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(New guide) Multi-tenant agentic AI system: A reference architecture to help you design a robust multi-tenant agentic AI system in Google Cloud.
(New guide) Implement agentic analytics workflows for distributed data: A high-level architecture for implementing cross-cloud analytics workflows that use AI agents.
(New guide) Networking for AI inference model serving on GKE: A high-level architecture to create a multiple-model inference service using Google Kubernetes Engine (GKE) and a GKE Inference Gateway.
(New guide) Networking for AI inference model serving on all backends: A high-level architecture to create a unified frontend for multiple AI models that are hosted on-premises or by any provider, including third-party and Google Cloud.
(New guide) Build trusted AI agents with Google Maps Platform: A high-level architecture to build trustworthy and effective AI agents by grounding them in real-world maps and calendar data.
Design storage for AI and ML workloads in Google Cloud: Added recommendations for using Rapid Bucket with Cloud Storage. Updated Google Cloud Managed Lustre performance tier and storage capacity.
Design an optimal storage strategy for your cloud workload: Updated Google Cloud Managed Lustre performance tier and storage capacity.
(New guide) Build a multicloud open data lakehouse: A high-level architecture to build a multicloud open data lakehouse that establishes a highly governed, secure pipeline from raw multicloud silos to AI and agentic driven actions.
Choose your agentic AI architecture components: Added information about Google Cloud MCP servers and how to mitigate tool bloat in your agentic AI workloads.
(New guide) Multi-agent private networking patterns: Guidance to help you design private networking infrastructure that supports a publicly accessible, multi-agent, Gemini Enterprise app with private connections between agents, subagents, and tools.
(New guide) Secure data environments in Google Cloud: A high-level architecture to secure sensitive datasets against accidental exposure and malicious exfiltration.
(New guide) Orchestrate security operations workflows: A high-level architecture to build a multi-agent AI system that orchestrates complex investigation and triage processes in a security operations center (SOC) .
(New guide) Multimodal GraphRAG resource orchestration: A high-level architecture to build and deploy a multi-agent AI system that consolidates fragmented multimodal data into a searchable knowledge graph.
(New guide) Guide technical workflows with bidirectional multimodal streaming: A high-level architecture to build and deploy a multi-agent AI system that provides technical guidance and automated safety monitoring through a continuous, bidirectional stream of multimodal data.
Migrate on-premises VMs to Google Cloud: Links to resources to help you migrate on-premises VMs from VMware vSphere, Nutanix AHV, and Microsoft Hyper-V to Google Cloud.
(New guide) Classify multimodal data: A high-level architecture to design a multi-agent AI system that analyzes disparate multimodal data and produces a high-confidence classification.
Google Cloud Well-Architected Framework AI and ML perspective: Performance optimization: Major update and expansion of the recommendations in the performance optimization pillar.
Oracle E-Business Suite with Oracle Database on Compute Engine: Added information about the Terraform configuration sample to deploy a topology for demonstration purposes.
RAG infrastructure for generative AI using Vertex AI and AlloyDB for PostgreSQL:
(New guide) Google Cloud Well-Architected Framework: Sustainability pillar: Expanded the sustainability guidance in the Well-Architected Framework as a full-fledged pillar of the framework.
(New guide) Generate solutions for customer support questions: A high-level architecture for using AI to generate responses to support requests from customers.
(New guide) Generate personalized product recommendations: A high-level architecture for using AI to generate personalized product recommendations for a retail application.
(New guide) Generate podcasts from audio files: A high-level architecture for an application that uses AI to generate podcasts based on audio input.
(New guide) Private connectivity for RAG-capable generative AI applications: Provides a reference architecture that you can use to help secure the network infrastructure for applications with retrieval-augmented generation (RAG).
(New guide) Generate content for personalized marketing campaigns: A high-level generative AI architecture to produce content for personalized marketing campaigns.
(New guide) Single-agent AI system using ADK and Cloud Run: Shows you how to build a single-agent AI system by using ADK and Cloud Run with Gemini and MCP.
(New guide) Administer interactive learning: Design a single-agent AI system that assesses a user's knowledge on a specific topic and generates a personalized learning experience.
(New guide) Automate data science workflows: Design a multi-agent AI system that automates complex data analytics and machine learning tasks.
(New guide) Orchestrate access to disparate enterprise systems: Use agentic AI to orchestrate access to disparate enterprise systems.
