Scale-out workloads demand the best combination of performance and price to bring down the cost of delivering applications, all while providing an excellent user experience. We are excited to announce a new virtual machine (VM) family, Tau VMs, coming to Google Cloud. Tau VMs extend Compute Engine’s VM offerings with a new option optimized for cost-effective performance of scale-out workloads. 

T2D, the first instance type in the Tau VM family, is based on 3rd Gen AMD EPYC^TM processors and leapfrogs the VMs for scale-out workloads of any leading public cloud provider available today, both in terms of performance and workload total cost of ownership (TCO). The x86 compatibility provided by these AMD EPYC processor-based VMs gives you market-leading performance improvements and cost savings, without having to port your applications to a new processor architecture. 

As illustrated below, Tau VMs offer 56% higher absolute performance and 42% higher price-performance (est. SPECrate2017_int_base) compared to general-purpose VMs from any of the leading public cloud vendors.

What our customers and partners are saying

Snap
“At Snap, it is critical for our business to continue improving our scale-out compute infrastructure for key Snapchat capabilities like AR, Lenses, Spotlight and Maps,” said Cody Powell, Senior Engineering Manager, Snap Inc. “We were impressed when we tested Google Cloud’s new Tau VMs with Google Kubernetes Engine. While it’s early days, we believe we can gain double digits in infrastructure performance improvements for key workloads—enabling us to do more with less and invest even more in new features for our amazing Snapchat community.”

Twitter
“High performance at the right price point is a critical consideration as we work to serve the global public conversation,” said Nick Tornow, Platform Lead, Twitter. “We are excited by initial tests that show potential for double digit performance improvement. We are collaborating with Google Cloud to more deeply evaluate benefits on price and performance for specific compute workloads that we can realize through use of the new Tau VM family.”

DoiT
“DoiT partners with leading cloud vendors who are focused on growth and cost optimization,” said Yoav Toussia-Cohen, CEO, DoiT International. “In our preliminary testing of Google’s new Tau VMs with the Coremark benchmark, we were thrilled to see the incredible performance at 50% better than a comparable ARM-based offering from another leading public cloud. With Tau VMs, Google Cloud has set a new bar for price-performance, making the cloud even more accessible to digital-native companies. We are excited to bring Google’s Tau VMs to our joint customers.”

Designed for demanding scale-out workloads 

Tau VMs bring the benefit of Google’s long-standing experience engineering platforms for scale-out workloads to our customers. They come in multiple predefined VM shapes, with up to 60vCPUs per VM, and 4GB of memory per vCPU. They offer up to 32 Gbps networking bandwidth and a wide range of network attached storage options, making Tau VMs ideal for scale-out workloads including web servers, containerized microservices, data-logging processing, media transcoding, and large-scale Java applications. 

Google Kubernetes Engine support

Google Kubernetes Engine (GKE) is the de facto standard for organizations looking for advanced container orchestration, delivering the highest levels of reliability, security, and scalability. GKE supports Tau VMs on day 1, helping you optimize price-performance for your containerized workloads. You can add Tau VMs to your GKE clusters by specifying the T2D machine type in your GKE node-pools. 

Pricing

Tau VMs will be priced to support significant TCO and price-performance improvements for your cloud applications. A 32vCPU VM with 128GB RAM will be priced at $1.3520 per hour for on-demand usage in us-central1. 

Coming soon to a Google Cloud region near you

If you are interested in trying out T2D VMs when they become available in Q3 2021 please sign-up here.

Finding ways to increase customer loyalty and profitability in a post-COVID world is top of mind for hotels, bars, and restaurants. Unfortunately, many food and beverage services providers struggle to deliver the personalized experiences that keep guests coming back for more. The reality is that most traditional hospitality apps offer only limited insight into guest activities, preferences, underlying operating costs, and other essential details.

We built SevenRooms to help food and beverage operators create truly memorable experiences by managing and personalizing every step of the guest journey. With SevenRooms, restaurants in more than 250 cities globally leverage technology and data to provide unforgettable hospitality experiences with a human touch. That includes seating guests at their favorite table, pouring complimentary glasses of wine from a preferred vintage, offering special menu options, and personalized experiences for special occasions.

