Google powers YouTube, Gmail, and Search, serving billions of users every day without breaking a sweat. That same infrastructure? You can use it too.
If you're a developer, DevOps engineer, or tech professional looking to understand Google Cloud Platform (GCP), you're in the right place. This guide breaks down cloud computing fundamentals, explains why companies choose GCP over AWS or Azure, and shows you how Google's global infrastructure actually works.
By the end of this post, you'll understand the cloud basics, know when to use IaaS vs PaaS vs SaaS, and grasp how GCP organizes resources from organizations down to individual VMs. Whether you're building your first cloud app or evaluating platforms for your startup, this is your foundation.
Let's start with the basics.
What is Cloud Computing?
Cloud computing means getting computing services over the internet instead of buying and maintaining your own servers. Think of it like electricity: you don't build a power plant, you just plug in and pay for what you use.
These services include everything you need to run applications: servers, storage, databases, networking, and software. Instead of spending $50,000 upfront on physical servers that might sit idle 70% of the time, you rent exactly what you need, when you need it.
Why Companies Move to the Cloud
Three main reasons drive the shift from on-premises data centers to cloud platforms:
Scalability: Your app goes viral overnight? Scale from 10 to 10,000 servers in minutes. Traffic drops? Scale back down. Try doing that with physical hardware.
Cost Efficiency: Pay only for what you actually use. No more buying servers for peak load that sit mostly empty. Netflix doesn't pay for Super Bowl Sunday capacity during regular weekdays.
Flexibility: Your developers in Paris, New York, and Tokyo all access the same infrastructure. No VPN headaches, no "it works on my machine" problems.
Real example: Spotify handles 500 million users with zero owned data centers. They run entirely on GCP, scaling compute resources based on listening patterns (mornings and evenings spike, overnight drops). Imagine the wasted hardware if they built for peak capacity.
Cloud Deployment Models
Not every company goes all-in on public cloud. Here are the three main approaches:
Public Cloud (GCP, AWS, Azure): Shared infrastructure owned by cloud providers. You rent a slice of Google's data centers alongside thousands of other customers. Most cost-effective and scalable option.
Private Cloud: Your own dedicated infrastructure, either on-premises or hosted. Banks and government agencies often use this for regulatory compliance. You get cloud benefits (automation, self-service) but maintain physical control.
Hybrid Cloud: Mix of both. Maybe your customer data stays on-premises for compliance, but your analytics pipelines run in GCP where you can spin up 1,000 machines for batch processing. Common in financial services: BNP Paribas uses this model.
Most startups and modern companies go public cloud. Most enterprises transitioning from legacy systems go hybrid.
Cloud Service Models: IaaS vs PaaS vs SaaS
Cloud services come in three flavors, each giving you different levels of control vs convenience. Think of it like transportation: do you want to drive (IaaS), take a taxi (PaaS), or ride a bus (SaaS)?
IaaS: Infrastructure as a Service
What you get: Raw computing resources (virtual machines, storage, networks). You manage everything from the operating system up.
What you manage: OS, runtime, applications, data, security patches, scaling logic.
GCP examples:
- Compute Engine (virtual machines)
- Cloud Storage (object storage)
- Persistent Disk (block storage)
- Virtual Private Cloud (networking)
When to use IaaS: Lift-and-shift migrations, legacy applications with specific OS requirements, maximum control over the stack.
Real scenario: You're migrating a 10-year-old Java application from your data center. It runs on specific Linux kernel versions and has hard-coded IP addresses. You create a Compute Engine VM matching your old server specs, copy your app over, and it just works. Later, you can modernize, but for now you need compatibility.
Analogy: Renting a house. You get the building and utilities, but you furnish it, maintain it, and fix things when they break.
PaaS: Platform as a Service
What you get: Managed environment where you just deploy code. The platform handles servers, scaling, patching, networking.
What you manage: Just your application code and configuration. Everything else is handled.
GCP examples:
- App Engine (fully managed app hosting)
- Cloud Functions (serverless functions)
- Cloud Run (containerized apps)
- Google Kubernetes Engine (managed Kubernetes)
When to use PaaS: Modern applications, microservices, APIs, anything where you want to focus on code not infrastructure.