AI and ML perspective: Security: Major update to expand the security principles and recommendations in the AI and ML perspective.
(New guide) Choose your agentic AI architecture components: Learn how to select architecture components to build your agentic AI system.
(New guide) Choose a design pattern for your agentic AI system: Learn how to select an agent design pattern to build your agentic AI system.
Design an optimal storage strategy for your cloud workload: Revised the scaling capacity and performance tiers for Managed Lustre.
Design storage for AI and ML workloads in Google Cloud: Updated storage recommendations for training and serving workflows. Revised the scaling capacity and performance tiers for Managed Lustre.
(New guide) RAG infrastructure for generative AI applications in Google Cloud: Provides a list of reference architectures to deploy a generative AI applications with retrieval-augmented generation (RAG) in Google Cloud.
(New guide) VPC Network Peering Cross-Cloud Network with NVAs and regional affinity: Describes how to deploy network virtual appliances (NVAs) in a single-region Cross-Cloud Network architecture.
(New guide) Multi-agent AI system in Google Cloud: A reference architecture to help you design robust multi-agent AI systems in Google Cloud.
(New guide) RAG infrastructure for generative AI using Google Agentspace and Vertex AI: Design infrastructure for a generative AI application with retrieval-augmented generation (RAG) using Google Agentspace and Vertex AI.
Optimize AI and ML workloads with Google Cloud Managed Lustre: Revised the storage capacity, scaling capacity, and performance tiers for Managed Lustre. Added link to the Managed Lustre module in the Cluster Toolkit for easy deployment.
Optimize AI and ML workloads with Cloud Storage FUSE: Updated Anywhere Cache features for multi-regional deployments.
(New guide) Oracle PeopleSoft on Compute Engine with Oracle Exadata: Shows how to build the infrastructure to run Oracle PeopleSoft applications with OCI Exadata databases in Google Cloud.
(New guide) Best practices for continuous access to Google Cloud: Describes best practices for using emergency access and IdP failover to ensure continuous access to Google Cloud.
AI and ML perspective: Reliability: Major update to expand the reliability principles and recommendations in the AI and ML perspective.
(New guide) Well-Architected Framework: Financial services industry (FSI) perspective: Principles and recommendations that are specific to FSI, aligned to each pillar of the Architecture Framework.
(New guide) Secure apps and resources by using context-aware access: Describes how you can use context-aware access to secure different types of apps and resources.
(New guide) Best practices for securing apps and resources by using context-aware access: Describes best practices for using context-aware access to secure apps and resources.
(New guide) GraphRAG infrastructure for generative AI using Vertex AI and Spanner Graph: Shows how to design infrastructure for GraphRAG-capable generative AI applications in Google Cloud by using Vertex AI and Spanner Graph.
(New guide) Optimize AI and ML workloads with Google Cloud Managed Lustre: Shows how to use Managed Lustre to optimize the performance of AI and ML workloads.
Patterns for connecting other cloud service providers with Google Cloud: Added Network Connectivity Center and Hybrid NAT where relevant. Updated VPN references to only refer to HA VPN.
AI and ML perspective: Cost optimization: Major update to expand the cost optimization recommendations in the AI and ML perspective.
File storage on Compute Engine:
Parallel file systems for HPC workloads: Added information about Google Cloud Managed Lustre and DDN Infinia.
Design an optimal storage strategy for your cloud workload: Added information about Filestore replication, Hyperdisk Balanced High Availability, Anywhere Cache, and capacity specifications for Google Cloud NetApp Volumes.
Hub-and-spoke network architecture: Added Network Connectivity Center as a design option.
Multi-regional deployment on Compute Engine: Technical updates to align design recommendations with Google Cloud Well-Architected Framework core principles.
Single-zone deployment on Compute Engine: Technical updates to align design recommendations with Google Cloud Well-Architected Framework core principles.
AI and ML perspective: Operational excellence: Major update to expand the operational excellence recommendations in the AI and ML perspective.
Parallel file systems for HPC workloads: Added guidance about Google Cloud Managed Lustre.
(New guide) Oracle E‑Business Suite with Oracle Database on Compute Engine VMs: Shows how to build the infrastructure to run Oracle E‑Business Suite applications with Oracle Database on Compute Engine VMs in Google Cloud.
