Cloud

As a customer of Google Kubernetes Engine (GKE), you’ve selected a container runtime with a high degree of managed operations, encompassing everything from automatic upgrades to effortless node management. This inherent efficiency allows you to focus more on your applications, and less on the underlying infrastructure. In an ideal world, this streamlined experience, coupled with GKE’s robust autoscaling capabilities, ensures perfect workload scheduling all the time. Your applications seamlessly scale up and down, always finding the resources they need, precisely when they need them.

Unfortunately though, the real world presents a few more challenges that need to be addressed. GKE offers powerful four-way autoscaling (Horizontal Pod Autoscaler, Vertical Pod Autoscaler, Cluster Autoscaler, and Node Auto Provisioning) that provides the building blocks to address the scalability needs for workloads and infrastructure. However, running an efficient platform for today’s dynamic workloads involves more than just ensuring scalability. Factors like cost optimization, capacity availability, the speed at which resources can scale, overall performance, and the flexibility of your infrastructure all profoundly affect and constrain how workload scheduling can be effectively planned on GKE. Honestly, it can get a bit cloudy on what is the best strategy and what are the trade offs between these parameters.

In this blog we will focus on specifically the GKE scheduler and the factors that can influence its workload placement decisions when capacity constraints exist. We will explore how to plan and design for these scenarios using various GKE features and workload configurations.

It’s (mostly) a constraint optimization problem

At its core, effective workload scheduling in GKE is not about finding the single best solution, but rather navigating a multi-dimensional constraint optimization problem. For many use cases it is less about overcoming strict limitations and more about finding trade-offs between competing priorities:

• Cost: You want to minimize overall infrastructure spend, optimize utilization, avoid over-provisioning, and leverage cost-effective solutions.
• Performance: You want to ensure the workloads that run on the platform can meet their SLOs according to their relative importance to the business.
• Flexibility and agility: You want to be able to react to changes in demand of your workloads by providing the necessary capacity when needed.

Understanding your individual preferences and tolerances across these dimensions is critical to understanding how to navigate this constraint space and how to design and configure your GKE environment. 

Core building blocks

While not the only factor, autoscaling and its configuration plays a key role in workload scheduling. The configuration of scaling is particular to each environment, and some best practices have been documented. GKE supports autoscaling across four dimensions:

• Horizontal Pod Autoscaler (HPA) – adjusts the number of pod replicas 

• Vertical Pod Autoscaler (VPA) – adjusts pod resource requests based on actual usage

• Cluster Autoscaler (CA) – automatically adjusts the number of nodes 

• Node Auto Provisioner (NAP) – adjusts node pool size based on workload demands 

When capacity is a concern, it’s crucial to understand how much resource your workload requests and consumes. The GKE scheduler relies on the pod resource.request value to make an optimum scheduling decision. If this is not set, this can result in incorrect placement (e.g., on nodes with not enough capacity) and workload instability due to pre-emption. The importance of setting requests is discussed in more detail here. 

Workload scheduling constraint scenarios

What are good options for running an efficient and performant platform when capacity is constrained? 

Let’s take some examples of common scenarios and discuss our options for getting the best result for our workload in terms of cost, performance, and flexibility.

Capacity is fixed or limited – but some high-priority workloads need guaranteed capacity

In this scenario the number of available nodes is considered to be static but the workloads still scale with demand. This creates the need to guarantee resources for critical workloads and explicitly define priority orders.

Solution: Workload priority classes and taints/tolerations

• Priority classes implement a hierarchy of workload importance, where higher priority workloads take precedence over lower ones during scheduling decisions. As shown in the diagram above, under capacity constraints, the scheduler evicts lower priority (blue) workloads to successfully schedule those with a higher priority (red). 

• Taints and tolerations allow capacity targeting by ensuring workloads are not scheduled onto inappropriate nodes. They make sure all the capacity on certain (tainted) nodes (e.g.. with GPUs or SSDs) is only available to specific workloads. Not even workloads with a higher priority class than those with a toleration will be scheduled on the tainted node. 

Applications experience sudden spikes in demand, and workloads need to be scaled quickly without performance degradation / errors

In this scenario we need to quickly schedule workloads on a horizontally scalable cluster. Even though GKE has features like container-optimized compute and image streaming that can drastically reduce provisioning time on new nodes, scheduling pods is still much quicker than scaling nodes. This can lead to resource bottlenecks and a degraded SLO.

Solution: Placeholder pods and scaling profiles

• Placeholder pods, or “balloon” pods, have the effect of holding or reserving spare running capacity in the cluster. When there’s a sudden spike and new pods need to be scheduled, these balloon pods are evicted, releasing capacity and allowing new pods to be scaled rapidly in their place. New nodes are provisioned by the cluster autoscaler to accommodate the evicted placeholder pods, and provide more capacity if needed.

• Auto-scaling profiles configure node scale-down behaviours based on either cost or performance. There are two cluster-based profiles in GKE: balanced and optimize-utilization. The balanced profile scales down nodes in a less aggressive manner compared to the optimize-utilization profile, meaning nodes are available for longer. Any further spikes in demand therefore are not delayed by new node provisioning times.

Workload-specific node scale-down is also available through the use of compute classes (described in more detail below). These allow for node consolidation triggers such as utilization and time delays to customize node lifetimes for different conditions. 

I need to provision additional nodes in my cluster but my preferred node type is not available

In this scenario, we address the need to scale out a cluster without knowing if our required or preferred node type, such as a certain hardware accelerator or spot instance, will be available.