Real scenario: You built a REST API in Python. With Cloud Run, you write your code, create a Docker container, and deploy. Cloud Run automatically handles scaling (from zero to thousands of instances), load balancing, HTTPS certificates, and monitoring. You never think about servers.
Analogy: Renting a furnished apartment. You bring your clothes and personal items, everything else is provided and maintained.
SaaS: Software as a Service
What you get: Ready-to-use software accessed through a browser or API.
What you manage: Your data and user permissions. That's it.
GCP examples:
- Google Workspace (Gmail, Docs, Drive, Meet)
- Looker (business intelligence)
- Apigee (API management)
When to use SaaS: Business tools, collaboration, anything where you just need functionality without customization.
Real scenario: Your company needs email and document collaboration. You sign up for Google Workspace, add your users, and everyone has Gmail, calendar, and Docs in 30 minutes. No email servers to patch, no spam filters to configure.
Analogy: Staying in a hotel. Everything is provided, maintained, and cleaned. You just use it.
Quick decision guide:
- Need full control? → IaaS
- Want to focus on code? → PaaS
- Just need it to work? → SaaS
Why Choose Google Cloud Platform?
Three major cloud providers dominate the market: AWS (32% market share), Azure (23%), and GCP (10%). AWS has the most services, Azure owns enterprise Windows integration, so why would you choose GCP?
Four reasons make GCP stand out: pricing, networking, AI/ML capabilities, and ease of use.
Superior Pricing Model
GCP has the most developer-friendly pricing in the industry:
Sustained use discounts: Automatic. Run a VM for a full month and GCP automatically discounts it up to 30%. AWS requires you to commit upfront to reserved instances.
Per-second billing: Most GCP services bill per second, not per hour. Your CI/CD pipeline runs VMs for 8 minutes? You pay for 8 minutes. On AWS, you'd pay for a full hour.
Committed use discounts: Commit to 1 or 3 years and save 50-70%. Similar to AWS reserved instances but with more flexibility (applies across machine types).
Real numbers: Run a 4 CPU, 16 GB RAM VM for a month:
- AWS: ~$140 (reserved) to $240 (on-demand)
- Azure: ~$150 (reserved) to $220 (on-demand)
- GCP: ~$100 (sustained discount applied automatically)
For the same workload, GCP often costs 30-50% less without you lifting a finger.
Best-in-Class Networking
Google owns the most extensive private fiber network on the planet. They literally laid cables across oceans connecting continents.
What this means for you:
- Your data travels on Google's private network, not the public internet
- Lower latency between regions (data moves faster)
- More reliable connections (fewer hops, fewer failure points)
- Better global performance for your apps
When you deploy a global application with users in Paris, New York, and Tokyo, GCP's network ensures consistent low-latency experiences everywhere. Your Paris users hit a European server, but if they need data from your US database, it travels on Google's private cables (not bouncing through random internet routers).
AI/ML Leadership
Google invented TensorFlow, the most popular machine learning framework. Their AI research leads the industry. This shows in their cloud services:
Pre-trained models: Vision API (image recognition), Natural Language API (text analysis), Translation API, Speech-to-Text, all with simple REST APIs. No ML expertise required.
BigQuery ML: Write SQL queries to build machine learning models. Yes, SQL. No Python notebooks, no model training complexity for common use cases.
Vertex AI: Complete ML platform for custom models, with AutoML for automatic model selection and tuning.
Real example: Snap Inc (Snapchat) runs entirely on GCP, leveraging Vision AI for filters and Cloud TPUs for custom ML models. They handle billions of photos daily with GCP's AI infrastructure.
Easy to Learn
If you've used AWS and GCP, you know GCP is simply more intuitive:
Consistent naming: Most services follow patterns (Cloud Storage, Cloud SQL, Cloud Functions). AWS has EC2, S3, RDS, Lambda—no consistent naming scheme.
Better UI: The GCP Console is cleaner and easier to navigate than AWS's overwhelming interface.
Clearer documentation: GCP docs tend to be more practical with better examples.
This matters when you're learning or onboarding new team members. Less time reading docs, more time building.
Who Uses GCP in Production?
Tech giants: Spotify (500M users), Twitter (migrated from AWS), Snapchat (entire infrastructure)
E-commerce: Home Depot, Carrefour
European companies: Doctolib (medical appointments), BlaBlaCar (ridesharing), Deezer (music streaming), L'Oréal, Engie
Finance: BNP Paribas, LCL, Crédit Agricole, BPCE (all using hybrid GCP setups)
If these companies trust GCP with billions in revenue and millions of users, it's production-ready.
