Skip to main content
Documentation
Technology areas
close
AI and ML
Application development
Application hosting
Compute
Data analytics and pipelines
Databases
Distributed, hybrid, and multicloud
Generative AI
Industry solutions
Networking
Observability and monitoring
Security
Storage
Cross-product tools
close
Access and resources management
Costs and usage management
Google Cloud SDK, languages, frameworks, and tools
Infrastructure as code
Migration
Related sites
close
Google Cloud Home
Free Trial and Free Tier
Architecture Center
Blog
Contact Sales
Google Cloud Developer Center
Google Developer Center
Google Cloud Marketplace
Google Cloud Marketplace Documentation
Google Cloud Skills Boost
Google Cloud Solution Center
Google Cloud Support
Google Cloud Tech Youtube Channel
/
English
Deutsch
Español – América Latina
Français
Indonesia
Italiano
Português – Brasil
中文 – 简体
中文 – 繁體
日本語
한국어
Console
Sign in
Cloud Architecture Center
Contact Us
Start free
Documentation
Technology areas
More
Cross-product tools
More
Related sites
More
Console
Contact Us
Start free
< Architecture Center home
What's new
Fundamentals
Content overview
Well-Architected Framework
Overview
What's new
Operational excellence
Overview
Ensure operational readiness and performance using CloudOps
Manage incidents and problems
Manage and optimize cloud resources
Automate and manage change
Continuously improve and innovate
View on one page
Security, privacy, and compliance
Overview
Implement security by design
Implement zero trust
Implement shift-left security
Implement preemptive cyber defense
Use AI securely and responsibly
Use AI for security
Meet regulatory, compliance, and privacy needs
Shared responsibility and shared fate
View on one page
Reliability
Overview
Define reliability based on user-experience goals
Set realistic targets for reliability
Build high availability through redundancy
Take advantage of horizontal scalability
Detect potential failures by using observability
Design for graceful degradation
Perform testing for recovery from failures
Perform testing for recovery from data loss
Conduct thorough postmortems
View on one page
Cost optimization
Overview
Align spending with business value
Foster a culture of cost awareness
Optimize resource usage
Optimize continuously
View on one page
Performance optimization
Overview
Plan resource allocation
Take advantage of elasticity
Promote modular design
Continuously monitor and improve performance
View on one page
Sustainability
AI and ML perspective
Overview
Operational excellence
Security
Reliability
Cost optimization
Performance optimization
View on one page
FSI perspective
Overview
Operational excellence
Security
Reliability
Cost optimization
Performance optimization
View on one page
View on one page
Deployment archetypes
Overview
Zonal
Regional
Multi-regional
Global
Hybrid
Multicloud
Comparative analysis
What's next
Reference architectures
Single-zone deployment on Compute Engine
Regional deployment on Compute Engine
Multi-regional deployment on Compute Engine
Global deployment on Compute Engine and Spanner
Landing zone design
Landing zones overview
Decide identity onboarding
Decide resource hierarchy
Network design
Decide network design
Implement network design
Decide security
Enterprise foundations blueprint
Overview
Architecture
Authentication and authorization
Organization structure
Networking
Detective controls
Preventative controls
Deployment methodology
Operations best practices
Deploy the blueprint
AI and machine learning
Content overview
Generative AI
Generative AI document summarization
Generative AI RAG with Cloud SQL
Generative AI knowledge base
RAG infrastructure using Vertex AI and Vector Search
RAG infrastructure using Vertex AI and AlloyDB
RAG infrastructure using GKE and Cloud SQL
GraphRAG infrastructure using Vertex AI and Spanner Graph
Use generative AI for utilization management
Model training
Best practices for implementing machine learning on Google Cloud
Cross-silo and cross-device federated learning on Google Cloud
Model development and data labeling with Google Cloud and Labelbox
MLOps
MLOps: Continuous delivery and automation pipelines in machine learning
Deploy and operate generative AI applications
Deploy an enterprise AI and ML model
Confidential computing for data analytics and AI
MLOps using TensorFlow Extended, Vertex AI Pipelines, and Cloud Build
Guidelines for high-quality, predictive ML solutions
AI and ML applications
Build an ML vision analytics solution with Dataflow and Cloud Vision API
Reference architecture
Deploy the architecture
Design storage for AI and ML workloads in Google Cloud
Harness CI/CD pipeline for RAG-capable applications
Implement two-tower retrieval with large-scale candidate generation
Optimize AI and ML workloads with Cloud Storage FUSE
Optimize AI and ML workloads with Managed Lustre
Use Vertex AI Pipelines for propensity modeling on Google Cloud
Third-party product integrations
C3 AI architecture on Google Cloud
Application development
Content overview
Development approaches and styles
Patterns for scalable and resilient apps
Development platform management
Deploy an enterprise developer platform
Overview
Architecture
Developer platform controls
Service architecture
Logging and monitoring
Operations
Costs and attributions
Deployment methodology
Cymbal Bank example
Mapping BeyondProd principles
Deploy the blueprint
Best practices for cost-optimized Kubernetes applications on GKE
Expose service mesh applications through GKE Gateway
Reference architecture
Deploy the architecture
Build globally distributed applications using GKE Gateway and Cloud Service Mesh
Reference architecture
Deploy the architecture
Patterns and practices for identity and access governance on Google Cloud
Resource management with ServiceNow
Select a managed container runtime environment
DevOps and development lifecycle
Architecture decision records overview
Develop and deliver apps with a deployment pipeline
Reference architecture
Deploy the architecture
DevOps Research and Assessment (DORA) capabilities
Application architectures
Apache Guacamole on GKE and Cloud SQL
Reference architecture
Deploy the architecture
Chrome Remote Desktop on Compute Engine
Set up for Linux
Set up for Windows
Connected device architectures on Google Cloud
Overview
Standalone MQTT broker
IoT platform product
Device to Pub/Sub connection to Google Cloud
Best practices for running an IoT backend
Best practices for automatically provisioning and configuring edge and bare metal systems and servers
Ecommerce platform with serverless computing
Manage and scale networking for Windows applications that run on managed Kubernetes
Reference architecture
Deploy the architecture
Dynamic web application with Python and JavaScript
Use a Cloud SDK Client Library
Three-tier web app
Website hosting
Big data and analytics
Content overview
End-to-end architectures
Analytics lakehouse