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Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more. Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources.
Dec 22 – Dec 26

To design production-ready AI agents, you must choose the right tools for memory, reasoning, and orchestration. To simplify this process, Choose your agentic AI architecture components provides an iterative framework to help you select products and tools that best match your workload characteristics. To build and deploy secure and reliable single-agent AI systems on a scalable serverless platform, see our Single-agent AI system using ADK and Cloud Run architecture guide.

Dec 15 – Dec 19

Announcing Advanced Governance Capabilities to Vertex AI Agent Builder: Today, with the integration of the Cloud API Registry, we’re excited to bring enhanced tool governance capabilities to Vertex AI Agent Builder. With this latest update, administrators can now manage available tools for developers across your organization directly in Vertex AI Agent Builder Console, and developers can leverage tools managed by the registry with a new ApiRegistry. Following last month’s expansion of our Agent Builder platform, we are also introducing new capabilities across the entire agent lifecycle to help developers build faster using new ADK capabilities and visual tools, and scale with high performance through the expansion of Agent Engine services, including the general availability of support for sessions and memory. Read more below.Vertex AI Agent Builder provides the unified platform to manage the entire agent lifecycle, helping you close the gap from prototype to a production-ready agent. To explore these new features, visit the updated Agent Builder documentation and release notes.
Single-tenant Cloud HSM is now Generally Available: We’re thrilled to announce the General Availability (GA) of Single-tenant Cloud HSM – a standards compliant, highly available, and scalable HSM cluster that provides you complete control over your cryptographic keys for highly sensitive workloads in the cloud for general purpose applications. Customers have complete control over their cryptographic keys and the ability to manage their own admin credentials through our gcloud APIs, which establish a cryptographically isolated cluster of dedicated HSM partitions for each customer. Single-tenant Cloud HSM is integrated with Cloud KMS, allowing its use with Customer-Managed Encryption Keys (CMEK). Single-tenant Cloud HSM is available in the following regions: us-central1, us-east4, europe-west1, and europe-west4.
Advanced AI, data, and compliance security capabilities are now available to Security Command Center (SCC) Premium pay-as-you-go (PayGo) customers. Previously exclusive to Enterprise and Premium subscriptions, we now offer to PayGo customers the AI Security Dashboard, Data Security Posture Management (DSPM), Compliance Manager, and Security Graph, including Graph Search and Correlated Threats. This update can help you address novel risks from generative AI and autonomous agents by offering integrated, automated protection for both traditional and AI workloads in Google Cloud. Customers can start a 30-day free trial to access the full SCC Premium experience.

Dec 8 – Dec 12

Application Design Center is now Generally AvailableWe’re excited to announce the General Availability (GA) of Application Design Center, enabling platform teams and developers to streamline cloud application infrastructure design, deployment, and evolution, ensuring security and best practices.This GA launch includes powerful new capabilities such as enterprise-grade governance with public APIs and gcloud CLI support; bring your own Terraform, full compatibility with VPC Service Controls; and simplified onboarding with app-managed project support. To learn more, read the Application Design Center GA launch blog.

Apigee Feature Templater Simplifies API Proxy Development for EveryoneThe new open-source Apigee Feature Templater (AFT) streamlines API proxy authoring by turning complex policies into reusable building blocks called “features.” Non-experts can quickly assemble robust proxies—including AI Gateways, security, and data masking—using simple CLI or REST commands. AFT accelerates time-to-market by enabling expert developers to delegate feature creation and empowering a broader team to compose APIs. Read the full release details.

Navigating the Industry Shift in Client Authentication for Apigee mTLSAn industry policy change is phasing out the Client Authentication Extended Key Usage (EKU) in public certificates, directly impacting server-to-server mTLS for Apigee. This shift forces organizations away from Public CAs to manage their own Private PKI to maintain service continuity by mid-2026. This article presents the two paths: implementing a Private Certificate Authority (Private CA), ideally using Google Cloud Certificate Authority Service (CAS) for immediate mTLS continuity; or modernizing long-term with Demonstrating Proof of Possession (DPoP) for maximum operational efficiency. Read about the two paths to mTLS continuity.

Learn how to implement Kubernetes Secrets in Apigee hybridApigee hybrid introduces direct, read-only access to custom Kubernetes Secrets within API proxies. This exclusive feature offers a superior way to handle highly sensitive credentials (like private keys and backend passwords) compared to KVMs. Secrets never leave the cluster boundary, ensuring enhanced compliance and security. It enables a clean separation of duties, allowing cluster operators to manage credentials via GitOps workflows while API developers securely reference them using flow variables, without ever viewing the raw sensitive data. Read the full article.

Don’t let your AI program fail at the final hurdle. Our new guide, Successful Chatbot: 5 Steps from ROI to Rollout, outlines the essential practices for rigorous customer testing and strategic deployment. Learn how to align testing with business goals, define clear evaluation criteria, and drive actionable insights. The post emphasizes that delivering a successful AI program requires more than just domain expertise, highlighting the importance of clear scoping, strategic staffing, and disciplined financial planning. This is crucial for maximizing confidence in your AI’s long-term impact, especially in regulated industries like healthcare.Useful Product links – Google Cloud’s Vertex AI & Agentspace, Google Cloud’s Healthcare API, and, Google Cloud’s Natural Language API

Apigee Now Supports Model Context Protocol (MCP) for Secure Agentic ToolsGoogle has expanded support for Model Context Protocol (MCP) with the release of fully-managed, remote MCP servers, giving developers worldwide consistent and enterprise-ready access to Google and Google Cloud services. This includes support for MCP in Apigee, which makes it possible for agents to use your secure, governed APIs and custom workflows cataloged in Apigee API hub as tools to complete tasks for end users. With Apigee’s support for MCP, you don’t need to make any changes to your existing APIs, write any code, or deploy and manage any local or remote MCP servers. Apigee uses your existing API specifications and manages the underlying infrastructure and transcoding, so that you can focus on the business logic for your agents. Read the full announcement.

Introducing Fully-Managed MCP Servers to Power Agentic AIGoogle Cloud is announcing fully-managed, remote Model Context Protocol (MCP) servers, enhancing Google’s API infrastructure to provide a unified, enterprise-ready layer for AI agents. This eliminates the burden on developers to install and manage individual MCP servers. Now, AI agents can reliably use trusted Google services like Google Maps, BigQuery, Compute Engine, and GKE as tools to perform complex tasks. This unified approach, managed via Cloud IAM and Apigee API Hub, ensures rigorous governance, security, and observability for all agentic interactions. Read the full announcement.

Marketplace Customer Credit Program now available for Marketplace Channel Private OffersGoogle Cloud’s Marketplace Customer Credit Program offers up to 3% in Google Cloud credits when customers purchase an eligible cloud marketplace solution for the first time, whether directly through an ISV or via a chosen channel partner. Learn more.

Two-step control plane minor upgrades with rollback safety in Public previewUpgrading a production Kubernetes cluster can be a stressful, high-stakes event. GKE’s new two-step control plane minor upgrade with rollback safety gives you a safe window to validate a new minor version before committing, with a simple rollback option if you find any issues. By decoupling control plane binary changes from new API and feature changes, you can easily revert to the previous minor version if issues arise during the validation period. Learn more about this new, safer upgrade process.

Google named a Leader in the 2025 IDC MarketScape for Worldwide Hyperscaler MarketplacesIDC Marketscape has positioned Google as a Leader in the 2025 IDC MarketScape for Worldwide Hyperscaler Marketplaces. We believe this recognition underscores our commitment to deliver a cloud marketplace experience that fuels the AI agent economy and accelerates innovation. This achievement reflects our dedication to creating an open and interoperable agentic AI ecosystem for our customers and partners. Learn more.

Dec 1 – Dec 5

Unlock the full potential of your data with Object Contexts in Google Cloud StorageThis new feature provides a foundation for semantic storage and actions, allowing you to integrate Gemini with GCS objects and enrich your objects in a more intelligent and meaningful way. Learn how to get started with Object Contexts and revolutionize your data workflows. Learn more.