GCP Infrastructure: How It Actually Works
Understanding how Google's infrastructure works helps you make better architecture decisions. Let's go from the physical world (cables and data centers) to the logical world (regions and zones).
Google's Global Network
Google owns and operates undersea fiber optic cables connecting continents. We're not talking about renting capacity on public cables. Google physically owns the cables at the bottom of the ocean.
They also have over 200 Points of Presence (POPs) worldwide. These are edge locations where users first connect to Google's network. The closer you are to a POP, the faster your connection.
Why this matters: When you deploy an app on GCP, user traffic enters Google's network at the nearest POP and stays on their private fiber all the way to your application. This is faster and more reliable than bouncing across random internet service providers.
Regions and Zones
GCP divides the world into regions, and each region into zones. Understanding this is critical for high availability.
Regions: A geographic location like us-central1 (Iowa, USA) or europe-west4 (Netherlands). Each region is completely isolated from other regions. If an entire region fails (asteroid strike, catastrophic network failure), other regions keep running.
As of 2026, GCP has 40+ regions worldwide covering every continent except Antarctica.
Zones: Individual data centers within a region. A zone is one or more data center buildings. Zones are named like us-central1-a, us-central1-b, us-central1-c.
Each region has at least 3 zones. Zones within a region are connected by high-speed, low-latency fiber (under 2ms round-trip between zones).
Key concept: Resources like VMs and disks are zone-specific. If you create a VM in us-central1-a, it lives in that specific data center.
High availability pattern: Deploy your app across multiple zones in the same region. If one zone fails (power outage, networking issue), your other zones keep serving traffic.
Disaster recovery pattern: Replicate critical data to a different region. If the entire Paris region goes down, your Frankfurt region takes over.
Real architecture example:
Your web application (high availability setup)
Region: europe-west4 (Netherlands)
├── Zone A: 2 web servers
├── Zone B: 2 web servers
├── Zone C: 1 database primary, 1 backup
If Zone A fails:
- Zones B and C continue serving traffic
- No downtime
If entire europe-west4 fails:
- Failover to europe-west1 (Belgium)
- Some downtime but data is safe
Multi-Regions
Some GCP services offer multi-region deployments that span multiple geographic regions automatically.
Multi-region example: Store data in the "EU" multi-region, and GCP automatically replicates it across multiple European regions (like europe-west1, europe-north1, etc.).
When to use multi-regions:
- Global content delivery (Cloud CDN, Cloud Storage)
- Maximum availability (survive even regional failures)
- Regulatory compliance (EU multi-region keeps data in Europe)
Trade-off: Higher cost, slightly higher latency for write operations (data needs to replicate across regions).
How Network Connections Work
Two scenarios show how GCP's network benefits your applications:
Scenario 1: User accessing Google service (YouTube)
User device → Nearest POP → CDN (cached content) → Delivered instantly
The user connects to the nearest Google POP (maybe 20ms away). The POP has a CDN cache with popular content (YouTube videos, Gmail assets). Content is served from the edge without going to distant data centers. This is why YouTube loads fast anywhere in the world.
Scenario 2: Querying your GCP data (BigQuery)
User device → Nearest POP → Google fiber network → Data center → Query processing → Results
Your user in Tokyo queries your BigQuery dataset in Iowa. They connect to Tokyo POP, enter Google's private network, and stay on it all the way to the Iowa data center. No public internet hops. Processing happens on Google's infrastructure, results return through the same fast path.
The difference between GCP and hosting on a random VPS? That VPS uses the public internet for everything. GCP uses a private, optimized global network.
GCP Resource Hierarchy: Organizing Your Cloud
GCP organizes resources in a hierarchy: Organization → Folders → Projects → Resources. Understanding this structure is essential for managing access, costs, and policies at scale.
The Four Levels
Organization (101monkey.com)
│
├── Folder (Engineering)
│ ├── Project (web-app-prod)
│ │ ├── VM: webserver-1
│ │ ├── VM: webserver-2
│ │ └── Bucket: static-assets-prod
│ │
│ └── Project (web-app-dev)
│ ├── VM: devserver
│ └── Bucket: static-assets-dev
│
└── Folder (Finance)
└── Project (invoice-system)
├── VM: invoice-processor
└── BigQuery Dataset: invoices
Level 1: Organization (Root)
The organization is the root of everything in your GCP account. It's tied to your Google Workspace or Cloud Identity domain (like company.com).