Solution: GKE custom compute classes. 

• These allow you to specify the preferred and fallback nodes that can be used to scale out your clusters. Priorities can be defined for specific node properties like CPU and accelerators, node characteristics (VM family, min CPU/Memory, Spot), or specific instance types (n4-standard-16).
  Compute classes also adopt active migration to top priorities, meaning that workloads will always be reconciled to the highest priority option (e.g. Spot instance) when it becomes available, if it was not available at deployment time.
  For users of resource-based committed use discounts (CUDs), compute classes can be configured in a way that prefers their committed resources before moving to other resources. To allow for full flexibility and between machine families, regions, and even compute platforms, you should also consider moving to flexible CUDs in the future.

My preferred resources are not available in a given region

In this scenario, the capacity required by the workload is very specific and in high demand. There might even be a possibility that the resources cannot be obtained in a region even through compute classes. This is especially important for AI-based workloads that require high-performance infrastructure and GPU or TPU accelerators.

Solution: Multi-Cluster Orchestrator and Multi-Cluster Gateway

• Multi-Cluster Orchestrator is an open-source project whose primary goal is ¨simplifying multi-cluster deployments, optimizing resource utilization and costs, and enhancing workload reliability, scalability, and performance.” Using this technology in GKE, platform engineers can in effect “capacity chase” across Google Cloud regions where a workload´s capacity requirements are matched with the regions where that capacity is available. Multi-Cluster Orchestrator then initiates cluster provisioning in that region to run the workload. 

• Multi-Cluster Gateway is a networking solution for GKE that leverages the Kubernetes Gateway API to manage application traffic across multiple GKE clusters, potentially spanning different regions. It simplifies the complex task of exposing services and balancing workloads in geographically distributed GKE environments

Conclusion and next steps 

GKE offers platform engineers a robust set of tools to optimize resource allocation, even when they face capacity constraints. Effective and holistic capacity planning depends on a clear understanding of the workloads, including their criticality, usage profiles, and capacity requirements. Managing constrained capacity can be a strategic way to control costs, making it crucial to optimize performance under these conditions.

To further enhance your capacity planning consider the following resources:

• Understand GKE cluster and workload signals such as utilisation and rightsizing and how they are important in capacity planning. 

• Monitor scaling events such as failed pod scheduling events and node/pod number changes in the Unschedulable Pods dashboard template. 

• Take a look at the recently released feature — GKE Horizontal Pod Autoscaling Observability Events — which provides the ability to view HPA autoscaler decision events in logs. This can help with the tracking and understanding of scaling event decisions which may influence platform design.

Earlier this year, the Association for Computing Machinery’s Special Interest Group on Management of Data (ACM SIGMOD) announced that Spanner, Google’s globally distributed database, was awarded the 2025 SIGMOD Systems Award. The SIGMOD Systems Award specifically honors systems whose technical contributions have profoundly impacted the theory or practice of large-scale data management. On behalf of the entire Spanner team, especially the engineers who were there at the beginning of Spanner’s journey, it is with deep humility and immense pride that we receive this recognition from such a distinguished community. We’re thrilled to be participating in the 2025 SIGMOD conference from June 22-27 in Berlin, Germany as a Platinum sponsor.

This honor feels particularly significant following Spanner’s 2022 SIGOPS Hall of Fame Award, which highlighted the crucial role of technologies like TrueTime and our network infrastructure, reaffirming the lasting significance of the original vision laid out in the first Spanner paper.

Spanner’s core innovation: TrueTime and external consistency

For Spanner to be recognized in this way is a powerful affirmation of the vision we set out to achieve years ago and the new ways Spanner enables applications to be built. According to the award citation, Spanner is recognized “for reimagining relational data management to enable serializability with external consistency at global scale.”

Why “reimagining”? Before Spanner, databases offered a stark choice: you could have ACID transactions and SQL, or you could have scale and multi-datacenter reliability. Scale and availability required a distributed system, and that meant eventual consistency and other forms of best-effort synchronization. Spanner showed that this choice was not fundamental — that it was possible to build a database that offered the horizontal scalability of a distributed system with the power and ease of use of transactions and SQL. It enabled companies for whom scale is job #1 to regain developer velocity and agility. Spanner drastically simplifies the logic required in distributed applications. Developers can reason about the state of the database as if it were a single, consistent entity, even when it spans the globe.

The key enabler for Spanner’s ability to deliver external consistency is TrueTime. Beyond just a synchronized global clock, TrueTime is an API that cleverly exposes clock uncertainty as a bounded interval, which allows higher-level algorithms to reason about the ordering of events. Google’s TrueTime implementation uses specialized hardware references like GPS receivers and atomic clocks to provide highly trustworthy and very tight time bounds. Spanner leverages this bounded uncertainty to achieve external consistency. When a transaction commits, Spanner assigns it a commit timestamp derived from TrueTime. Spanner then enforces a “commit wait” — which can be overlapped with making the transaction durable – to ensure that the commit timestamp is definitively in the past before making the transaction’s effects visible. This ensures that the assigned commit timestamps definitively reflect the true global serialization order of transactions, even across data centers. The result is remarkable: external consistency with no performance cost.

 Addressing the consistency-scale dilemma

To truly appreciate the journey, it helps to travel back to the early and mid-2000s at Google. The internet was exploding, and our biggest challenge was scaling our software infrastructure to keep pace. We needed databases that could store and process a copy of the internet using vast fleets of commodity servers. This spurred the development of internal systems that delivered incredible performance and scalability, but they came with trade-offs.