Nov 24 – Nov 28

Boost API Security: IP Allowlisting and ML Enhancements for Apigee Abuse DetectionTo keep your applications safe, it’s critical to detect and block attacks on your APIs as quickly as possible. In the past few months, we’ve made some changes to Advanced API Security’s Abuse Detection feature to make it easier and faster to identify legitimate attacks and take action. Get all the details on Apigee’s new IP allowlisting.
Apigee AI Gateway Deep Dive on December 11Join the final Apigee Community Tech Talk of the year for a deep dive into the Apigee AI Gateway. This session provides practical details on integrating, proxying, and converting complex MCP protocol services with traditional REST backends. Learn specific techniques for securing, monitoring, and gaining technical control over MCP backends to meet enterprise-grade governance requirements. Register now

Nov 10 – Nov 14

Deploy n8n to Cloud RunWith just a few commands, you can deploy n8n to Cloud Run and have it up and running, ready to supercharge your business with AI workflows that can manage spreadsheets, read and draft emails, and more. n8n and Cloud Run are both easy to use and powerful tools that empower developers to do more with AI. Learn more here.
GKE Node Memory Swap in Public previewYou can now configure swap space on your GKE Standard nodes to provide a crucial buffer against Out-of-Memory (OOM) errors for memory-intensive applications, especially during unexpected usage spikes. Enabling swap can improve workload resilience, reduce pod evictions due to memory pressure, and enhance overall application stability and cost-effectiveness. This feature is currently available in a public preview.

Nov 3 – Nov 7

Announcing the Data Engineering AgentData teams can now automate complex SQL pipeline tasks with the new Data Engineering Agent for BigQuery, available in Preview. This agent simplifies development, maintenance, and troubleshooting, allowing engineers to focus on strategic initiatives. It supports natural language pipeline creation, intelligent modification, and seamless migration from legacy tools.Transform your data engineering workflows today!
From Threat Model to TTX: Bringing a New Design Partner to the TableGain an overview of threat modeling, how threat models can be performed rapidly, and why threat model scenarios make excellent tabletop scenarios – especially for products that are still in development.To get more information about threat modeling or tabletop exercises, check out The Defender’s Advantage or reach out to a Mandiant cybersecurity expert for specialized assistance.
Application Monitoring now includes a Topology.Application Monitoring now includes a graphical representation of runtime dependencies (i.e Topology) for your App Hub defined application. This now allows you to quickly understand your app architecture, spot anomalous runtime interactions and resolve issues flagged from alerts quicker. Runtime dependencies are extracted from the OpenTelemetry traces you send to Cloud Trace from your App Hub registered workload.Follow the outline here to register your app and unlock the benefits of Application Monitoring and its newly launched Topology
Supercharge AI Agents: Apply Enterprise Governance to GenAI Workflows with ApigeeAs Generative AI agents move to production, you need control over cost, reliability, and security. A powerful new pattern introduces Apigee as the unified AI Agent Gateway for Large Language Model (LLM) calls. Route agent traffic through Apigee to gain immediate enterprise-grade governance, including dynamic circuit breaking, token consumption quotas, and sensitive data masking. A new Apigee wrapper for the Agent Development Kit (ADK) simplifies implementation. Turn your agents into manageable, secure AI products.Read the full article and explore the new pattern.

Oct 20 – Oct 24

Dataframe visualization in Colab Enterprise. Use visualization cells to create custom, stylized visualizations of your DataFrames: no coding required! Choose your fields, chart type, aggregation, and color scheme, then see a visualization of your data without leaving your notebook. Check out the tutorial and get started with data visualization today.

Oct 13 – Oct 17

Build Serverless AI in the Cloud Run HackathonReady to go from idea to global scale in minutes? The Cloud Run Hackathon is here! Build serverless AI apps with AI Studio, orchestrate intelligent agents, or harness the power of GPUs. Compete for a share of $50,000+ in prizes!

Submissions are open from Oct 6, 2025 to Nov 10, 2025.
Learn more and register: run.devpost.com

Oct 6 – Oct 10

Multi-agent AI systems help you optimize complex and dynamic processes by segmenting them into discrete tasks that multiple specialized AI agents collaboratively execute. To get started with building secure and reliable multi-agent AI systems, see this reference architecture guide: Design a multi-agent AI system in Google cloud. The example architecture in this guide showcases a couple of agent patterns: sequential, and loop. For a comprehensive review of all the possible agent design patterns and for help with choosing patterns that are appropriate for your use cases, see this design guide: Choose a design pattern for your agentic AI system.

Sept 29 – Oct 3

Announcing Koog Supports for Agent2Agent protocol (A2A)The future of interconnected AI is here. We’re thrilled to announce that Koog now supports A2A, a protocol that lets agents talk directly, securely, and seamlessly across companies and clouds. For Kotlin developers, this unlocks a new era of powerful, enterprise-grade AI. Build sophisticated agents that automatically discover and collaborate with other services, all while calling on Google Cloud’s state-of-the-art models like Gemini directly from your workflows. Stop building bridges and start creating truly intelligent, interconnected systems today. Learn more about building with Koog, A2A, and Google Cloud.

Sept 15 – 19

Your AI is Now a Local Expert: Grounding with Google Maps is GAWe are excited to announce the General Availability (GA) of Grounding with Google Maps in Vertex AI. This feature lets developers build generative AI applications that are connected to real-world, up-to-date information from Google Maps, using its data on over 250 million places worldwide.To learn more and get started, visit our documentation and check out our demo.
Production-ready YOLO model training serving workflow on Vertex AIThis guide walks you through a complete, automated workflow for training a custom YOLO model on Vertex AI. You’ll learn how to use a custom training job, package the model in a custom prediction container, and register it in the Vertex AI Model Registry, making it ready for easy deployment. Best of all, this approach is designed to work directly with existing Vertex AI managed datasets for object detection, meaning you can reuse the same data you’re already using for AutoML models.Checkout details on developer forums

Sept 8 – 12

Scaling Inference To Billions of Users And AI Agents: Discover the architecture required to serve AI models at a planetary scale. This article details how Google Cloud’s ecosystem—from the GKE Inference Gateway for smart load balancing to the power of custom TPUs and open-source engines like vLLM—provides a production-ready path. Move beyond the hype and learn how to build for the next wave of AI. Explore the technical deep-dive.
We’re celebrating the one-year anniversary of bringing Confidential Computing with Intel TDX to Google Cloud. We’ve been shipping new capabilities to help you protect your most sensitive data while it’s in use. Now Generally Available:

Confidential GKE Nodes with Intel TDX: Secure entire Kubernetes clusters, node pools, and workloads.
Confidential Space with Intel TDX: Build secure data clean rooms for collaboration on sensitive information.
Confidential GPUs: Protect cutting-edge AI workloads with Confidential NVIDIA H100s GPUs on GCE and GKE.We’ve also expanded Intel TDX to more regions! Read the blog

Aug 25 – 29

Applied AI for Modern Manufacturers: New original growth series, hosted by Jake Hall, The Manufacturing Millennial, that dives into leading trends, best practices, and what companies are doing right now with AI in manufacturing. Hear from industry thought leaders – Rick Bullotta, Jonathan Wise, Walker Reynolds and Berardino Baratta – and Google Cloud experts – Praveen Rao, Eric Lam, Dave Nguyen Ph.D., Geoffrey Hirschheim, and Jim Anderson. Watch Modules 1 and 2 now, where we delve into the AI Innovation and trends and AI Costs and ROI in the Era of Digital Manufacturing. Next module kicks off Tuesday, Sep 2. Join now

Firestore with MongoDB compatibility is now generally available (GA): Developers can now build cost-effective, scalable, and highly reliable apps on Firestore’s serverless database using a familiar MongoDB-compatible API. With the general availability of Firestore with MongoDB compatibility, the 600,000 active developers within the Firestore community can now use existing MongoDB application code, drivers, and tools, as well as the open-source MongoDB ecosystem, with Firestore’s serverless service. Firestore offers benefits like multi-region replication, virtually unlimited scalability, up to 99.999% SLA, single-digit millisecond read performance, integrated Google Cloud governance, and pay-as-you-go pricing. Register now for the webinar on September 9th for a deep dive into Firestore with MongoDB compatibility.

