-App-Deploy-Github-Action

Architecture Overview

The complete flow looks like this:

Developer
    |
    | Git Push
    v
GitHub Repository
    |
    | Webhook
    v
Jenkins
    |
    | Build Docker Image
    v
Docker
    |
    | Run Container
    v
Application Running

Without webhooks:

Developer
    |
    | Git Push
    v
GitHub Repository

Jenkins Build Now (Manual Trigger)
    |
    v
Build and Deploy

With webhooks:

Developer
    |
    | Git Push
    v
GitHub Repository
    |
    v
Webhook
    |
    v
Jenkins
    |
    v
Build and Deploy Automatically

Prerequisites

Before starting, ensure you have:

• Ubuntu Server
• Jenkins installed
• Docker installed
• Git installed
• GitHub repository
• Node.js application with Dockerfile
  Verify installations:

jenkins --version
docker --version
git --version

Installing Docker on Jenkins Server

Install Docker:

sudo apt update
sudo apt install docker.io -y

Enable Docker:

sudo systemctl enable docker
sudo systemctl start docker

Verify:

docker --version

Allow Jenkins to Use Docker

By default Jenkins cannot execute Docker commands.

Add Jenkins user to Docker group:

sudo usermod -aG docker jenkins

Restart Jenkins:

sudo systemctl restart jenkins

Verify:

sudo su - jenkins
docker ps

If Docker works without sudo, Jenkins is ready.

Creating the Pipeline

Initially we created a Jenkins pipeline that manually clones the repository.

Example:

pipeline {
    agent any

    stages {

        stage('Clone Repository') {
            steps {
                sh '''
                    mkdir -p devops
                    cd devops
                    rm -rf Node.js-App-Deploy-Github-Action
                    git clone -b main https://github.com/username/repository.git
                '''
            }
        }

        stage('Build Image') {
            steps {
                sh '''
                    cd devops/Node.js-App-Deploy-Github-Action
                    docker build -t node-app .
                '''
            }
        }

        stage('Deploy') {
            steps {
                sh '''
                    docker run -d -p 8000:8080 node-app
                '''
            }
        }
    }
}

This works, but every deployment requires manually clicking:

Build Now

Problem with Multiple Deployments

Suppose the application is already running.

Running:

docker run -d -p 8000:8080 node-app

again will fail because port 8000 is already occupied.

Error:

Bind for 0.0.0.0:8000 failed

Better Deployment Approach

Before starting a new container, remove the old one.

docker rm -f node-app-container || true

Then start a new container:

docker run -d --name node-app-container -p 8000:8080 node-app

Understanding docker rm -f node-app-container || true

Let’s break it down.

docker rm

Removes a container.

docker rm node-app-container

Works only if container is stopped.

-f

Force remove.

docker rm -f node-app-container

This:

1. Stops container
2. Removes container

||

OR operator.

Syntax:

command1 || command2

If command1 fails, command2 executes.

true

Always returns success.

true

Exit code:

0

Final Meaning

docker rm -f node-app-container || true

If container exists:

Remove it

If container doesn’t exist:

Ignore error and continue

This prevents Jenkins from failing.

Manual Triggering

The simplest approach is manual execution.

Navigate to:

Jenkins Job
|
└── Build Now

Advantages:

• Easy to understand

• Good for learning
  Disadvantages:

• Requires human intervention

• Not real CI/CD

Automatic Triggering

The goal of CI/CD is:

Code Push
    |
    v
Automatic Build
    |
    v
Automatic Deployment

This is where GitHub webhooks come into play.

Required Jenkins Plugins

Install the following Jenkins plugins before configuring the CI/CD pipeline:

1. Git Plugin

• Enables Jenkins to interact with Git repositories.
• Allows Jenkins to clone repositories, fetch changes, and checkout specific branches.
• Required for integrating Jenkins with GitHub repositories.

2. GitHub Plugin

• Provides integration between Jenkins and GitHub.
• Allows Jenkins to communicate with GitHub repositories and services.
• Supports GitHub-related features within Jenkins.

3. GitHub Integration Plugin

• Enables GitHub webhook support.
• Allows Jenkins to automatically trigger builds when code is pushed to GitHub.
• Essential for implementing automated CI/CD workflows.

4. Pipeline Plugin

• Enables support for Jenkins Pipelines.
• Allows execution of Jenkinsfiles written in Declarative or Scripted Pipeline syntax.
• Required for defining CI/CD workflows as code.

5. Credentials Plugin

• Provides secure storage for sensitive information.
• Allows storing:
  • GitHub Personal Access Tokens (PATs)
  • Usernames and passwords
  • SSH keys
  • API tokens
• Prevents hardcoding secrets in Jenkins jobs and pipelines.

GitHub Authentication

When Jenkins needs to access a GitHub repository, authentication requirements depend on the repository type.