(New guide) Harness CI/CD pipeline for RAG applications: Shows how to implement a continuous integration (CI) and continuous deployment (CD) pipeline for a retrieval-augmented generation (RAG) application in Google Cloud. The architecture uses CI/CD products from Harness to deploy containers to Cloud Run services.
Design an optimal storage strategy for your cloud workload: Added guidance about Google Cloud Managed Lustre.
(New guide) Optimize AI and ML workloads with Cloud Storage FUSE: Learn how to optimize performance for AI and ML workloads on Google Kubernetes Engine (GKE) by using Cloud Storage FUSE.
Design storage for AI and ML workloads in Google Cloud: Updated to include Cloud Storage FUSE, Anywhere Cache, Hyperdisk ML, and Google Cloud Managed Lustre.
(New guide) Oracle E-Business Suite with Oracle Exadata in Google Cloud: Shows how to build the infrastructure to run Oracle E-Business Suite applications with Oracle Cloud Infrastructure Exadata in Google Cloud.
Infrastructure for a RAG-capable generative AI application using Vertex AI and Vector Search: Added information about the Terraform configuration sample to deploy the architecture.
Infrastructure for a RAG-capable generative AI application using Vertex AI and Vector Search: Updated the data processing component in the reference architecture to use a Cloud Run function in place of a Cloud Run job.
Google Cloud Architecture Framework: Security, privacy, and compliance: Major update to align the recommendations with core principles of security.
Cross-Cloud Network for distributed applications: Updates to the document set to reflect feature releases over the past months.
Best practices and reference architectures for VPC design: Updates to the document to reflect feature releases over the past months.
(New guide) Cross-Cloud Network inter-VPC connectivity using Network Connectivity Center: Describes how to design the network segmentation structure and connectivity of Cross-Cloud Network with Network Connectivity Center.
(New guide) Optimize AI and ML workloads with Parallelstore: Learn how to optimize performance for artificial intelligence (AI) or machine learning (ML) workloads with parallel file system storage by using Parallelstore.
Cross-Cloud Network for distributed applications: Updates to the document set to reflect feature releases over the past months.
(New guide) Implement two-tower retrieval for large-scale candidate generation: Describes how to implement an end-to-end two-tower candidate generation workflow with Vertex AI.
Google Cloud Architecture Framework: Reliability pillar: Major update to align the recommendations with core principles of reliability.
(New guide) Confidential computing for data analytics and AI: Provides an overview of confidential computing, explores use cases for data analytics and federated learning across various industries, and includes architecture examples for some use cases.
(New guide) Stream logs from Google Cloud to Datadog: Provides an architecture to send log event data from across your Google Cloud ecosystem to Datadog Log Management. The architecture is accompanied by a deployment guide.
(New guide) Infrastructure for a RAG-capable generative AI application using Vertex AI and Vector Search: Describes how to design infrastructure for a generative AI application with retrieval-augmented generation (RAG) by using Vector Search.
Google Cloud Architecture Framework: Performance optimization: Major update to align the recommendations with core principles of performance optimization.
(New guide) Cross-Cloud Network inter-VPC connectivity using VPC Network Peering: Describes how to configure hub-and-spoke Cross-Cloud Network using VPC Network Peering.
(New guide) Deploy and operate generative AI applications: Describes how you can adapt DevOps and MLOps processes to develop, deploy, and operate generative AI applications on existing foundation models.
(New guide) Migrate from AWS Lambda to Cloud Run: Describes how to design, implement, and validate a plan to migrate from AWS Lambda to Cloud Run.
Google Cloud Architecture Framework: Operational excellence: Major update to align the recommendations with core principles of operational excellence.
Design an optimal storage strategy for your cloud workload: Added information about Parallelstore. Updated NetApp Volumes availability capabilities and capacity limits.
(New series) Architecture Framework: AI and ML perspective: Describes principles and recommendations that are specific to AI and ML, for each pillar of the Architecture Framework: operational excellence, security, reliability, cost optimization, and performance optimization.
(New guide) Enterprise application on Compute Engine VMs with Oracle Exadata in Google Cloud: Provides a reference architecture for an application that's hosted on Compute Engine VMs with connectivity to Oracle Cloud Infrastructure (OCI) Exadata databases in Google Cloud.
(New guide) Business continuity with CI/CD on Google Cloud: Learn how to plan and implement business continuity and disaster recovery (DR) for the CI/CD process.