Aug 18 – 22

Earth Engine in BigQuery is now Generally Available, bringing advanced geospatial analytics directly to your BigQuery workflows. Unlock insights with satellite data!

Aug 11 – Aug 15

New HPC VM and Slurm-gcp Images: A new HPC VM Image (under the project cloud-hpc-image-public) is now available, featuring a Rocky Linux 8-based image, IntelMPI v2021.16, and RDMA drivers. In partnership with SchedMD, new Slurm images (Slurm 25.05) have also been released. These are based on the latest HPC VM Image and are available for Ubuntu 22.04/24.04 Accelerator Images (ARM/AMD64) and Debian 12. These releases allow for the deployment of Slurm-ready clusters on GCP, providing the advantages of an HPC-optimized and performance-tested foundation. Read more.
Scaling our Gemini Embedding model in Vertex AI. Following increased popularity from its General Availability launch in May, we’ve recently increased quota and input size limits for customers of Vertex AI’s most powerful text embedding model, gemini-embedding-001.

Customers can now send up to 250 input texts per request (generating 250 embeddings) instead of only a single piece of text, bringing improved throughput and decreased round-trip network latency to large-scale embedding applications.
We’ve increased quota limits for this model by 10x for most users, allowing hassle-free scaling of embedding applications to millions of tokens per minute and beyond.Get started with Gemini Embeddings today!

Aug 4 – Aug 8

GKE Node Memory Swap in private preview: You can now configure swap space on your GKE Standard nodes to provide a crucial buffer against Out-of-Memory (OOM) errors for memory-intensive applications, especially during unexpected usage spikes. Enabling swap can improve workload resilience, reduce pod evictions due to memory pressure, and enhance overall application stability and cost-effectiveness. This feature is currently available in a private preview.

Contact your Google Cloud account team for more information and to request access.

If you’d like to see more configurations, please contact your account team or make a feature request on our issue tracker!

Unlock Peak Performance: GKE Topology Manager is Now Generally Available: For customers running performance-sensitive workloads like AI/ML and HPC, GKE Topology Manager is now GA and ready to optimize your performance through NUMA alignment. By ensuring CPU, memory, and GPU resources are allocated on the same NUMA node, the Topology Manager minimizes cross-socket latency and maximizes throughput for your most demanding applications. Configure your alignment policies via the NodeConfig API to achieve significant performance gains.

Achieve these performance gains by configuring your alignment policies via the NodeConfig API.
If you’d like to see more expansion of Topology manager, please contact your account team or make a feature request on our issue tracker!

Fine-Tune at Scale: A Massive GKE NodeConfig Expansion for All Workloads: GKE has massively expanded node customization capabilities, adding nearly 130 new Sysctl and Kubelet configurations. This gives you finer-grained control for any workload needing node customization, performance requirements, or application-specific tuning. By replacing complex DaemonSets with native controls, you can benefit from enhanced security, high flexibility, faster node startup times, and less operational management.

Check out our public documentation to learn how to consume these new NodeConfig options.
If you’d like to see more configurations, please contact your account team or make a feature request on our issue tracker!

New capability for managing licenses in Compute Engine: We are announcing a new capability in Compute Engine which allows users to easily change the OS licenses on their VMs. Users can now append, remove, or replace OS licenses, enabling seamless transitions between license types—such as converting Red Hat Enterprise Linux from pay-as-you-go (PAYG) to bring-your-own subscription (BYOS), or upgrading from Ubuntu to Ubuntu Pro—without needing to redeploy instances. This feature empowers customers to meet their evolving licensing with speed and flexibility. To learn more, read about managing licenses on Compute Engine.

GKE Turns 10 Hackathon: Calling all developers! Google Kubernetes Engine (GKE) is turning 10, and we’re celebrating with a hackathon! Join us to build powerful AI agents that interact with microservice applications using Google Kubernetes Engine and Google AI models. Compete for over $50,000 in prizes and demonstrate the power of building agentic AI on GKE.

Submissions are open from Aug 18, 2025 to Sept, 22 2025
Learn more and register: gketurns10.devpost.com

Jul 28 – Aug 1

Now GA: C4 VMs with Local SSD, bare metal, and larger shapes, on Intel Xeon 6: C4’s expanded shapes are now GA! This expansion introduces C4 shapes with Google’s next-gen Titanium Local SSD, C4 bare metal instances, and new extra-large shapes, all powered by the latest Intel Xeon 6 processors, Granite Rapids. We’re excited to be the first leading hyperscaler to bring Xeon 6 to customers, delivering performance gains of up to 30% for general compute and up to 60% for ML recommendation workloads, and up to 35% lower access latency on Titanium Local SSD shapes. Learn more here!

Jul 14 – 18

DMS SQL Server to PostgreSQL migrations are now generally available! Accelerate your SQL Server modernization to Cloud SQL for PostgreSQL or AlloyDB for PostgreSQL with:

Automatic database schema and code conversion
Gemini augmented code conversion
Gemini assisted PostgreSQL training and code improvements
Low-downtime, CDC based data movement

Learn more and start your migration journey today!
Jul 7 – 11

Level up your AI Agent game with “The Agent Factory,” a new video podcast for developers! We’re going beyond the buzz to explore practical design, build, deploy, & management strategies for production-ready AI agents using Google Cloud. Expect code snippets, architecture deep dives, and integrations with open-source frameworks. Subscribe now!

Jun 23 – 27

Announcing partnership between Maxim AI and Google Cloud’s Vertex AI to evaluate agentic applications — Maxim AI offers a comprehensive platform to help teams build, evaluate, and observe their AI agents with greater speed and confidence, covering the entire AI lifecycle from prompt engineering to production monitoring. This new partnership deeply integrates Vertex AI’s Gen AI evaluation service directly within the Maxim AI environment, allowing users to leverage Gemini to power assistant responses and evaluate them using Vertex AI’s comprehensive suite of evaluators. This provides access to metrics such as helpfulness, relevance, safety, and trajectory. The setup allows users to simulate, evaluate, and trace complex multi-turn interactions on Maxim, helping teams bring reliable AI products to market faster through a seamless developer experience. To learn more, check out this blog from Maxim AI

Run non-request workloads at scale with Cloud Run Worker Pools, now in Public Preview — Looking for the ease-of-use and scalability of serverless, without being limited to HTTP request-driven workloads? Cloud Run Worker Pools provide the same elasticity and high-quality developer experience as Cloud Run Services, but are designed for non-request workloads. Worker Pools are ideal for pull-based use cases like processing messages from Pub/Sub or Kafka, and other backend processing. Check out the public documentation to learn more about how to choose between Services, Jobs, and Worker Pools. Then give Worker Pools a try by deploying a sample Worker Pool.

Building a Multi-Agent Research Assistant for Financial Analysis with Schroders & Google Cloud — Financial analysts spend hours grappling with ever-increasing volumes of market and company data to extract key signals, combine diverse data sources, and produce company research. To maximise its edge as an active manager, Schroders wants to enable its analysts to shift from data collection to the higher-value strategic thinking that is critical for business scalability and client investment performance. To achieve this, Schroders and Google Cloud collaborated to build a multi-agent research assistant prototype using Vertex AI Agent Builder. Find out more here.

Jun 16 – 20

Simplify Your Multi-Cloud Strategy with Cloud Location Finder, now in Public Preview: As cloud environments expand beyond traditional architectures to include multiple clouds, managing your infrastructure effectively becomes more complex. Imagine effortlessly accessing consistent and up-to-date location information across different cloud providers, so your multi-cloud applications are designed and optimized with performance, security, and regulatory compliance in mind. Today, we are making this a reality with Cloud Location Finder, a new Google Cloud service which provides up-to-date location data across Google Cloud, Amazon Web Services (AWS), Azure, and Oracle Cloud Infrastructure (OCI). Now, you can strategically deploy workloads across different cloud providers with confidence and control. Cloud Location Finder is accessible via REST APIs and gcloud CLI, explore the Cloud Location Finder documentation and blog to learn more.

