What's new in the Architecture Center

(New guide) NCC Cross-Cloud Network with NVAs and regional failover: A reference architecture for including NVAs in an NCC deployment. This deployment allows for regional failover.

(New guide) Microsoft SQL Server Always On availability group in Google Cloud: A reference architecture for deploying high-availability (HA) Microsoft SQL Server databases in Google Cloud by using Always On availability groups.

(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:

• Updated the data ingestion and quality evaluation subsystems to use Cloud Run functions instead of Cloud Run jobs.
• Updated the data ingestion subsystem to show generic components for data preparation and embeddings generation.

(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:

• Added information about Google Cloud Managed Lustre and DDN Infinia.
• Updated the protocol specifications for Filestore.
• Updated the protocol and performance specifications for NetApp Volumes.

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.