Scaling guest experience and retention on Google Cloud

When developing SevenRooms, we needed a technology partner that would enable our small team to securely scale services, automate manual tasks, and accelerate time to market while reducing IT costs. That’s why we started working with Google App Engine—and later became more involved with the Google for Startups Cloud Program.

We soon realized many traditional apps lacked the integrations and capabilities needed to respond to the unique challenges facing food and beverage operators that the Google for Startups Cloud Program services provide. With guidance from experts on the Google Startups Success team, we quickly transformed SevenRooms from a beta restaurant and nightclub reservation and guest management point solution into a full-fledged guest experience and retention platform that analyzes actionable data to automatically build and continuously update detailed guest profiles.

The combination of Google App Engine and other Google Cloud solutions makes everything easier to build and scale. We’re seeing our total cost of ownership (TCO) decline by 10-15%, so we can shift additional resources to R&D to help customers create one-of-a-kind interactions with their guests. And importantly, we can bring new products to market 200-300% faster than competitors.

Because SevenRooms handles a lot of sensitive guest data, stringent security protocols are vital. We store all data on Google Cloud, taking advantage of its highly secure-by-design infrastructure and built-in support for international data privacy laws such as GDPR. We use BigQuery and Looker for data analysis and reporting, and power our NoSQL database with Firestore. We also scale workloads and run Elasticsearch on Google Compute Engine (GCE)—and seamlessly integrate our reservation booking engine with Google Maps and Google Search.

In the future, we’re looking to further market actionable guest data with the help of advanced machine learning (ML) models with TensorFlow and Google Cloud Tensor Processing Units (TPUs). Cloud TPUs and Google Cloud data and analytics services are fully integrated with other Google Cloud offerings, including Google Kubernetes Engine (GKE). By running ML workloads on Cloud TPUs, SevenRooms will benefit from Google Cloud’s leading networking and data analytics technologies such as BigQuery.

We’re also exploring additional Google Cloud solutions such as Anthos to unify the management of infrastructure and applications across on-premises, edge, and multiple public clouds, as well as Google Cloud Run to deploy scalable containerized applications on a fully managed serverless platform. These solutions will enable us to continue to quickly expand our services and offer customers a variety of new benefits.

Building a profitable, sustainable future in food and beverage

Our work with the Google Startups Success team has been instrumental to helping us get where we are today. Their responsiveness is incredible and stands out compared to services from other technology providers. Google Cloud gives us a highly secure infrastructure and next-level training to evolve our infrastructure using solutions such as BigQuery and Backup and Disaster Recovery.

We also work with Google Cloud partner, DoiT International, to further scale and optimize our operations. In particular, DoiT provided expert scripts to short-cut lengthy processes, while actively troubleshooting any issues or questions that came up. DoiT continues to share guidance in key areas for future products and features. They have provided expertise for architecture, infrastructure and cost management. Moving forward, we’re excited to work with Google Cloud and DoiT to handle a growing surge in users which we anticipate in 2023 and beyond.

With Google Cloud, SevenRooms is revitalizing food and beverage service delivery by enabling businesses to cultivate and maintain direct guest relationships, deliver exceptional experiences, and encourage repeat visits. We’ve compiled many case studies that demonstrate how our customers see great results by personalizing their interactions with guests, from $1.5M in cost savings, to $400K of additional revenue, to a 68% jump in email open rates.

Demand for our guest experience and retention platform keeps growing as we help our customers take a people-first approach by delivering unique and tailored dining experiences. We can’t wait to see what we accomplish next as we expand our team and reach new markets worldwide.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

Organizations across industries are looking to AI to turn troves of data into intelligence, powered by the latest advances in generative AI. Yet for many organizations, there is a barrier to adopting the latest models because they can be costly to train or serve. A new class of cloud GPUs is needed to lower the cost of entry for businesses that want to tap the power of AI. 