SOTA Gemini Text Embedding is Now Generally Available in Vertex AI: We recently launched a new Gemini Embedding text model (gemini-embedding-001) through the Vertex AI GenAI API. This groundbreaking model, leveraging Gemini’s core language understanding, sets a new benchmark for text embeddings. It’s the first unified model to excel across English, multilingual text, and code, outperforming previous models (text-embedding-005, text-multilingual-embedding-002) and achieving top ranking on the MTEB Multilingual leaderboard (100+ tasks). Our internal benchmarks demonstrate substantial performance improvements across various industry verticals, including retail, news, finance, healthcare, legal, and code. Detailed results are available in our technical report.

Backup vaults now support disk backups and multi-regions: We’ve added exciting new features to Google Cloud Backup and Disaster Recovery service! You can now secure your Persistent Disk and Hyperdisk backups in backup vaults, protecting them from cyber attacks and accidental data loss. In addition, backup vaults can now be created in multi-region storage locations, maximizing your data resilience and supporting compliance with business continuity requirements. Check out the blog to learn more!

DeepSeek R1, a powerful 671B parameters model, is now available as a fully managed API on Vertex AI in Preview, making advanced AI capabilities more accessible to developers. This Model as a Service (MaaS) offering eliminates the need for extensive GPU resources and infrastructure management, allowing developers to focus on building applications. DeepSeek R1 on Vertex AI provides a simple, scalable API with features like transparent “chain-of-thought” reasoning and enterprise-ready security. It’s currently available at no additional cost during the preview, and can be accessed via UI, REST API, or the OpenAI Python API Client Library. Learn more.

Jun 9 – 13

Serverless Spark Now GA in BigQuery: Unified Analytics, Accelerated: Google Cloud Serverless for Apache Spark is now generally available in BigQuery, offering a unified developer experience in BigQuery Studio. Run Spark and SQL side-by-side on the same data, powered by the Lightning Engine for up to 3.6x faster performance and enhanced with Gemini productivity. Simplify your data pipelines and accelerate insights with this deeply integrated, zero-ops solution.
Cloud Pub/Sub introduced Pub/Sub Single Message Transforms (SMTs) to make it easy to perform simple data transformations right within Pub/Sub: An overarching goal of Pub/Sub is to simplify streaming architectures. We already greatly simplified data movement with Import Topics and Export Subscriptions, which removed the need to use additional services for ingesting raw streaming data through Pub/Sub into destinations like BigQuery. Pub/Sub Single Message Transforms (SMTs), designed to be a suite of features making it easy to validate, filter, enrich, and alter individual messages as they move in real time. The first SMT is available now: JavaScript User-Defined Functions (UDFs), which allows you to perform simple, lightweight modifications to message attributes and/or the data directly within Pub/Sub via snippets of JavaScript code. JavaScript UDFs as the first Single Message Transform is generally available starting today for all users. You’ll find the new “Add Transform” option in the Google Cloud console when you create a topic or subscription in your Google Cloud project. You can also use gcloud CLI to start using JavaScript Single Message Transforms today.
This analysis evaluates the efficiency of fine-tuning a Llama 3-8B model on Vertex AI using both a single A100 GPU and a distributed four-A100 setup with Axolotl. While both methods achieved similar model convergence, the results underscore the power of distributed training. The process, which took 1 day and 20 hours on a single device, was completed in just 11 hours in the distributed environment—a dramatic acceleration. This speed was achieved with consistently high GPU utilization (94%), though at the cost of higher system and GPU memory overhead. For a detailed breakdown of the methodology, resource utilization metrics, and performance curves, you can review the complete work here.

May 26 – 30

Cloud Run GPUs are now GA: NVIDIA GPU support for Cloud Run is now generally available, offering a powerful runtime for a variety of use cases that’s also remarkably cost-efficient. Developers can now get on-demand access to GPUs with our serverless runtime, Cloud Run. Follow the footsteps of customers like MidJourney, vivo, and Wayfair. Read blog.
Datastream now supports MongoDB as a source! Seamlessly ingest data from MongoDB (Replica Sets, Sharded Clusters, self-hosted, AtlasDB) into BigQuery/Cloud Storage. Enjoy scalable, fully-managed data streaming with backfill and CDC, enabling real-time insights and data-driven decisions. Link

May 19 – May 23

Beyond cuts and fades: Understanding narrative flow with Gemini for accurate scene transition detection — Google Cloud’s Gemini models are revolutionizing video understanding by accurately detecting narrative scene transitions, moving beyond simple cuts and fades. This breakthrough technology understands the holistic context of videos by analyzing visual, audio, and textual elements simultaneously. Media companies can now convert passive video assets into structured data, enabling intelligent content discovery, strategic ad placement, and personalized viewing experiences. The result? Up to 38% increased viewer engagement and 27% reduced abandonment rates.

Read more on the medium blog.

Learn more and access the code repository: View Code Repo

Announced at I/O: Deploy AI apps to Cloud Run from AI Studio and MCP — We are making AI deployments easier and more accessible by introducing new ways to deploy your apps to Cloud Run.

You can deploy applications developed in AI Studio with a click of a button to Cloud Run, including Gemma 3.

Model Context Protocol(MCP) is becoming a popular open protocol standardizing how AI agents interact with other tools. Now with Cloud Run MCP server, you can deploy apps from compatible AI agents like from Claude or VS Code Copilot.

Read blog to learn more.

May 12 – May 16

Google for Startups Accelerator: AI For Energy now accepting applications!Applications are now open for startups headquartered in Europe and Israel, working on solutions for utilities, grid operators and energy developers; solutions for residential and commercial end-use customers focused on demand flexibility and solutions for industrial customers. This equity-free program offers 10 weeks of intensive mentorship and technical project support to startups integrating AI into their core energy services or products. Selected startups will collaborate with a cohort of peer founders and engage with leaders across Google and the energy sector. The curriculum will provide founders with access to AI tools and include workshops on tech and infrastructure, UX and product, growth, sales, leadership and more. Learn more and apply before June 30th, 2025.

Extending Google Cloud Workstations containers to run any GUI based programAre you having difficulty customizing Google Cloud Workstations to run a GUI program outside of the supported configurations of IDE’s? If so, you’re not alone. In this article we discuss how to use the base Workstations Docker image and build it to run a terminal and Google Chrome.

Google Cloud Marketplace simplifies deals and improves economics. Announcing three initiatives that build upon Google Cloud Marketplace as a growth engine for customers and partners:

Improving partner deal economics to help partners retain more earnings by moving to a variable revenue share model

Simplifying commit drawdown for purchases through channel partners

Unlocking new workloads with the Marketplace Customer Credit Program incentiveLearn more

2025 Google Cloud DORA Awards are now open for submission!Has your team achieved remarkable success through DORA principles? It’s time to shine. We’re thrilled to announce the launch of the 2025 Google Cloud DORA Awards, celebrating outstanding achievements in technology delivery and operational performance. Submit your story today!

May 5 – May 9

AI assisted development with MCP Toolbox for DatabasesWe are excited to announce new updates to MCP Toolbox for Databases. Developers can now use Toolbox from their preferred IDE, such as Cursor, Windsurf, Claude Desktop, more and leverage our new pre-built tools such as execute_sql and list_tables for AI-assisted development with Cloud SQL for PostgreSQL, AlloyDB and self-managed PostgreSQL.

Get Started with MCP Toolbox for Databases

Apr 28 – May 2

Itching to build AI agents? Join the Agent Development Kit Hackathon with Google Cloud! Use ADK to build multi-agent systems to solve challenges around complex processes, customer engagement, content creation, and more. Compete for over $50,000 in prizes and demonstrate the power of multi-agent systems with ADK and Google Cloud.

Submissions are open from May 12, 2025 to June 23, 2025.
Learn more and register here.