Google Cloud Architecture Framework: Cost optimization: Major update to align the recommendations with core principles of cost optimization.
(New guide) Migrate from Amazon RDS and Amazon Aurora for PostgreSQL to Cloud SQL and AlloyDB for PostgreSQL: Describes how to design, implement, and validate a plan to migrate from Amazon Relational Database Service (RDS) or Amazon Aurora for PostgreSQL to Cloud SQL.
(New guide) Scalable BigQuery backup automation: Build a solution to automate recurrent BigQuery backup operations at scale, with two backup methods: BigQuery snapshots and exports to Cloud Storage. This architecture is accompanied by a deployment guide.
Design an optimal storage strategy for your cloud workload: Updated guidance about storage recommendations and storage options decision tree with information about Hyperdisk ML and Hyperdisk Balanced. Updated file storage guidance based on performance scalability and supported file system protocols.
(New guide) Enterprise application with Oracle Database on Compute Engine: Provides a reference architecture to host an application that uses an Oracle database, deployed on Compute Engine VMs.
(New guide) Select a managed container runtime environment: Learn about managed runtime environments and assess your requirements to choose between Cloud Run and GKE Autopilot.
(New guide) Use generative AI for utilization management: A reference architecture for health insurance companies to automate prior authorization (PA) request processing and improve their utilization review (UR) processes.
Architecting disaster recovery for cloud infrastructure outages: Added DR guidance for Organization Policy Service.
(New guide) Migrate from Amazon RDS and Amazon Aurora for MySQL to Cloud SQL for MySQL: Describes how to design, implement, and validate a plan to migrate from Amazon RDS or Amazon Aurora to Cloud SQL for MySQL.
(New guide) Manage and scale networking for Windows applications that run on managed Kubernetes: Discusses how to manage networking for Windows applications that run on Google Kubernetes Engine using Cloud Service Mesh and Envoy gateways. This reference architecture is accompanied by a deployment guide.
Disaster recovery scenarios for data: Added guidance about using the following capabilities to back up and recover self-managed databases deployed in Google Cloud:
Disaster recovery scenarios for applications: Added guidance about using the following capabilities to back up and recover applications deployed in Google Cloud:
File storage on Compute Engine: Added guidance about Filestore Regional.
(New guide) Architect your workloads: Design resilient, single-region environments on Google Cloud.
Architecting disaster recovery for cloud infrastructure outages: Updated the DR guidance for Google Security Operations SIEM.
(New guide) From edge to multi-cluster mesh: Deploy globally distributed applications through GKE Gateway and Cloud Service Mesh: Provides the steps needed to deploy applications externally through Google Kubernetes Engine (GKE) Gateways running on multiple GKE clusters within a service mesh.
(New guide) From edge to multi-cluster mesh: Globally distributed applications exposed through GKE Gateway and Cloud Service Mesh: Describes exposing applications externally through Google Kubernetes Engine (GKE) Gateways running on multiple GKE clusters within a service mesh.
(New guide) Migrate from AWS to Google Cloud: Migrate from Amazon RDS for SQL Server to Cloud SQL for SQL Server: Describes how to design, implement, and validate a plan to migrate from Amazon Relational Database Service (RDS) to Cloud SQL for SQL Server.
Infrastructure for a RAG-capable generative AI application using Vertex AI: Added a design alternative that uses Vertex AI Vector Search for the vector store and semantic search components in the architecture.
(New guide 2 of 4) Network segmentation and connectivity for distributed applications in Cross-Cloud Network: Describes how to design the network segmentation structure and connectivity of Cross-Cloud Network for distributed applications.
(New guide: 1 of 4) Cross-Cloud Network for distributed applications: Provides an overview about how you can design Cross-Cloud Network for distributed applications.
(New guide 3 of 4) Service networking for distributed applications in Cross-Cloud Network: Describes how to design Cross-Cloud Network service networking for distributed applications.
(New guide 4 of 4) Network security for distributed applications in Cross-Cloud Network: Describes how to design Cross-Cloud Network security for distributed applications.
Design an optimal storage strategy for your cloud workload: Added information about the Regional service tier of Filestore.
(New guide) Build an ML vision analytics solution with Dataflow and Cloud Vision API: Deploy a Dataflow pipeline to process large-scale image files with Cloud Vision. Dataflow stores the results in BigQuery so that you can use them to train BigQuery ML pre-built models. This architecture is accompanied by a reference architecture and a deployment guide.