As we gained more familiarity with these systems, and started using them to build big interactive applications like Gmail, we consistently heard from internal developers about the challenge of working with eventual consistency and cross-shard synchronization, as well as the friction of modeling every problem (no matter how complex) as key-value pairs. It quickly became apparent that we needed to build a globally distributed database that offered the familiarity and guarantees of traditional relational databases — including ACID transactions, serializability, and external consistency — without giving up Google’s ever-growing need for bigger databases serving bigger audiences. Moreover, working closely with our customers, it became clear that actually, it was something we could build. The rest is history!

Spanner as a cloud service

As a cornerstone of Google’s infrastructure, Spanner powers some of our most critical, planet-scale services, including Google Ads, Google Search indexing, Gmail, YouTube, Google Photos, metadata for Cloud Storage, and BigQuery, demonstrating its robustness and scalability under extreme load.

The next logical step was to make these capabilities available externally to customers through Google Cloud. The launch and subsequent evolution of  Spanner aimed to democratize this technology, bringing the power of a globally consistent, scalable database to organizations of all sizes, from startups to global enterprises, simplifying their application development and operations.

Spanner’s core value proposition for customers stems directly from its unique architecture:

• Global scale with strong consistency: Spanner delivers on the original promise: ACID transactions with external consistency across a database that can scale horizontally across regions and continents, automatically managing data distribution (sharding) as needed. This directly addresses the capability highlighted by the SIGMOD award.  

• Unmatched availability: Leveraging synchronous, Paxos-based replication across multiple zones or regions, Spanner offers an industry-leading 99.999% availability Service Level Agreement (SLA) for multi-region configurations. This helps provide extreme fault tolerance and minimizes downtime risk for mission-critical applications.  

• Simplified operations: As a fully managed service, Spanner automates complex operational tasks like sharding, replication management, backups, and maintenance. This frees development teams from significant operational burdens, allowing them to focus on building applications rather than managing database infrastructure. This contrasts sharply with the manual effort often required for traditional sharded databases or the complexity of implementing consistency logic at the application layer for NoSQL systems.  

• Developer productivity: Spanner offers familiar SQL query interfaces, supporting both Google SQL and PostgreSQL dialects, which significantly flattens the learning curve for developers. Furthermore, its strong consistency eliminates entire classes of complex problems related to data synchronization and reconciliation that often plague applications built on eventually consistent systems.  

Empowering customers and industries

Of course, today’s data landscape has changed since we published the first Spanner paper in 2012. In the modern AI-first data world, we see customers more focused than ever on getting full value from their data, which is often spread out over many systems with different data models, varying scalability, and uneven reliability. We’re addressing these new challenges head on, introducing Spanner Graph, vector search for AI applications, and integrated full-text search. These allow you to bring together a wide range of data and iterate on it rapidly within a single, consistent, scalable platform. We’ve also helped you run even more cost-effectively as your data grows, with increased compute and storage density, tiered pricing through editions, and cost-optimizing features such as tiered storage. Finally, we’ve made it easier to bring scale-out workloads into Spanner through enhanced interoperability with tools and capabilities, such as  Cassandra-compatible APIs.

While academic recognition such as the SIGMOD award is gratifying, we’ve always felt that the true measure of a system’s impact lies in how it empowers users to solve real-world problems and build innovative applications. Spanner’s unique combination of capabilities have proven transformative across various industries.

In financial services, where consistency, availability, and security are paramount, Spanner provides the foundation for many critical systems. Companies like Goldman Sachs use it to consolidate trade ledgers, while others like Arigato Bank rely on it to handle high volumes of financial transactions with perfect consistency, even during peak loads. Digital-native banks like Minna Bank have built their entire infrastructure on Spanner, leveraging its availability and consistency to meet stringent regulatory requirements and customer expectations.

The gaming industry is constantly pushing the boundaries of scale and real-time interaction. Spanner has helped game developers launch globally successful titles like Dragon Quest Walk by Colopl, handling millions of concurrent players from day one. Its ability to manage player profiles, in-game inventory, and leaderboards consistently across a global player base, while elastically scaling to handle unpredictable traffic spikes, has been crucial for delivering a seamless player experience

In retail and e-commerce, Spanner helps businesses manage the complexities of modern commerce. Walmart uses Spanner to modernize its inventory and payment management, providing a real-time, consistent view across online and physical stores. MercadoLibre, a global online marketplace and e-commerce provider, leverages Spanner to handle the global needs of their customers, including massive demand spikes during major product launches.

Leaders in transportation, such as Uber, rely on Spanner to handle millions of concurrent users and billions of trips per month across over 10,000 cities and billions of database transactions per day.

These examples illustrate that for many modern applications, Spanner’s specific blend of global consistency, massive scalability, high availability and interoperable multi-model isn’t just a technical advantage; it’s a fundamental enabler. Business models built around real-time global inventory, instantaneously consistent financial records, or seamless worldwide multiplayer experiences become significantly simpler, less risky, and more feasible to implement with Spanner.

The future with Spanner

The SIGMOD award recognizes the contributions of over 30 individuals, and countless other current and prior Googlers who have played roles throughout its development and evolution. It’s been a privilege to witness the journey from the initial ambitious concepts to the globally impactful system it is today. The journey from the original OSDI paper published to this 2025 SIGMOD Systems Award highlights over a decade of sustained research, engineering, and investment by Google. This long-term commitment is rare and is a key factor behind Spanner’s enduring success and impact.