Apr 21 – Apr 25

Iceland’s Magic: Reliving Solo Adventure through GeminiEmbark on a journey through Iceland’s stunning landscapes, as experienced on Gauti’s Icelandic solo trip. From majestic waterfalls to the enchanting Northern Lights, Gautami then takes these cherished memories a step further, using Google’s multi-modal AI, specifically Veo2, to bring static photos to life. Discover how technology can enhance and dynamically relive travel experiences, turning precious moments into immersive short videos. This innovative approach showcases the power of AI in preserving and enriching our memories from Gauti’s unforgettable Icelandic travels. Read more.

Introducing ETLC – A Context-First Approach to Data Processing in the Generative AI Era: As organizations adopt generative AI, data pipelines often lack the dynamic context needed. This paper introduces ETLC (Extract, Transform, Load, Contextualize), adding semantic, relational, operational, environmental, and behavioral context. ETLC enables Dynamic Context Engines for context-aware RAG, AI co-pilots, and agentic systems. It works with standards like the Model Context Protocol (MCP) for effective context delivery, ensuring business-specific AI outputs. Read the full paper.

Apr 14 – Apr 18

What’s new in Database CenterWith general availability, Database Center now provides enhanced performance and health monitoring for all Google Cloud databases, including Cloud SQL, AlloyDB, Spanner, Bigtable, Memorystore, and Firestore. It delivers richer metrics and actionable recommendations, helps you to optimize database performance and reliability, and customize your experience. Database Center also leverages Gemini to deliver assistive performance troubleshooting experience. Finally, you can track the weekly progress of your database inventory and health issues.
Get started with Database Center today

Access Database Center in Google Cloud console

Review the documentation to learn more

Apr 7 – Apr 11

This week, at Google Cloud Next, we announced an expansion of Bigtable’s SQL capabilities and introduced continuous materialized views. Bigtable SQL and continuous materialized views empower users to build fully-managed, real-time application backends using familiar SQL syntax, including specialized features that preserve Bigtable’s flexible schema — a vital aspect of real-time applications. Read more in this blog.
DORA Report Goes Global: Now Available in 9 Languages!Unlock the power of DevOps insights with the DORA report, now available in 9 languages, including Chinese, French, Japanese, Korean, Portuguese, and Spanish. Global teams can now optimize their practices, benchmark performance, and gain localized insights to accelerate software delivery. The report highlights the significant impact of AI on software development, explores platform engineering’s promises and challenges, and emphasizes user-centricity and stable priorities for organizational success. Download the DORA Report Now
New Google Cloud State of AI Infrastructure Report ReleasedIs your infrastructure ready for AI? The 2025 State of AI Infrastructure Report is here, packed with insights from 500+ global tech leaders. Discover the strategies and challenges shaping the future of AI and learn how to build a robust, secure, and cost-effective AI-ready cloud. Download the report and enhance your AI investments today. Download the 2025 AI infrastructure report now
Google Cloud and Oracle Accelerate Enterprise Modernization with New Regions, Expanded CapabilitiesAnnouncing major Oracle Database@Google Cloud enhancements! We’re launching the flexible Oracle Base Database Service and powerful new Exadata X11M machines. We’re rapidly expanding to 20 global locations, adding new Partner Cross-Cloud Interconnect options, and introducing Cross-Region Disaster Recovery for Autonomous Database. Benefit from enhanced Google Cloud Monitoring, integrated Backup & DR, plus expanded support for enterprise applications like SAP. Customers can run critical Oracle workloads with more power, resilience, and seamless Google Cloud integration. Get started right away from your Google Cloud Console or learn more here.

Mar 17 – Mar 21

Cloud CISO Perspectives: 5 tips for secure AI success – To coincide with new AI Protection capabilities in Security Command Center, we’re offering 5 tips to set up your organization for secure AI success.
Our 4-6-3 rule for strengthening security ties to business: The desire to quickly transform a business can push leaders to neglect security and resilience, but prioritizing security can unlock value. Follow these 4 principles, 6 steps, and 3 metrics to use a security-first mindset to drive business results.
The new Data Protection Tab in Compute Engine ensures your resources are protected: Not only have we co-located your backup options, but we also have introduced smart default data protection for any Compute Engine instance created via Cloud Console. Here’s how it works.
DORA report – Impact of Generative AI in Software DevelopmentThis report builds on and extends DORA’s research into AI. We review the current landscape of AI adoption, look into its impact on developers and organizations, and outline a framework and practical guidance for successful integration, measurement, and continuous improvement. Download the report!

Mar 10 – Mar 14

Protecting your APIs from OWASP’s top 10 security threats: We compare OWASP’s top 10 API security threats list to the security capabilities of Apigee. Here’s how we hold up.

Project Shield makes it easier to sign up, set up, automate DDoS protection: It’s now easier than ever for vulnerable organizations to apply to Project Shield, set up protection, and automate their defenses. Here’s how.

How Google Does It: Red teaming at Google scale – The best red teams are creative sparring partners for defenders, probing for weaknesses. Here’s how we do red teaming at Google scale.

AI Hypercomputer is a fully integrated supercomputing architecture for AI workloads – and it’s easier to use than you think. Check out this blog, where we break down four common use cases, including reference architectures and tutorials, representing just a few of the many ways you can use AI Hypercomputer today.

Transform Business Operations with Gemini-Powered SMS-iT CRM on Google Cloud: SMS-iT CRM on Google Cloud unifies SMS, MMS, email, voice, and 22+ social channels into one Smart Inbox. Enjoy real-time voice interactions, AI chatbots, immersive video conferencing, AI tutors, AI operator, and unlimited AI agents for lead management. Benefit from revenue-driven automation, intelligent appointment scheduling with secure payments, dynamic marketing tools, robust analytics, and an integrated ERP suite that streamlines operations from project management to commerce. This comprehensive solution is designed to eliminate inefficiencies and drive exponential growth for your business. Experience the Future Today.

Join us for a new webinar, Smarter CX, Bigger Impact: Transforming Customer Experiences with Google AI, where we’ll explore how Google AI can help you deliver exceptional customer experiences and drive business growth. You’ll learn how to:

Transform Customer Experiences: With conversational AI agents that provide personalized customer engagements.

Improve Employee Productivity & Experience: With AI that monitors customers sentiment in real-time, and assists customer service representatives to raise customer satisfaction scores.

Deliver Value Faster: With 30+ data connectors and 70+ action connectors to the most commonly used CRMs and information systems.Register here

Mar 3 – Mar 7

Hej Sverige! Google Cloud launches new region in Sweden – More than just another region, it represents a significant investment in Sweden’s future and Google’s ongoing commitment to empowering businesses and individuals with the power of the cloud. This new region, our 42nd globally and 13th in Europe, opens doors to opportunities for innovation, sustainability, and growth — within Sweden and across the globe. We’re excited about the potential it holds for your digital transformations and AI aspirations.
[March 11th webinar] Building infrastructure for the Generative AI era: insights from the 2025 State of AI Infra report: Staying at the forefront of AI requires an infrastructure built for AI. Generative AI is revolutionizing industries, but it demands a new approach to infrastructure. In this webinar, we’ll unveil insights from Google Cloud’s latest research report and equip tech leaders with a practical roadmap for building and managing gen AI workloads, including: the top gen AI use cases driving the greatest return on investment, current infrastructure approaches and preferences for Generative AI workloads, the impact of performance benchmarks, scalability, and security on cloud provider selection. Register today.
Cloud CISO Perspectives: Why PQC is the next Y2K, and what you can do about it: Much like Y2K 25 years ago, post-quantum cryptography may seem like the future’s problem — but it will soon be ours if IT doesn’t move faster, explains Google Cloud’s Christiane Peters. Here’s how business leaders can get going on PQC prep.
How Google Does It: Using threat intelligence to uncover and track cybercrime — How does Google use threat intelligence to uncover and track cybercrime? Google Threat Intelligence Group’s Kimberly Goody takes you behind the scenes.
5 key cybersecurity strategies for manufacturing executives — Here are five key governance strategies that can help manufacturing executives build a robust cybersecurity posture and better mitigate the evolving risks they face.
Datastream now offers Salesforce source in Preview. Instantly connect, capture changes, and deliver data to BigQuery, Cloud Storage, etc. Power real-time insights with flexible authentication and robust backfill/CDC. Unlock Salesforce data for Google Cloud analytics, reporting, and generative AI. Read the documentation to learn more.
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Gen AI O11y for Go Developers
Gen AI O11y for Java Developers
Gen AI O11y for NodeJS Developers
Gen AI O11y for Python DevelopersStay tuned for future events at cloudonair.withgoogle.com.