Infrastructure for a RAG-capable generative AI application using Vertex AI: Added information about getting started with deploying the reference architecture by using a Jump Start Solution.
(New guide) Global deployment with Compute Engine and Spanner: Learn how to architect a multi-tier application that runs on Compute Engine VMs and Spanner in a global topology on Google Cloud.
(New guide) C3 AI architecture on Google Cloud: Develop applications using C3 AI and Google Cloud.
Architecting disaster recovery for cloud infrastructure outages: Added DR guidance for Personalized Service Health.
Disaster recovery building blocks: Added DNS policies to the DR building blocks.
Disaster recovery building blocks: Added information about the soft-deletion feature in Cloud Storage.
Architecting disaster recovery for cloud infrastructure outages: Added DR guidance for Vertex AI online predictions.
Deploying the enterprise application blueprint: Added information about using a single Git repository (a monorepo) instead of a separate repository for each application.
Architecting disaster recovery for cloud infrastructure outages: Added DR guidance for Vertex AI batch predictions.
Deploy an enterprise developer platform on Google Cloud: Consolidated the eab-fleet-(env) project into the eab-gke-(env) project in each environment.
(New guide) Use Google Cloud Armor, load balancing, and Cloud CDN to deploy programmable global front ends: Provides an architecture that uses a global front end incorporating Google Cloud best practices to help scale, secure, and accelerate the delivery of internet-facing applications.
Architecting disaster recovery for cloud infrastructure outages: Added DR guidance for Cloud Billing.
(New guide) Infrastructure for a RAG-capable generative AI application using GKE: Design the infrastructure to run a generative AI application with retrieval-augmented generation (RAG) using GKE, Cloud SQL, and open source tools like Ray, Hugging Face, and LangChain.
Architecting disaster recovery for cloud infrastructure outages: Added DR guidance for Vertex ML Metadata.
Architecting disaster recovery for cloud infrastructure outages: Added DR guidance for Vertex AI Pipelines.
(New guide) Jump Start Solution: Generative AI RAG with Cloud SQL: Deploy a retrieval augmented generation (RAG) application with vector embeddings and Cloud SQL.
(New guide) Model development and data labeling with Google Cloud and Labelbox: Provides guidance for building a standardized pipeline to help accelerate the development of ML models.
(New guide) Build and deploy generative AI and machine learning models in an enterprise: Describes the generative AI and machine learning blueprint, which deploys a pipeline for creating AI models.
AI and machine learning resources: Added introduction information with guiding links to our generative AI and traditional AI resources.
(New guide) Jump Start Solution: Generative AI Knowledge Base: Demonstrates how to build an extractive question-answering (EQA) pipeline to produce content for an internal knowledge base.
(New guide) Cross-silo and cross-device federated learning on Google Cloud: Provides guidance to help you create a federated learning platform that supports either a cross-silo or cross-device architecture.
(New guide) Design storage for AI and ML workloads in Google Cloud: Select the recommended storage options for your AI and ML workloads.
Design an optimal storage strategy for your cloud workload: Added guidance about data transfer options.
Architecting disaster recovery for cloud infrastructure outages: Added information about zonal and regional resilience of Speech-to-Text, Looker, and Cloud Intrusion Detection System.
(New guide) Configure networks for FedRAMP and DoD in Google Cloud: Provides configuration guidance to help you comply with design requirements for FedRAMP High and DoD IL2, IL4, and IL5 when you deploy Google Cloud networking policies.
(New guide) Infrastructure for a RAG-capable generative AI application using Vertex AI: Design infrastructure to run a generative AI application with retrieval-augmented generation (RAG) to help improve the factual accuracy and contextual relevance of LLM-generated content.
Architecting disaster recovery for cloud infrastructure outages: Added information about zonal and regional resilience of Sole Tenant Nodes.
From edge to mesh: Deploy service mesh applications through GKE Gateway: Switched from Ingress API to the more modern Gateway API. Updated relevant sections to reflect this change.
(New guide) Single-zone deployment on Compute Engine: Provides a reference architecture for a multi-tier application that runs on Compute Engine VMs in a single Google Cloud zone and describes the design factors to consider when you build a single-zone architecture.