Thanks to you, our customers, Spanner remains a living system that continues to evolve, benefiting from ongoing improvements, driven by both internal use and the needs of our cloud customers. Looking ahead, we remain committed to pushing the boundaries of what’s possible with distributed databases. We’re excited about enabling new kinds of applications, including those leveraging AI, and continuing our mission to simplify the complexities of data management for developers building the next generation of world-changing applications.

Experience the Spanner difference 

The 2025 ACM SIGMOD Systems Award is a tremendous honor and a validation of the path we embarked on years ago. If you are at the  2025 SIGMOD conference, join us on Tuesday, June 24 at 4:30 pm to hear from and meet with the Google team. 

If Spanner’s capabilities for consistency, scale, and availability resonate with the challenges you face, we encourage you to learn more:

• Explore Spanner to learn about supported features and use cases, and get started with a 90-day Spanner free trial instance.

• Dive deeper with documentation  

• For the academically inclined, read the original and successor papers on Spanner  

In closing, we believe Spanner unlocks new possibilities for building reliable, scalable, and globally consistent applications, and we are excited to see what you, our customers, will build with it.

Today, we’re incredibly proud to announce the graduation of the latest cohort from the Google for Startups Accelerator: AI First from Europe & Israel! This milestone marks the culmination of an intensive three-months journey for these 14 innovative startups, who’ve dedicated themselves to growing their businesses and pushing the boundaries of artificial intelligence. The hybrid program offered expert mentorship, robust technical support, and access to a powerful global network, empowering founders to scale their impact. 

“With Google’s support, we brought our AI recruitment platform into its next generation — the most advanced in the world, with a business model built for $7M+ ARR within a year.  Their guidance and exposure to breakthrough models took our tech years ahead.“ – Shira Spetter, CEO, iVERSE

With AI projected to contribute a staggering $15.7 trillion to the global economy by 2030 (PwC), supporting these AI startups is crucial for accelerating groundbreaking innovations, deploying scalable solutions, and ensuring AI’s benefits are widely accessible to businesses and communities worldwide. Because when AI innovation thrives, the world moves forward.

“We were initially cautious about deploying LLMs in production for key functionalities. However, after experiencing Gemini 2.5, we’re not just convinced – we’re actively integrating it to power exciting new features.” – Maria Fe Paz, CEO and founder of Connect by Circular-Lab.

The cohort celebrated the milestone at Viva Technology in Paris where they presented their companies, met potential venture capitalists and had an intimate fireside chat with Joëlle Barral, Research & Engineering Senior Director at Google DeepMind and Arno Amabile, Advisor to the French President Special Envoy for AI.

Learn more about the graduating startups and their inspiring work:

1. Ambr AI (UK) helps professionals master difficult workplace conversations through realistic Voice AI practice simulations, providing a safe, convenient environment with instant feedback to build crucial communication skills.

2. Connect by Circular-Lab (Spain) uses AI/ML to structure and centralize diagnostic data, making it accessible for labs, hospitals, and industry stakeholders.

3. Folio (Israel) empowers industrial sales and application engineers by turning technical specs, configuration data, and application info into instant answers, recommendations, and agentic workflows, speeding work, cutting errors, and boosting revenue for industrial manufacturers and distributors.

4. Good With (UK) delivers ai-driven financial behaviour analytics for real-time credit risk assessment, enabling lenders to convert binary ‘Yes/No’ decisioning into a ‘Safe Journey to Yes’ which increases ‘good’ customer acquisition and reduces loss.

5. Hybr (UK) is a SaaS enabled lettings platform for letting agents cut workloads by up to 80%, turning leads into lets faster, and building transparency into the rental process.

6. iVerse (Israel) is an AI talent platform built on 50+ years of occupational psychology — combining real-time behavioral analysis, proprietary evaluation layers, and a scalable model to match top AI talent with top AI opportunities worldwide.

7. Material Evolution (UK) is transforming cement with a novel AI-driven tech that uses industrial waste that requires no heat and significantly reduces carbon emissions.

8. Metsystem (Denmark) develops an AI powered metastasis-targeting platform to predict personalized cancer treatments and help pharmaceutical companies stratify patients for drug trials.  

9. Noxon (Germany) builds wearable Muscle-Computer Interface (MCI) that makes muscle diagnostics and therapies more accessible, user-friendly, and scalable for remote care.

10. Punto Health (UK/ Spain) is transforming dementia care with an AI-powered platform that delivers continuous, personalised support for patients and carers, while improving monitoring and coordination for providers.

11. ShareID (France) enables real-time, privacy-first identity verification without storing personal or biometric data, redefining digital trust.

12. Tech1M (UK) is an intelligent recruitment engine with AI Agents for sourcing, screening, interviewing and hiring talents anywhere in the world.

13. V-Art (Ukraine) is a DeepTech startup streamlining IP monetization for brands and AI with a solution to manage and license any digital content at scale.

14. Whering (UK) is a digital wardrobe & AI styling app that allows users to unlock infinite outfit combinations from the clothes they already own.

We can’t wait to see what comes next for these AI-first solutions and incredible teams driving them. Learn more about Google for Startups Accelerator programs on startup.google.com.

The momentum of the Gemini 2.5 era continues to build. Following our recent announcements, we’re empowering enterprise builders and developers with even greater access to the intelligence, and flexibility of our most capable models yet, directly within Vertex AI, our unified platform for enterprise-scale AI development.