Feb 24 – Feb 28

Rethinking 5G: Ericsson and Google Cloud are collaborating to redefine 5G mobile core networks with a focus on autonomous operations. By leveraging AI and cloud infrastructure, we aim to enhance efficiency, security, and innovation in the telecommunications industry. This partnership addresses the increasing demands of 5G and connected devices, paving the way for a more dynamic and intelligent network future, and setting the stage for next-generation technologies like 6G. Learn more here.
Adopt a principles-centered well-architected framework to design, build, deploy, and manage Google Cloud workloads that are secure, resilient, efficient, cost-efficient, and high-performing. Also get industry and technology-focused well-architected framework guidance, like for AI and ML workloads.

Feb 17 – Feb 21

Easier Default Backup Configuration for Compute Engine Instances – The Create a Compute Instance page in the Google Cloud console now includes enhanced data protection options to streamline backup and replication configurations. By default, an option to back up data is pre-selected, ensuring recoverability in case of unforeseen events. Learn more here.

Feb 10 – Feb 14

[Webinar] Generative AI for Software Delivery: Strategies for IT Leaders: Generative AI is transforming the way organizations build and deploy software. Join Google Cloud experts on February 26th to learn how organizations can leverage AI to streamline their software delivery, including: the role of gen AI in software development, how to use gen AI for migration and modernization, best practices for integrating gen AI into your existing workflows, and real-world applications of gen AI in software modernization and migration through live demos. Register here.

Feb 3 – Feb 7

SQL is great but not perfect. We’d like to invite you to reimagine how you write SQL with Google’s newest invention: pipe syntax (public available to all BigQuery and Cloud Logging users). This new extension to GoogleSQL brings a modern, streamlined approach to data analysis. Now you can write simpler, shorter and more flexible queries for faster insights. Check out this video to learn more.

Jan 13 – Jan 17

C4A virtual machines with Titanium SSD—the first Axion-based, general-purpose instance with Titanium SSD, are now generally available. C4A virtual machines with Titanium SSDs are custom designed by Google for cloud workloads that require real-time data processing, with low-latency and high-throughput storage performance. Titanium SSDs enhance storage security and performance while offloading local storage processing to free up CPU resources. Learn more here.

Jan 6 – Jan 10

A look back on a year of Earth Engine advancements: 2024 was a landmark year for Google Earth Engine, marked by significant advancements in platform management, cloud integration, and core functionality and increased interoperability between Google Cloud tools and services. Here’s a round up of 2024’s top Earth Engine launches.
Get early access to our new Solar API data and features: We’re excited to announce that we are working on 2 significant expansions to the Solar API from Google Maps Platform and are looking for trusted testers to help us bring them to market. These include improved and expanded buildings coverage and greater insights for existing solar installations with Detected Arrays. Learn more.
Google for Startups Accelerator: Women Founders applications are now open for women-led startups headquartered in Europe and Israel. Discover why this program could be the perfect fit for your startup and apply before January 24th, 2025.
Best of N: Generating High-Quality Grounded Answers with Multiple Drafts – We are excited to announce that Check Grounding API has released a new helpfulness score feature. Building on top of our existing groundedness score, we now enable users to implement Best of N to improve RAG response quality without requiring extensive model retraining. Learn more about Best of N and how it can help you here.

Quelle: Google Cloud Platform

24. Januar 2026

da Agency

How to Automate Arm Migration with Docker MCP Toolkit, VS Code, and GitHub Copilot

This post is a collaboration between Docker and Arm, demonstrating how Docker MCP Toolkit and the Arm MCP Server work together to simplify architecture migrations.

Moving workloads from x86 to Arm64 architecture has become increasingly important. Organizations seek to reduce cloud costs and improve performance. AWS Graviton, Azure Cobalt, and Google Cloud Axion have made Arm-based computing mainstream, promising 20-40% cost savings and better performance for many workloads.

But here’s the challenge: How do you migrate your applications to Arm without breaking things?

Traditional migration approaches require:

Manual code analysis for x86-specific dependencies

Tedious compatibility checks across multiple tools

Manual performance evaluation

What if you could orchestrate the entire Arm migration workflow from a single interface? Docker MCP Toolkit makes this possible.

By connecting specialized Arm migration tools directly to GitHub Copilot, you can automate compatibility analysis, intrinsic conversion, and performance prediction—all through natural conversation in VS Code.

Here’s what that looks like in practice: You ask GitHub Copilot to migrate your legacy C++ application to Arm64. Copilot doesn’t just tell you what needs changing—it actually executes: scanning your code for x86 intrinsics, converting x86 SIMD intrinsics to Arm SIMD intrinsics, updating your Dockerfile, predicting Arm performance improvements, and creating a pull request with all changes. All through natural conversation in VS Code. No manual porting. No up-front architecture expertise required.

If you have questions about any step in the process, you can directly ask Copilot, which will invoke the Arm MCP Server knowledge base tool. The knowledge base has information pulled directly from all Learning Paths on learn.arm.com, as well as knowledge of all Arm intrinsics, and will both summarize that information for you as well as provide links to the concrete documentation that you can peruse yourself.

Now you might ask – “Can’t I just rebuild my Docker image for Arm64?” True, for most applications. But when you hit that one legacy app with hand-optimized x86 assembly, AVX2 intrinsics, or architecture-specific compiler flags? That’s when Docker MCP Toolkit with the Arm MCP Server becomes essential.

By the end of this guide, you’ll migrate a real-world legacy application—a matrix multiplication benchmark written with AVX2 intrinsics for x86—to Arm64 automatically using GitHub Copilot and Docker MCP Toolkit.

What normally takes 5-7 hours of manual work will take you about 25 to 30 minutes.

The Arm Migration Challenge

Let me show you exactly what we’re solving. Consider a matrix multiplication benchmark originally written for x86-64 with AVX2 optimizations—the kind of code that makes Arm migration painful.

Here’s a Dockerfile that will cause problems when trying to migrate to Graviton:

FROM centos:6

# Install EPEL repository (required for some development tools)
RUN yum install -y epel-release &&
sed -i 's|^mirrorlist=|#mirrorlist=|g' /etc/yum.repos.d/epel.repo &&
sed -i 's|^#baseurl=http://download.fedoraproject.org/pub/epel|baseurl=http://archives.fedoraproject.org/pub/archive/epel|g' /etc/yum.repos.d/epel.repo

# Install build tools
RUN yum install -y
devtoolset-2-gcc
devtoolset-2-gcc-c++
devtoolset-2-binutils
make
&& yum clean all

WORKDIR /app
COPY *.h *.cpp ./

# AVX2 intrinsics are used in the code
RUN scl enable devtoolset-2 "g++ -O2 -mavx2 -o benchmark
main.cpp
matrix_operations.cpp
-std=c++11"

CMD ["./benchmark"]

Now you might ask why this won’t work on Arm? Looking at this Dockerfile, there are two immediate blockers for Graviton migration:

No Arm64 support in base image – The centos:6 image was built for x86 only, so this container won’t even start on Arm hardware.

x86-specific compiler flag – The -mavx2 flag tells the compiler to use AVX2 vector instructions, which don’t exist on Arm processors.

Even experienced developers miss these issues in larger codebases.