What it does:
- Controls organization-wide policies (all projects inherit these)
- Provides a single audit trail for compliance
- Enables billing across all projects
Example: You set a policy at the Organization level requiring multi-factor authentication. Every user accessing any project must now use MFA. No exceptions.
Level 2: Folders (Optional but Recommended)
Folders are logical grouping containers under the Organization. They help organize projects by department, environment, or team.
Common folder structures:
By department:
- Folder: Engineering
- Folder: Marketing
- Folder: Finance
By environment:
- Folder: Production
- Folder: Staging
- Folder: Development
By team:
- Folder: Backend-Team
- Folder: Frontend-Team
- Folder: Data-Team
Why use folders?: Set policies once on a folder, apply to all projects inside. Example: Your finance folder has strict audit logging. Every project in that folder automatically inherits the audit policy.
Level 3: Projects (Required)
Projects are the basic unit for enabling services, managing billing, and deploying resources. Every GCP resource belongs to exactly one project.
Project characteristics:
- Has a unique project ID (globally unique, immutable)
- Has a project name (you choose, can be changed)
- Linked to a billing account
- Enables APIs (compute, storage, etc.)
- Contains your actual resources
Example project names:
invoice-app-productionanalytics-pipeline-devml-experimentswebsite-staging
Best practice: Separate projects for different environments (dev, staging, prod). This gives you clean separation of resources, permissions, and billing.
Level 4: Resources (Your Actual Services)
Resources are the services you use: virtual machines, storage buckets, databases, BigQuery datasets, load balancers, etc.
Every resource exists within a project and inherits all policies from its parent chain (project → folder → organization).
Example resources:
- Compute Engine VM:
webserver-prod-1 - Cloud Storage bucket:
gs://my-app-assets - BigQuery dataset:
analytics_data - Cloud SQL database:
users-db
Policy Inheritance: How Permissions Flow Down
Policies set at higher levels automatically apply to everything below. You cannot override a stricter parent policy.
Example inheritance chain:
Organization: "All users must use MFA"
└── Folder (Engineering): "Engineering team can view all resources"
└── Project (web-app-prod): "Alice is project owner"
└── VM (webserver-1): "Service account can access this VM"
What Alice can do:
- Full control over the web-app-prod project (owner role)
- View all resources in Engineering folder (inherited from folder)
- Must use MFA to access anything (inherited from organization)
What happens if you try to disable MFA on the project? You can't. The organization policy overrides.
This inheritance model ensures consistent security and compliance across thousands of projects without manually configuring each one.
Why the Hierarchy Matters
Organization: Your startup grows from 5 to 50 projects. Without a hierarchy, you manually set policies on all 50. With folders, you set policies on 3 folders and they cascade automatically.
Cost tracking: View spending by folder. "How much does our engineering department cost?" One view of the engineering folder's billing.
Access management: New developer joins the backend team. Add them to the backend team Google Group, they automatically get access to all backend projects. They leave, remove from group, access revoked everywhere instantly.
Compliance: Finance regulations require all finance data stays in Europe. Set an organization policy restricting finance folder to EU regions. No developer can accidentally deploy finance data to US regions.
The hierarchy is about managing cloud infrastructure at scale without going crazy.
GCP Services: A Quick Tour
GCP offers over 200 services across every computing category. You don't need to know them all (nobody does), but understanding the major categories helps you pick the right tool for each job.
Compute Services
Run your code and applications:
Compute Engine: Virtual machines (VMs). Full control over OS and software. Use for traditional applications, databases, anything needing specific configurations.
App Engine: Fully managed platform. Upload code, App Engine handles scaling, load balancing, health checks automatically. Use for web apps and APIs where you want zero server management.
Cloud Functions: Serverless functions triggered by events. Run code in response to HTTP requests, file uploads, database changes. Use for event-driven automation and lightweight APIs.
Cloud Run: Run containers without managing servers. Scales from zero to thousands automatically. Use for containerized microservices and APIs.