The significant updates announced today are designed to help your organization build sophisticated, customized, and efficient AI solutions, more confidently. These include: 

• Gemini 2.5 Flash and 2.5 Pro now generally available: Our most intelligent models for speed and advanced reasoning are production-ready providing organizations with the stability, reliability and scalability needed to confidently deploy the most advanced AI capabilities into mission-critical applications. 

• New Gemini 2.5 Flash-Lite in public preview: Experience the cost-efficient Gemini 2.5 model yet with optimized performance for high-volume tasks.

• New Supervised Fine-Tuning (SFT) for Gemini 2.5 Flash is generally available: Tailor our high-speed model to your unique enterprise data and needs.

• New updated Live API with native audio in public preview: Streamline the development of complex, real-time audio AI systems.

Build with confidence using production-ready Gemini 2.5

Gemini 2.5 Flash: Optimized for speed, efficiency, and scale

Gemini 2.5 Flash, is now generally available in Vertex AI, the Gemini API, and Google AI Studio, engineered for high-throughput enterprise tasks such as large-scale summarization, responsive chat applications, and efficient data extraction. These advancements provide a comprehensive toolkit to elevate your enterprise applications and unlock new levels of productivity and innovation. Build with confidence on this production-ready foundation. 

“SmartBear uses AI to power Test Hub, its solution for building and executing regression tests for web, desktop, and mobile. With Gemini 2.5 Flash on Vertex AI, we can accelerate tasks like translating extensive manual test scripts into robust automated tests with remarkable speed and cost-effectiveness. The ROI is multifaceted: we’re empowering our customers to realize the benefits of automation execution, while simultaneously producing intent-based, resilient-to-change test plans. This drastically increases testing velocity and enables faster feature delivery—helping our customers move with greater speed and confidence, powered by a more efficient and scalable AI foundation.”
– Fitz Nowlan, PhD, VP of AI, SmartBear

“At Connective Health, our mission is empowering healthcare providers and driving better patient outcomes. Gemini 2.5 Flash on Vertex AI is instrumental in helping us extract vital medical records from complex free-text records. Customer trust is paramount, so our AI initiatives are always developed in close collaboration with healthcare providers, ensuring its use is accurate and impactful. The rapid advancements in Gemini’s capabilities allow us to continually enhance how we deliver these critical insights, and we’re excited to explore further applications to improve the lives of more patients and providers.”
– Joe Athman, CTO, Connective Health

“At Suggestic, we’re advancing the future of personalized nutrition by making nutritional data instantly actionable through our next-generation, image-based inference API. By leveraging Gemini 2.5 Flash as our core model, we’ve consistently achieved exceptional accuracy and processing efficiency, significantly outperforming alternative models on the Nutrition5k dataset. Gemini 2.5 Flash delivered a remarkable 25% improvement across critical benchmarks, including processing speed, enabling us to implement advanced image modification tools that enhance inference accuracy without sacrificing response times. Its native support for structured output and unparalleled capability in handling complex, tool-augmented tasks ensures seamless, real-time experiences, making Gemini 2.5 Flash the optimal choice for robust, production-grade solutions.”
– Shai Rozen, Co-founder, Suggestic

Gemini 2.5 Pro: Unlock state-of-the-art intelligence

Our most capable model, Gemini 2.5 Pro, is also now generally available in Vertex AI, the Gemini API, and Google AI Studio. Designed for your most demanding enterprise AI challenges like making sense of massive datasets for scientific discovery or accelerating migration of critical legacy code, it excels at highly complex reasoning, advanced code generation, and deep multimodal understanding.

“At Snap, we believe today’s devices and user interfaces can constrain the full potential of AI. So, we’re bringing AI into the world through Spectacles, our standalone, see-through, immersive AR glasses, and Gemini on Google Cloud. Through the powerful combination of our Depth Module API and Gemini 2.5 Pro, it’s already possible to translate 2D coordinates of an image into 3D space, enabling information and annotations to be anchored on the real world – even as you move around. We’re excited to unlock a whole new paradigm for spatial intelligence on Spectacles.”
– Terek Judi, Staff Product Manager, Snap Inc.

“At Multimodal, we’re reimagining how business and IT teams in finance and insurance co-create intelligent agentic workflows. By integrating Gemini 2.5 Pro into our AgentFlow platform, we’ve transformed how customers experience Zero Shot AI—enabling them to instantly see how AI agents operate on their own documents, workflows, and use cases, without needing lengthy pilots or custom demos. Gemini 2.5’s large context window and structured reasoning unlock a level of depth and adaptability that’s been impossible before, allowing our agents to understand, reason through, and act across highly specific domain workflows. This fundamentally changes the go-to-market experience: business teams can now visualize and validate impact on day one. For industries where trust, compliance, and precision are paramount, that’s a game-changer.”
– Andrew McKishnie, VP of Engineering, Multimodal

Enhanced customization and efficiency for your needs

Gemini 2.5 Flash-Lite in public preview: Gain cost-efficiency with low latency
Get an early look at Gemini 2.5 Flash-Lite, the most cost-effective Gemini 2.5 model yet, optimized for performance in high-volume workloads. Delivering higher performance than the previous Flash-Lite model, 2.5 Flash-Lite is 1.5 times faster than 2.0 Flash, at a lower cost, on Vertex AI. It’s ideal for tasks like classification, translation, intelligent routing, and other cost-sensitive, high-scale operations.