The source code uses AVX2 intrinsics for vectorized operations:

#include "matrix_operations.h"
#include <iostream>
#include <random>
#include <chrono>
#include <stdexcept>
#include <immintrin.h> // AVX2 intrinsics

Matrix::Matrix(size_t r, size_t c) : rows(r), cols(c) {
data.resize(rows, std::vector<double>(cols, 0.0));
}

void Matrix::randomize() {
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<> dis(0.0, 10.0);

for (size_t i = 0; i < rows; i++) {
for (size_t j = 0; j < cols; j++) {
data[i][j] = dis(gen);
}
}
}

Matrix Matrix::multiply(const Matrix& other) const {
if (cols != other.rows) {
throw std::runtime_error("Invalid matrix dimensions for multiplication");
}

Matrix result(rows, other.cols);

// x86-64 optimized using AVX2 for double-precision
for (size_t i = 0; i < rows; i++) {
for (size_t j = 0; j < other.cols; j++) {
__m256d sum_vec = _mm256_setzero_pd();
size_t k = 0;

// Process 4 elements at a time with AVX2
for (; k + 3 < cols; k += 4) {
__m256d a_vec = _mm256_loadu_pd(&data[i][k]);
__m256d b_vec = _mm256_set_pd(
other.data[k+3][j],
other.data[k+2][j],
other.data[k+1][j],
other.data[k][j]
);
sum_vec = _mm256_add_pd(sum_vec, _mm256_mul_pd(a_vec, b_vec));
}

// Horizontal add using AVX
__m128d sum_high = _mm256_extractf128_pd(sum_vec, 1);
__m128d sum_low = _mm256_castpd256_pd128(sum_vec);
__m128d sum_128 = _mm_add_pd(sum_low, sum_high);

double sum_arr[2];
_mm_storeu_pd(sum_arr, sum_128);
double sum = sum_arr[0] + sum_arr[1];

// Handle remaining elements
for (; k < cols; k++) {
sum += data[i][k] * other.data[k][j];
}

result.data[i][j] = sum;
}
}

return result;
}

double Matrix::sum() const {
double total = 0.0;
for (size_t i = 0; i < rows; i++) {
for (size_t j = 0; j < cols; j++) {
total += data[i][j];
}
}
return total;
}

void benchmark_matrix_ops() {
std::cout << "n=== Matrix Multiplication Benchmark ===" << std::endl;

const size_t size = 200;
Matrix a(size, size);
Matrix b(size, size);

a.randomize();
b.randomize();

auto start = std::chrono::high_resolution_clock::now();
Matrix c = a.multiply(b);
auto end = std::chrono::high_resolution_clock::now();

auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end – start);

std::cout << "Matrix size: " << size << "x" << size << std::endl;
std::cout << "Time: " << duration.count() << " ms" << std::endl;
std::cout << "Result sum: " << c.sum() << std::endl;
}

If you look at the following code, you might find that this code is heavily optimized for Intel/AMD x86 processors and won’t work on Arm.

x86-exclusive header – #include <immintrin.h> only exists on x86 systems. Arm uses <arm_neon.h> instead.

AVX2 intrinsics throughout – Every _mm256_* function is Intel-specific:

_mm256_setzero_pd() – Creates a 256-bit zero vector (Arm NEON is 128-bit)

_mm256_loadu_pd() – Loads 4 doubles at once (NEON loads 2)

_mm256_set_pd() – Sets 4 doubles (no direct NEON equivalent)

_mm256_add_pd() / _mm256_mul_pd() – 256-bit operations (NEON uses 128-bit)

_mm256_extractf128_pd() – Extracts high 128 bits (not needed on NEON)

Vector width mismatch – AVX2 processes 4 doubles per operation, while Arm NEON processes 2. The entire loop structure needs adjustment. (SVE/SVE2 on newer Arm cores (Neoverse V1/V2, Graviton 3/4) provides 256-bit or wider vector-length agnostic (VLA) registers, matching or exceeding AVX2 registers.)

Horizontal reduction logic – The horizontal add pattern using _mm256_extractf128_pd and _mm256_castpd256_pd128 is x86-specific and must be completely rewritten for Arm SIMD.

Manual conversion requires rewriting 30+ lines of intrinsic code, adjusting loop strides, and testing numerical accuracy. This is exactly where automated migration tools become essential.

Each of these issues blocks Arm migration in different ways. Manual migration requires not just converting intrinsics, but also modernizing the entire build infrastructure, finding Arm equivalents, and validating performance. For any substantial codebase, this becomes prohibitively expensive.

What GitHub Copilot Can and Can’t Do Without Arm MCP

Let’s be clear about what changes when you add the Arm MCP Server to Docker MCP Toolkit.

Without Arm MCP

You ask GitHub Copilot to migrate your C++ application from x86 to Arm64. Copilot responds with general advice: “Convert AVX2 intrinsics to NEON”, “Update your Dockerfile to use ARM64 base image”, “Change compiler flags”. Then you must manually research NEON equivalents, rewrite hundreds of lines of intrinsic code, update the Dockerfile yourself, hope you got the conversion right, and spend hours debugging compilation errors.

Yes, Copilot can write code. But without specialized tools, it’s guessing based on training data—not using concrete knowledge base documentation or using purpose-built tools to analyze your actual application architecture.

With Arm MCP + Docker MCP Toolkit

You ask GitHub Copilot the same thing. Within minutes, it:

Uses check_image tool to verify your base image supports ARM64

Runs migrate_ease_scan on your actual codebase to find x86-specific code

Uses knowledge_base_search to find correct Arm SIMD equivalents for every x86 intrinsic

Converts your code with architecture-specific accuracy

Updates your Dockerfile with Arm-compatible base images

Creates a pull request with all changes.

Real code gets scanned. Real intrinsics get converted. Real pull requests appear in your repository. Close VS Code, come back tomorrow, and the migration is ready to test, complete with documentation explaining every change.

The difference? Docker MCP Toolkit gives GitHub Copilot access to actual Arm migration tooling, not just general knowledge about Arm architecture.

Why This Is Different from Manual Migration

You could manually use Arm migration tools: install utilities locally, run checks, research intrinsics, update code. Here’s what that process looks like:

Manual process:

Install Arm migration tools (15 minutes)

Run compatibility scans (5 minutes)

Research each x86 intrinsic equivalent (30 minutes per intrinsic)

Manually rewrite code (2-3 hours)

Update Dockerfile (15 minutes)

Fix compilation errors (1-2 hours)

Document changes (30 minutes)

Total: 5-7 hours per application

With Docker MCP Toolkit + Arm MCP:

Ask GitHub Copilot to migrate (20 minutes)

Review and approve changes (10-20 minutes)

Merge pull request

Total: 30-40 minutes per application

Setting Up Visual Studio Code with Docker MCP Toolkit

Prerequisites

Before you begin, make sure you have:

A machine with 8 GB RAM minimum (16GB recommended)

The latest Docker Desktop release

VS Code with GitHub Copilot extension

GitHub account with personal access token

Step 1. Enable Docker MCP Toolkit

Open Docker Desktop and enable the MCP Toolkit from Settings.

To enable:

Open Docker Desktop

Go to Settings → Beta Features

Toggle Docker MCP Toolkit ON

Click Apply

Caption: Enabling Docker MCP Toolkit under Docker Desktop

Add Required MCP Servers from CatalogAdd Arm, Sequential Thinking and GitHub Official by following the links below, or by selecting “Catalog” in the Docker Desktop MCP toolkit:

Arm MCP Server – Arm migration tools and architecture expertise

GitHub MCP Server – Repository operations and pull request management

Sequential Thinking MCP Server – Complex problem decomposition and planning

Caption: Searching for Arm MCP Server in the Docker MCP Catalog

Step 2. Configure the Servers

Configure the Arm MCP Server

To access your local code for the migrate-ease scan and MCA tools, the Arm MCP Server needs a directory configured to point to your local code.

Caption: Arm MCP Server configuration

Once you click ‘Save’, the Arm MCP Server will know where to look for your code. If you want to give a different directory access in the future, you’ll need to change this path.

Available Arm Migration Tools

Click Tools to view all the six MCP tools available under Arm MCP Server.

Caption: List of MCP tools provided by the Arm MCP Server

knowledge_base_search – Semantic search of Arm learning resources, intrinsics documentation, and software compatibility

migrate_ease_scan – Code scanner supporting C++, Python, Go, JavaScript, and Java for Arm compatibility analysis

check_image – Docker image architecture verification (checks if images support Arm64)

skopeo – Remote container image inspection without downloading

mca – Machine Code Analyzer for assembly performance analysis and IPC predictions

sysreport_instructions – System architecture information gathering

Configure GitHub MCP Server

The GitHub MCP Server lets GitHub Copilot create pull requests, manage issues, and commit changes.