Public Repository

• Can typically be cloned without authentication.

Example:

git clone https://github.com/username/repository.git

Private Repository

• Requires authentication.
• GitHub no longer supports account passwords for Git operations.
• A Personal Access Token (PAT) must be used instead of a password.

Why Use a Personal Access Token (PAT)?

• More secure than passwords.
• Allows granular permission control.
• Can be revoked without affecting your GitHub account password.
• Recommended by GitHub for all Git operations requiring authentication.

Classic Personal Access Token

Older token type.

Advantages:

• Simple

• Easy to configure
  Disadvantages:

• Broad permissions

• Less secure
  Example scopes:

repo
workflow
admin:repo_hook

Fine-Grained Personal Access Token

Newer and recommended approach.

Advantages:

• Repository-level access
• Better security
• Granular permissions
  Example:

Repository Access:
Only selected repositories

Permissions:

Contents: Read and Write
Metadata: Read
Webhooks: Read and Write

Fine-Grained vs Classic Token

For modern projects, prefer Fine-Grained tokens.

Adding GitHub Token to Jenkins

Navigate to:

Manage Jenkins
|
Credentials

Select:

Global Credentials

Choose:

Add Credentials

Kind:

Username with Password

Example:

Username: GitHub Username
Password: Personal Access Token

ID:

github-creds

Save.

Pipeline Script vs Pipeline Script from SCM

Many beginners get confused here.

Pipeline Script

Pipeline stored inside Jenkins UI.

Example:

pipeline {
    agent any
}

Advantages:

• Quick setup
  Disadvantages:

• Not version controlled

• Difficult to maintain

Pipeline Script from SCM

Pipeline stored in GitHub repository.

Repository structure:

project/
|
|-- Dockerfile
|-- package.json
|-- app.js
|-- Jenkinsfile

Jenkins automatically downloads Jenkinsfile.

Advantages:

• Version controlled
• Industry standard
• Easier maintenance
  Recommended approach.

Configuring Pipeline from SCM

Create Jenkins job.

Select:

Pipeline

Under Definition:

Pipeline script from SCM

SCM:

Git

Repository URL:

https://github.com/username/repository.git

Branch:

*/main

Script Path:

Jenkinsfile

Save.

Creating GitHub Webhook

Navigate to:

GitHub Repository
|
Settings
|
Webhooks
|
Add Webhook

Payload URL:

http://JENKINS_PUBLIC_IP:8080/github-webhook/

Content Type:

application/json

Event:

Just the push event

Save webhook.

Configuring Jenkins Trigger

Open job configuration.

Under Build Triggers:

Select:

GitHub hook trigger for GITScm polling

Save.

Testing the Webhook

Push code:

git add .
git commit -m "testing webhook"
git push origin main

Expected flow:

GitHub Push
    |
    v
Webhook
    |
    v
Jenkins
    |
    v
Pipeline Starts

No manual click required.

Common Troubleshooting

Webhook Returns 404

Cause:

Wrong webhook URL

Correct:

http://SERVER-IP:8080/github-webhook/

Webhook Returns 403

Cause:

Authentication or security issue

Verify:

• GitHub plugin
• GitHub integration plugin

Webhook Returns 200 But Build Doesn’t Start

Common cause:

Pipeline Script instead of Pipeline Script from SCM

or

Repository mapping issue

Dockerfile Not Found

Example:

unable to evaluate symlinks in Dockerfile path

Cause:

Wrong working directory.

Check:

pwd
ls -la

Verify Dockerfile location.

Permission Denied While Running Docker

Cause:

Jenkins not in docker group

Fix:

sudo usermod -aG docker jenkins
sudo systemctl restart jenkins

Final Jenkinsfile

pipeline {
    agent any

    stages {

        stage('Build Image') {
            steps {
                sh '''
                    docker build -t node-app .
                '''
            }
        }

        stage('Deploy') {
            steps {
                sh '''
                    docker rm -f node-app-container || true
                    docker run -d --name node-app-container -p 8000:8080 node-app
                '''
            }
        }
    }
}

Conclusion

A Jenkins pipeline can be triggered manually or automatically. Manual triggering is useful for learning and testing, but real CI/CD begins when code pushes automatically trigger builds and deployments.

The recommended production approach is:

1. Store the Jenkinsfile in GitHub.
2. Use Pipeline Script from SCM.
3. Configure GitHub credentials using a Personal Access Token.
4. Enable GitHub webhook integration.
5. Use Docker for packaging and deployment.
6. Remove old containers before deploying new versions.
   With this setup, every code push automatically builds a Docker image, deploys a fresh container, and updates the application without requiring any manual intervention.

✍️ Author: Omkar Sharma

📬 Feel free to connect on LinkedIn or explore more on GitHub