Google Kubernetes Engine (GKE): Managed Kubernetes for container orchestration. Use when you need fine-grained control over containerized applications at scale.
When to use what?
- Legacy app with specific OS requirements → Compute Engine
- Web app, want zero server management → App Engine or Cloud Run
- Small automation tasks → Cloud Functions
- Complex microservices architecture → GKE
Storage Services
Store data in various formats:
Cloud Storage: Object storage (like Amazon S3). Store files, videos, backups, static website assets. Unlimited scale, globally accessible.
Persistent Disk: Block storage attached to VMs (like hard drives). Use for VM root disks and application data.
Filestore: Network file system (NFS). Use when multiple VMs need to access shared files (like a shared home directory).
When to use what?
- Store files/videos/backups → Cloud Storage
- VM needs a disk → Persistent Disk
- Multiple VMs sharing files → Filestore
Database Services
Store structured and unstructured data:
Cloud SQL: Managed MySQL, PostgreSQL, or SQL Server. Use for traditional relational databases without managing database servers.
Cloud Spanner: Globally distributed SQL database with strong consistency. Use when you need SQL with unlimited scale (replaces Cloud SQL at massive scale).
Firestore: NoSQL document database (like MongoDB). Use for mobile apps, real-time sync, flexible schema requirements.
Bigtable: NoSQL wide-column database for massive scale (billions of rows). Use for time-series data, IoT, analytics.
BigQuery: Data warehouse for analytics, not transactions. Use for analyzing terabytes of data with SQL queries.
Networking Services
Connect resources and serve traffic:
Virtual Private Cloud (VPC): Your private network in GCP. Isolate resources, control firewall rules, define subnets.
Cloud Load Balancing: Distribute traffic across multiple VMs or regions. Handles millions of requests per second.
Cloud CDN: Cache content at edge locations globally. Make your website load fast anywhere in the world.
Cloud DNS: Managed domain name system. Host your domains on Google's infrastructure.
AI/ML Services
Add intelligence to applications without ML expertise:
Vertex AI: Complete platform for training and deploying custom ML models.
Vision API: Analyze images (detect objects, faces, text in images).
Natural Language API: Understand text (sentiment analysis, entity extraction).
Translation API: Translate text between 100+ languages.
Speech-to-Text / Text-to-Speech: Convert audio to text and vice versa.
Data Analytics
Process and analyze data:
BigQuery: Data warehouse for analyzing massive datasets with SQL.
Dataflow: Process streaming and batch data (Apache Beam).
Dataproc: Managed Hadoop and Spark for big data processing.
Pub/Sub: Messaging service for event-driven architectures.
Developer Tools
Build, deploy, and monitor applications:
Cloud Build: CI/CD pipelines for building and testing code.
Artifact Registry: Store Docker images and other artifacts.
Cloud Source Repositories: Git repositories hosted on GCP.
Cloud Monitoring: Track performance, set alerts, view logs.
Cloud Logging: Centralized logging for all GCP services.
This is just scratching the surface. Over the next posts in this series, we'll dive deep into the most important services with hands-on examples.
What's Next: Your GCP Learning Path
You now understand cloud computing fundamentals, why GCP stands out among cloud providers, how Google's infrastructure works, and how GCP organizes resources. This foundation sets you up for hands-on work.
In the next post, we'll tackle resource management: Identity and Access Management (IAM), billing and budget control, and the six different methods for creating GCP resources (Console, gcloud, Terraform, APIs, Marketplace, and Cloud Shell). You'll learn how to control who can do what in your GCP projects and how to avoid surprise cloud bills.
To prepare:
- Create a free GCP account if you haven't already (includes $300 credit for 90 days)
- Explore the GCP Console, navigate through the services menu
- Check out the regions available (Console → Compute Engine → Zones)
- Think about how you'd organize projects for a company with dev, staging, and prod environments
Coming up in this series:
- Post 2: IAM, billing, and resource deployment methods
- Post 3: Deep dive into Compute Engine, BigQuery, and Cloud Storage
- Post 4: Advanced BigQuery for data analytics
- Post 5: Data processing with Dataflow and Dataproc
- And 7 more posts covering containers, Kubernetes, Terraform, architecture patterns, and GCP certifications
Ready to start managing your GCP resources like a pro? See you in Post 2 where we get hands-on with IAM, billing, and deploying your first resources.