Caption: Steps to configure GitHub Official MCP Server

Configure Authentication:

Select GitHub official

Choose your preferred authentication method

For Personal Access Token, you’ll need to get the token from GitHub > Settings > Developer Settings

Caption: Setting up Personal Access Token in GitHub MCP Server

Configure Sequential Thinking MCP Server

Click “Sequential Thinking”

No configuration needed

Caption: Sequential MCP Server requires zero configuration

This server helps GitHub Copilot break down complex Arm migration decisions into logical steps.

Step 3. Add the Servers to VS Code

The Docker MCP Toolkit makes it incredibly easy to configure MCP servers for clients like VS Code.

To configure, click “Clients” and scroll down to Visual Studio Code. Click the “Connect” button:

Caption: Setting up Visual Studio Code as MCP Client

Now open VS Code and click on the ‘Extensions’ icon in the left toolbar:

Caption: Configuring MCP_DOCKER under VS Code Extensions

Click the MCP_DOCKER gear, and click ‘Start Server’:

Caption: Starting MCP Server under VS Code

Now you’re ready to perform an Arm migration!

Step 4. Verify Connection

Open GitHub Copilot Chat in VS Code and ask:

What Arm migration tools do you have access to?

You should see tools from all three servers listed. If you see them, your connection works. Let’s migrate some code.

Caption: Playing around with GitHub Co-Pilot

Real-World Demo: Migrating a Legacy x86 Application

Now that you’ve connected GitHub Copilot to Docker MCP Toolkit, let’s migrate that matrix multiplication benchmark we looked at earlier.

Time to migrate: 20 minutesInfrastructure: $0 (all runs in Docker containers)Prerequisites: The code we showed earlier in this post

The Workflow

Docker MCP Toolkit orchestrates the migration through a secure MCP Gateway that routes requests to specialized tools: the Arm MCP Server scans code and converts intrinsics, GitHub MCP Server creates pull requests, and Sequential Thinking plans multi-step migrations. Each tool runs in an isolated Docker container: secure, reproducible, and under your control.

Step 1. Clone the repo

git clone https://github.com/JoeStech/docker-blog-arm-migration

Give GitHub Copilot Migration Instructions

Open your project in VS Code. In GitHub Copilot Chat, paste this prompt:

Your goal is to migrate this codebase from x86 to Arm64. Use the Arm MCP Server tools to help you with this migration.

Steps to follow:
1. Check all Dockerfiles – use check_image and/or skopeo tools to verify Arm compatibility, changing the base image if necessary
2. Scan the codebase – run migrate_ease_scan with the appropriate language scanner and apply the suggested changes
3. Use knowledge_base_search when you need Arm architecture guidance or intrinsic equivalents
4. Update compiler flags and dependencies for Arm64 compatibility
5. **Create a pull request with all changes using GitHub MCP Server**

Important notes:
– Your current working directory is mapped to /workspace on the MCP server
– NEON lane indices must be compile-time constants, not variables
– If you're unsure about Arm equivalents, use knowledge_base_search to find documentation
– Be sure to find out from the user or system what the target machine is, and use the appropriate intrinsics. For instance, if neoverse (Graviton, Axion, Cobalt) is targeted, use the latest SME/SME2.

**After completing the migration:**
– Create a pull request with a detailed description of changes
– Include performance predictions and cost savings in the PR description
– List all tools used and validation steps needed

Step 2. Watch Docker MCP Toolkit Execute

GitHub Copilot orchestrates the migration using Docker MCP Toolkit. Here’s what happens:

Phase 1: Image Analysis

GitHub Copilot starts by analyzing the Dockerfile’s base image using the Arm MCP Server’s skopeo tool.

Caption: GitHub Copilot uses the skopeo tool from the Arm MCP Server to analyze the centos:6 base image. The tool reports that this image has no arm64 build available. This is the first blocker identified – the container won’t even start on Arm hardware.

This immediately identifies that CentOS 6 has no Arm64 builds and must be replaced.

Phase 2: Code Analysis

Next, Copilot runs the migrate_ease_scan tool with the C++ scanner on the codebase.

Caption: The migrate_ease_scan tool analyzes the C++ source code and detects AVX2 intrinsics, the -mavx2 compiler flag, and x86-specific headers. This automated scan identifies all architecture-dependent code that requires conversion – work that could take hours to find manually.

The scan results show exactly what needs to change for Arm compatibility. Each detected issue includes the file location, line number, and specific code that requires modification. This precision eliminates guesswork and ensures nothing is missed.

Phase 3: Arm Optimization and Best Practices

Forx86 intrinsics found in Phase 2, Copilot queries the Arm MCP Server’s knowledge base for Arm equivalents, if needed. It then makes replacements as necessary.

Caption: GitHub Copilot uses the knowledge_base_search tool to find Arm NEON equivalents for each AVX2 intrinsic.

The tool returns official Arm documentation showing the conversions: _mm256_loadu_pd() becomes vld1q_f64(), _mm256_add_pd() becomes vaddq_f64(), and so on. This knowledge comes from learn.arm.com learning paths and intrinsic documentation.

The knowledge base provides not just the conversion mappings, but also architectural context: AVX2’s 256-bit vectors vs NEON’s 128-bit vectors, which means loop adjustments are needed. Copilot uses this information to rewrite the matrix multiplication code correctly.

Phase 4: Create the GitHub PR and Summarize

After completing the migration, Copilot creates a PR in GitHub and summarizes the changes made.

The changes are substantial:

Replaced centos:6 → ubuntu:22.04, added TARGETARCH for multi-arch builds

Added Arm64 detection and -march=armv8-a+simd compiler flag

Converted AVX2 → NEON intrinsics with architecture guards

The build is now simpler, modern, and Arm-compatible.

Phase 5: Checking the Pull Request

You can verify the Pull Request by visiting https://github.com/JoeStech/docker-blog-arm-migration/pull/1/

To verify performance, you can build and run the benchmark:

docker buildx build –platform linux/arm64 -t benchmark:arm64 . –load

docker run –rm benchmark:arm64

Which should output:

SIMD Matrix Operations Benchmark
================================
Running on Arm64 architecture with NEON optimizations
=== Matrix Multiplication Benchmark ===
Matrix size: 200×200
Time: 17 ms
Result sum: 1.98888e+08

Caveats

A very important thing to remember is that not all models will provide equal results, and while the Arm MCP Server provides deterministic context, the models themselves are stochastic. Always use a flagship latest-generation model to get the best results, and test any guesses the model makes regarding performance improvement.

How Docker MCP Toolkit Changes Development

Docker MCP Toolkit changes how developers interact with specialized knowledge and capabilities. Rather than learning new tools, installing dependencies, or managing credentials, developers connect their AI assistant once and immediately access containerized expertise.

The benefits extend beyond Arm migration:

Consistency – Same tools, same results across all developers

Security – Containerized isolation prevents tool interference

Version Control – MCP server versions tracked with application code

Reproducibility – Migrations behave identically across environments

Discoverability – Docker MCP Catalog makes finding the right server straightforward

Most importantly, developers remain in their existing workflow. VS Code. GitHub Copilot. Git. No context switching to external tools or dashboards.

Wrapping Up

You’ve just automated Arm64 migration using Docker MCP Toolkit, the Arm MCP Server, and GitHub Copilot. What used to require architecture expertise, manual intrinsic conversion, and hours of debugging now happens through natural conversation, safely executed in Docker containers.

Ready to try it? Open Docker Desktop and explore the MCP Catalog. Start with the Arm MCP Server, add GitHub, experiment with Sequential Thinking. Each server unlocks new capabilities.

The future of migration isn’t manually porting every application. It’s having an AI assistant that can execute tasks across your entire stack securely, reproducibly, and at the speed of thought.

Learn More

New to Docker? Download Docker Desktop

Explore the MCP Catalog: Discover containerized, security-hardened MCP servers

Get Started with MCP Toolkit: Official Documentation

Quelle: https://blog.docker.com/feed/