(New guide) Regional deployment on Compute Engine: Architect a multi-tier application that runs on Compute Engine VMs in multiple zones within a Google Cloud region.
(New guide) Use RIOT Live Migration to migrate to Redis Enterprise Cloud: Migrate from Redis compatible sources like Redis Open Source (Redis OSS), AWS ElastiCache, and Azure Cache for Redis to a fully managed Redis Enterprise Cloud instance in Google Cloud using the Redis Input and Output Tool (RIOT) Live Migration service. This architecture is accompanied by a deployment guide and an assessment guide.
Disaster recovery building blocks: Updated the guidance for Google Kubernetes Engine (GKE) with information about the Backup for GKE and multi-cluster Gateway features.
Architecting disaster recovery for cloud infrastructure outages: Added information about zonal and regional resilience of Connectivity Tests and Network Analyzer.
(New guide) Import logs from Cloud Storage to Cloud Logging: Import logs that were previously exported to Cloud Storage back to Cloud Logging. This architecture is accompanied by a deployment guide.
Architecture fundamentals: This page provides a consolidated view of the Architecture Center resources that provide fundamental architectural guidance applicable to all the technology categories.
Manage just-in-time privileged access to projects: Updated the deployment instructions for JIT Access 1.6.
(New guide) Okta user provisioning and single sign-on: Set up federated user provisioning and single sign-on using Okta.
(New guide) Multi-regional deployment on Compute Engine: Reference architecture for a multi-region, multi-tier topology on Compute Engine VMs and a third-party database.
File storage on Compute Engine: Changed Filestore High Scale to Zonal, updated Filestore Zonal support for the CSI Driver, added Google Cloud NetApp Volumes, and removed NetApp Cloud Volume Service.
Enterprise foundations blueprint: Major rewrite of the guide and updates to the deployable Terraform code:
(New guide) Deploy an enterprise developer platform on Google Cloud: Provides a blueprint to help enterprises set up a developer platform for building and managing container-based applications in Google Cloud.
(New guide) Jump Start Solution: Stateful app with zero downtime deployment on GKE: Update a live app without a noticeable disruption by using the Stateful app with zero downtime deployment on GKE app.
(New guide) Jump Start Solution: Stateful app with zero downtime deployment on Compute Engine: Update a live app without a noticeable disruption by using the Stateful app with zero downtime deployment on Compute Engine app.
(New Guide: 2 of 3) Hybrid and multicloud architecture patterns: Discusses common hybrid and multicloud architecture patterns, and describes the scenarios that these patterns are best suited for.
Adds new content and revises existing content.
(New Guide: 1 of 3) Build hybrid and multicloud architectures using Google Cloud: Provides practical guidance on planning and architecting your hybrid and multi-cloud environments using Google Cloud.
Adds new content and revises existing content.
(New Guide: 3 of 3) Hybrid and multicloud secure networking architecture patterns: Discusses several common secure network architecture patterns that you can use for hybrid and multicloud architectures.
Adds new content and revises existing content.
(New guide) Data transformation between MongoDB Atlas and Google Cloud: Data transformation between MongoDB Atlas as the operational data store and BigQuery as the analytics data warehouse.
Design an optimal storage strategy for your cloud workload: Updated the capacity numbers for Hyperdisk and Local SSD.
Limiting scope of compliance for PCI environments in Google Cloud: Updated to reflect PCI DSS 4.0.
Architecting disaster recovery for cloud infrastructure outages: Added information about zonal and regional resilience of Certificate Authority Service.
Best practices for running tightly coupled HPC applications: Removed the Libfabric script, because it is no longer needed from Intel MPI 2021.10 onwards.
(New series) Migrate across Google Cloud regions: Start preparing your workloads and data for migration across Google Cloud regions.
PCI Data Security Standard compliance: Updated to reflect the release of PCI DSS 4.0.
(New guide) Set up an embedded finance solution using Google Cloud and Cloudentity: Describes architectural options for providing your customers with a seamless and secure embedded finance solution.
(New guide) Migrate to Google Cloud: Minimize costs: Minimize costs of your single- and multi-region Google Cloud environments, and of migrations across Google Cloud regions.
Google Cloud Architecture Framework: Reorganized the Reliability category and moved SLO content to new pages.
Deploy Apache Guacamole on GKE and Cloud SQL: Updated deployment to use Artifact Registry, and updated Cloud Shell commands for compatibility with latest Terraform provider.