Supervised Fine-Tuning (SFT) for Gemini 2.5 Flash: Customized AI for your business
Achieve unparalleled customization with the GA release of Supervised Fine-Tuning (SFT) for Gemini 2.5 Flash on Vertex AI. Adapt Gemini to your enterprise’s specific datasets, industry-specific terminology, and unique brand voice, leading to higher accuracy on specialized tasks.

Live API with native audio in public preview: Build real-time interactive services 
Streamline the development of sophisticated, real-time AI systems with the Live API, now in public preview with native audio-to-audio capabilities. This enables more natural and responsive voice-driven applications and complex AI agent interactions.

“Newo.ai enables small and medium businesses to deploy fully functional AI receptionists that handle all incoming communication channels—voice and text—in just 3 minutes with one click. We’ve worked through thousands of customer scenarios to enable AI Employee creation using only a Google Maps listing or website. While this appears simple, we deliver sophisticated conversation flows requiring advanced reasoning, low latency, multilingual capabilities, and empathetic responses—features powered by the Live API and Gemini 2.5 Flash on Vertex AI. This combination allows us to deliver production-ready AI employees that generate up to 30x ROI for our clients.”
– David Yang, Co-founder, Newo.ai

Driving your enterprise AI initiatives forward, these comprehensive Vertex AI updates enable you to continue to scale confidently with robust, production-grade models. You can now tailor powerful AI precisely to your unique operational needs and data, optimize for cost-efficiency in high-throughput scenarios, and build next-generation, interconnected AI solutions that push the boundaries of innovation.

“At Citizen Health, we develop AI advocates that empower rare‑disease patients/caretakers to understand and navigate their healthcare journeys. Our data pipelines stream longitudinal EHR data – decades of clinician notes, imaging reports, and genomic panels – directly into Gemini  2.5 Pro’s million‑token context windows, enabling patients and caretakers to receive concise, context‑rich answers in near real-time. We orchestrate Gemini 2.5 Flash and Gemini 2.5 Pro models within a LangGraph‑powered multi‑agent framework, ensuring the most relevant evidence reaches patients and caretakers without hallucinations. Gemini’s long‑context comprehension coupled with rapid inference converts exhaustive document review into a seamless conversation, allowing families to spend less time deciphering records and more time making informed care decisions.”
– Daniel Wang, CTO, Citizen Health

Pricing and availability
The Gemini 2.5 family of models offers a range of options to meet diverse enterprise needs. With Gemini 2.5 Flash moving to general availability, its pricing has been updated to reflect its improved quality and comprehensive capabilities. We are also introducing preview pricing for Gemini 2.5 Flash-Lite, our most cost efficient Gemini 2.5 model yet. For complete details on pricing for Gemini 2.5 Flash, Gemini 2.5 Pro, and the Gemini 2.5 Flash-Lite preview, please visit our pricing page.

Start moving to production today with Gemini 2.5 Flash and Gemini 2.5 Pro, now generally available on Vertex AI.

Integrating context from tools and data sources into LLMs can be challenging, which impacts ease-of-use in the development of AI agents. To address this challenge, Anthropic introduced the Model Context Protocol (MCP), which standardizes how applications provide context to LLMs. Imagine you want to build an MCP server for your API to make it available to fellow developers so they can use it as context in their own AI applications. But where do you deploy it? Google Cloud Run could be a great option.

Drawing directly from the official Cloud Run documentation for hosting MCP servers, this guide shows you the straightforward process of setting up your very own remote MCP server. Get ready to transform how you leverage context in your AI endeavors!

MCP Transports

MCP follows a client-server architecture, and for a while, only supported running the server locally using the stdio transport.

MCP has evolved and now supports remote access transports: streamable-http and sse. Server-Sent Events (SSE) has been deprecated in favor of Streamable HTTP in the latest MCP specification but is still supported for backwards compatibility. Both of these two transports allow for running MCP servers remotely.

With Streamable HTTP, the server operates as an independent process that can handle multiple client connections. This transport uses HTTP POST and GET requests.

The server MUST provide a single HTTP endpoint path (hereafter referred to as the MCP endpoint) that supports both POST and GET methods. For example, this could be a URL like https://example.com/mcp.

You can read more about the different transports in the official MCP docs.

Benefits of running an MCP server remotely

Running an MCP server remotely on Cloud Run can provide several benefits:

• Scalability: Cloud Run is built to rapidly scale out to handle all incoming requests. Cloud Run will scale your MCP server automatically based on demand.

• Centralized server: You can share access to a centralized MCP server with team members through IAM privileges, allowing them to connect to it from their local machines instead of all running their own servers locally. If a change is made to the MCP server, all team members will benefit from it.

• Security: Cloud Run provides an easy way to force authenticated requests. This allows only secure connections to your MCP server, preventing unauthorized access.

IMPORTANT: The security benefit is critical. If you don’t enforce authentication, anyone on the public internet can potentially access and call your MCP server. 

Prerequisites

• Python 3.10+

• Uv (for package and project management, see docs for installation)

• Google Cloud SDK (gcloud)

Installation

Create a folder, mcp-on-cloudrun, to store the code for our server and deployment:

Let’s get started by using uv to create a project. Uv is a powerful and fast package and project manager.

After running the above command, you should see the following pyproject.toml:

Next, let’s create the additional files we will need: a server.py for our MCP server code, a test_server.py that we will use to test our remote server, and a Dockerfile for our Cloud Run deployment.

Our file structure should now be complete:

Now that we have our file structure taken care of, let’s configure our Google Cloud credentials and set our project:

Math MCP Server

LLMs are great at non-deterministic tasks: understanding intent, generating creative text, summarizing complex ideas, and reasoning about abstract concepts. However, they are notoriously unreliable for deterministic tasks  –  things that have one, and only one, correct answer.

Enabling LLMs with deterministic tools (such as math operations) is one example of how tools can provide valuable context to improve the use of LLMs using MCP.

We will use FastMCP to create a simple math MCP server that has two tools: add and subtract. FastMCP provides a fast, Pythonic way to build MCP servers and clients.

Add FastMCP as a dependency to our pyproject.toml:

Copy and paste the following code into server.py for our math MCP server:

Transport

We are using the streamable-http transport for this example as it is the recommended transport for remote servers, but you can also still use sse if you prefer as it is backwards compatible.

If you want to use sse, you will need to update the last line of server.py to use transport="sse".

Deploying to Cloud Run

Now let’s deploy our simple MCP server to Cloud Run. 

Copy and paste the below code into our empty Dockerfile; it uses uv to run our server.py:

You can deploy directly from source, or by using a container image.

For both options we will use the --no-allow-unauthenticated flag to require authentication.

This is important for security reasons. If you don’t require authentication, anyone can call your MCP server and potentially cause damage to your system.

Option 1 – Deploy from source 

Option 2 – Deploy from a container image 

Create an Artifact Registry repository to store the container image.

Build the container image and push it to Artifact Registry with Cloud Build.

Deploy our MCP server container image to Cloud Run.

Once you have completed either option, if your service has successfully deployed you will see a message like the following:

Authenticating MCP Clients

Since we specified --no-allow-unauthenticated to require authentication, any MCP client connecting to our remote MCP server will need to authenticate.

The official docs for Host MCP servers on Cloud Run provides more information on this topic depending on where you are running your MCP client.

For this example, we will run the Cloud Run proxy to create an authenticated tunnel to our remote MCP server on our local machines.

By default, the URL of Cloud Run services requires all requests to be authorized with the Cloud Run Invoker (roles/run.invoker) IAM role. This IAM policy binding ensures that a strong security mechanism is used to authenticate your local MCP client.

Make sure that you or any team members trying to access the remote MCP server have the roles/run.invoker IAM role bound to their IAM principal (Google Cloud account).

NOTE: The following command may prompt you to download the Cloud Run proxy if it is not already installed. Follow the prompts to download and install it.

You should see the following output:

All traffic to http://127.0.0.1:8080 will now be authenticated and forwarded to our remote MCP server.

Testing the remote MCP server

Let’s test and connect to the remote MCP server using the FastMCP client to connect to http://127.0.0.1:8080/mcp (note the /mcp at the end as we are using the Streamable HTTP transport) and call the add and subtract tools. 

Add the following code to the empty test_server.py file:

NOTE: Make sure you have the Cloud Run proxy running before running the test server.

In a new terminal run:

You should see the following output:

You’ve done it! You have successfully deployed a remote MCP server to Cloud Run and tested it using the FastMCP client.

Want to learn more about deploying AI applications on Cloud Run? Check out this blog from Google I/O to learn the latest on Easily Deploying AI Apps to Cloud Run!

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• Host MCP servers on Cloud Run

• MCP server for helping deploy applications to Cloud Run

We’re excited to announce the general availability of our next-generation C4D virtual machine family. Powered by 5th Gen AMD EPYC processors (Turin) paired with Google Titanium’s latest advancements, C4D provides customers with meaningful performance improvements — up to 80% higher throughput for web serving and 30% better performance for general computing workloads compared to the previous generation. This improvement in performance enables you to maximize your cloud investment and achieve more with fewer resources. 

Beyond raw performance, C4D supports key enterprise capabilities including our first AMD-based Bare Metal instances offering direct access to all the resources on the server for maximum control and performance (bare metal will be available in the coming weeks); and the next-gen Titanium Local SSD, which enhances I/O-intensive operations with up to 35% lower latency vs. the prior generation. These hardware advancements are paired with enterprise-grade security and reliability, featuring a 30-day uptime window between planned maintenance events. With this combination of peak performance, expanded capabilities and enterprise-grade controls, C4D is suited for a wide range of general-purpose computing workloads, from databases, AI inference, web, application and game servers, to mission-critical business applications.

Supercharge your workloads while optimizing costs

General computing and web serving workloads

For general-purpose computing workloads, C4D VMs deliver up to 25% better performance per dollar than C3D, based on estimated SPECrate®2017_int_base benchmark results. C4D also delivers up to 20% better performance per dollar than comparable, generally available offerings from other cloud providers on the same benchmark. This helps you meet demanding performance requirements for a wide range of workloads — including web servers, game, ad and application servers, and containerized microservices — while optimizing resource usage and reducing costs. 

For web-serving workloads, C4D leverages AMD Turin’s improved L3 cache efficiency and next-generation branch prediction to deliver higher throughput/vcpu, resulting in up to 75% increase in price-performance compared to the previous-generation C3D. This drives faster page rendering and a smoother end-user experience, with up to 45% higher price-performance compared to competitive offerings. 