(New guide) FortiGate architecture in Google Cloud: Deploy a FortiGate Next Generation Firewall in Google Cloud, using Compute Engine and Virtual Private Cloud networking.
Jump Start Solution: Analytics lakehouse: Updated the Deploy the solution section to clarify that the organizational
policy constraint constraints/compute.requireOsLogin must not be enforced.
Parallel file systems for HPC workloads: Added Sycomp Storage Fueled by IBM Spectrum Scale as an option for parallel file system (PFS) storage, and replaced NetApp Cloud Volumes Service with Google Cloud NetApp Volumes.
Parallel file systems for HPC workloads: Added Parallelstore and Weka Data Platform as options for parallel file system (PFS) storage.
Designing networks for migrating enterprise workloads: Adds Cross-Cloud Interconnect functionality and updates Private Service Connect information.
(New guide) Google Cloud Architecture Framework: Added the deployment archetypes page in the System Design category.
Scalable TensorFlow inference system: Converted the Tensorflow inference system guide into a reference architecture that includes design considerations.
(New guide) Google Cloud deployment archetypes: Overview and comparative analysis of the zonal, regional, multi-regional, global, hybrid, and multicloud deployment archetypes.
PCI DSS compliance on GKE: Updated to meet the requirements of PCI DSS version 4.0, use Cloud IDS instead of a third-party IDS, and use the PodSecurity admission controller instead of PodSecurityPolicy.
Inter-service communication in a microservices setup: Updated the architecture, design guidance, and deployment steps based on the latest demo application.
Architecting disaster recovery for cloud infrastructure outages: Added DR guidance for Access Transparency.
Architectures for high availability of PostgreSQL clusters on Compute Engine: Added information about the write-ahead log and the Log Sequence Number.
Best practices for running tightly coupled HPC applications: Updated to include guidance for H3 compute-optimized VMs.
(New guide) Migrate from AWS to Google Cloud: Migrate from Amazon EKS to GKE: Design, implement, and validate a plan to migrate from Amazon EKS to Google Kubernetes Engine.
Migrating Node.js apps from Heroku to Cloud Run: Updated for the latest Heroku changes.
(New guide) Design secure deployment pipelines: Best practices for designing secure deployment pipelines based on your confidentiality, integrity, and availability requirements.
Twelve-factor app development on Google Cloud: Added new product information and security considerations. Removed outdated content.
(New guide) Identify and prioritize security risks with Wiz Security Graph and Google Cloud: Describes how to identify and prioritize security risks in your cloud workloads with Wiz Security Graph and Google Cloud.
(New guide) Connect Google Virtual Private Clouds to Oracle Cloud Infrastructure using Equinix: Use Equinix Network Edge and Partner Interconnect to deploy private, multi-cloud connectivity between Google Cloud VPC networks and Oracle® VCNs.
Implement your Google Cloud landing zone network design: Updated to reflect the current features of Private Service Connect.
Decide the network design for your Google Cloud landing zone: Added more details to the design options.
Stream logs from Google Cloud to Splunk: Converted the Google Cloud-to-Splunk logging guide into a reference architecture that includes design considerations.
Google Cloud Architecture Framework: Updated the best practices in the Cost Optimization category.
Google Cloud infrastructure reliability guide: Updated the aggregate availability calculations to reflect changes in the availability SLAs for Compute Engine and Cloud SQL.
Landing zone design in Google Cloud: Updated the section, "Identify resources to help implement your landing zone."
Google Cloud Architecture Framework: AI/ML: Updated the list of AI and ML services in the System Design category.
GKE Enterprise reference architecture: Google Distributed Cloud Virtual for Bare Metal: Added load balancing information and project details. Updated the IP address allocation, cluster architecture, and node sizing information.
(New guide) Import data from an external network into a secured BigQuery data warehouse: Describes an architecture that you can use to help secure a data warehouse in a production environment, and provides best practices for importing data into BigQuery from an external network, such as an on-premises environment.
(New guide) Use distributed tracing to observe microservice latency: Shows how to capture trace information on microservice applications using OpenTelemetry and Cloud Trace.
(New guide) Deploy a secured serverless architecture using Cloud Functions: Provides guidance on how to help protect serverless applications that use Cloud Functions (2nd gen) by layering additional controls onto your existing foundation.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2026-07-27 UTC.