“AppLovin, a global leader in mobile advertising, is constantly looking for cutting-edge infrastructure innovations to deliver exceptional performance for our clients. Google Cloud’s C4D VMs enable us to do just that — driving up to 40% improvement over the prior generation, which leads to significant efficiency gains and latency reduction.” – Basil Shikin, Chief Technology Officer, AppLovin

“On C4D, our ad servers perform 191% faster than N2D, and 81% faster than C3D to serve the same amount of ad-requests. This improved performance comes at a lower overall cost because we can run a smaller number of more efficient nodes, but not only that, for us more performance/less latency means not only savings but more revenue since the fill-rate for ads (successful bid/ask matching) grows exponentially.” – Pablo Loschi, Principal Systems Engineer, Verve Group

“C4D Performance was impressive — most workloads, including page rendering & video processing, were 40+% faster than the previous generation. This kind of improvement makes a real difference to users of platforms like SpareRoom.” – Dimitrios Kechagias, Principal Developer, Cloud Infrastructure Optimisation Lead, SpareRoom

Databases and data-intensive applications

The C4D family is purpose-built for data-intensive applications such as databases and data analytics, by offering the latest generation compute and advanced storage capabilities. C4D’s high core frequency of up to 4.1Ghz and improved Instructions Per Clock (IPC) accelerate transactional workloads such as MySQL by up to 55% versus the prior generation with faster, more efficient query processing. For applications that require large datasets in memory, C4D provides VM sizes scaling up to 384vcpu and 3TB of high-bandwidth DDR5 memory. To scale database I/O performance, customers benefit from the integrated Hyperdisk Extreme storage with up to 500k IOPS and the new Titanium Local SSD that reduces read latency by 35% compared to the prior generation. Together, these capabilities increase performance and responsiveness for your mission-critical databases, delivering up to 35% better price-performance for Redis and MySQL workloads than comparable generally available VMs from other hyperscalers.

“We are constantly looking for advanced computing options to improve experience for the players. With Google’s new C4D VMs, we see drastically improved performance for our observability stack which handles 50k inserts/sec concurrently. Compared to C3D, we were able to cut our resource footprint by half, while reducing CPU load by 35% and seeing a 30% improvement in indexing latency. We look forward to adopting C4D at scale.” – Grzegorz Dlugolecki, Principal Cloud & Kubernetes Engineer, Chess.com

“Across over 100 benchmarks, going from the C3D to C4D yielded 1.7x the performance! This is a heck of a generational improvement for Google Cloud or any public cloud provider for that matter. C4D performance is extremely compelling and opens up a lot of new compute possibilities in the public cloud.” – Michael Larabel, Founder and Principal Author, Phoronix (Read the full study here)

AI inference and complex computations
C4D’s processor offers full support for AVX-512 with a 512 bit datapath, a 50% increase in memory channels, and higher IPC compared to the prior generation. This provides significant improvements for compute-heavy tasks such as CPU-based inference, matrix operations, financial modeling and simulations, and analytics. For recommendation inference, C4D VMs demonstrate an up to 75% price-performance uplift compared to C3D and up to 35% better price-performance versus the comparable competitive offerings from leading hyperscalers, to accelerate the time to results and reduce TCO.

“Silk has tested C4D, and found it to deliver a dramatic increase of up to 40% in performance compared to the previous generation, C3D, enabling our customers to enjoy significant gains in efficiency and agility of their mission critical workloads, over the transactional, analytics and AI use cases.” – Adik Sokolovski, Chief R&D Officer, Silk

Security, maintenance controls and shapes   

With Titanium, C4D offers improved infrastructure performance, lifecycle management, reliability, and security. Storage and network management is offloaded to the Titanium adapter, reserving the host resources for running customer workloads. 

Titanium also enables our first AMD-based Bare Metal instances, which provide direct access to server resources. Bare metal instances are ideal for workloads that require low-level system access — like custom hypervisors, container platforms, or applications with specialized performance or licensing needs. Sectors such as financial services, security, and private cloud platforms will particularly benefit from C4D Bare Metal offerings.

C4D VMs support Hyperdisk, Google Cloud’s workload-optimized block storage. Designed for high performance and scalability, Hyperdisk is cost-efficient, easy to manage at scale, and enterprise-ready. C4D VMs are compatible with Hyperdisk Balanced and Extreme, supporting up to 512 TiB of capacity per instance. With up to 320K IOPS per instance, Hyperdisk Balanced offers an optimal mix of performance and cost-efficiency, for a broad range of workloads. Hyperdisk Extreme delivers ultra-low latency and supports up to 500K IOPS and 10,000 MiB/s throughput per instance — making it well-suited for demanding workloads like databases and caching layers. With real-time tuning of IOPS and bandwidth, Hyperdisk helps ensure your applications always have the storage resources they need.

To enhance security, C4D VMs support confidential computing with AMD Secure Encrypted Virtualization (AMD SEV), utilizing hardware-based memory encryption to help protect your data and applications while in use. This makes C4D an excellent choice for sensitive data, privileged information, PII, and workloads subject to data privacy regulations and compliance requirements.

Experience C4D today

C4D delivers exceptional performance, scalability, and efficiency for today’s most demanding workloads. Powered by next-gen AMD processors, Titanium infrastructure, and Hyperdisk storage, C4D delivers the performance and capabilities needed to make the most of your cloud resources. Whether you’re just getting started with Compute Engine or planning to upgrade from previous generations, C4D offers a clear path to greater efficiency and performance. C4D is now available in 12 regions and 28 zones — check regional availability on our Regions and Zones page and deploy your first instance in the Google Cloud console or with Google Kubernetes Engine.