Today, we’re introducing G2, the newest addition to the Compute Engine GPU family in Google Cloud. G2 is the industry’s first cloud VM powered by the newly announced NVIDIA L4 Tensor Core GPU, and is purpose-built for large inference AI workloads like generative AI. G2 delivers cutting-edge performance-per-dollar for AI inference workloads that run on GPUs in the cloud. By switching from NVIDIA A10G GPUs to G2 instances with L4 GPUs, organizations can lower their production infrastructure costs up to 40%. We also found that customers switching from NVIDIA T4 GPUs to L4 GPUs can achieve 2x-4x better performance. As a universal GPU offering, G2 instances also help accelerate other workloads, offering significant performance improvements on HPC, graphics, and video transcoding. Currently in private preview, G2 VMs are both powerful and flexible, and scale easily from one up to eight GPUs. 

Currently organizations require end-to-end enterprise ready infrastructure that will future proof their AI and HPC initiatives for a new era. G2s will be ready to be deployed on Vertex AI, GKE, and GCE, giving customers the freedom to architect their own custom software stack to meet their performance requirements and budget. With optimized Vertex AI support for G2 VMs, AI users can tap the latest generative AI models and technologies. With an easy to use UI and automated workflows, customers can access, tune and serve modern models for video, text, images, and audio without the toil of manual optimizations. The combination of these services with the power of G2 will help customers harness the power of complex machine models for their business.

NVIDIA L4 GPUs with Ada Lovelace Architecture

G2 machine families enable machine learning customers to run their production infrastructure in the cloud for a variety of applications such as language models, image classification, object detection, automated speech recognition, and language translation. Built on the Ada Lovelace architecture with fourth-generation Tensor Cores, the NVIDIA L4 GPU provides up to 30 TFLOPS of performance for FP32, and 242 TFLOPs for FP16. Newly added FP8 support, on top of existing INT8, BFLOAT16 and TF32 capabilities, makes the L4 ideal for ML inference. 

With the latest third-generation RT Cores and DLSS 3.0 technology, G2 instances are also great for graphics-intensive workloads such as rendering and remote workstations when paired with NVIDIA RTX Virtual Workstation. NVIDIA L4 provides 3x video encoding and decoding performance, and adds new AV1 hardware-encoding capabilities. For example, G2 can enable gaming customers running game engines such as Unreal and Unity with modern graphics cards to run real-time applications. Likewise, media and entertainment customers that need GPU-enabled virtual workstations can use the L4 to create photo-realistic, high-resolution 3D content for movies, games, and AR/VR experiences using applications such as Autodesk Maya or 3D Studio Max.

What customers are saying

A handful of early customers have been testing G2 and have seen great results in real-world applications. Here are what some of them have to say about the benefits that G2 with NVIDIA L4 GPUs bring:

AppLovin

AppLovin enables developers and marketers to grow with market leading technologies. Businesses rely on AppLovin to solve their mission-critical functions with a powerful, full stack solution including user acquisition, retention, monetization and measurement.

“AppLovin serves billions of AI powered recommendations per day, so scalability and value are essential to our business,” said Omer Hasan, Vice President, Operations at AppLovin. “With Google Cloud’s G2 we’re seeing that NVIDIA L4 GPUs offer a significant increase in the scalability of our business, giving us the power to grow faster than ever before.”

WOMBO

WOMBO aims to unleash everyone’s creativity through the magic of AI, transforming the way content is created, consumed, and distributed. 

“WOMBO relies upon the latest AI technology for people to create immersive digital artwork from users’ prompts, letting them create high-quality, realistic art in any style with just an idea,” said Ben-Zion Benkhin, Co-Founder and CEO of WOMBO. “Google Cloud’s G2 instances powered by NVIDIA’s L4 GPUs will enable us to offer a better, more efficient image-generation experience for users seeking to create and share unique artwork.”

Descript

Descript’s AI-powered features and intuitive interface fuel YouTube and TikTok channels, top podcasts, and businesses using video for marketing, sales, and internal training and collaboration. Descript aims to make video a staple of every communicator’s toolkit, alongside docs and slides. 

“G2 with L4’s AI Video capabilities allow us to deploy new features augmented by natural-language processing and generative AI to create studio-quality media with excellent performance and energy efficiency” said Kundan Kumar, Head of Artificial Intelligence at Descript.

Workspot

Workspot believes that the software-as-a-service (SaaS) model is the most secure, accessible and cost-effective way to deliver an enterprise desktop and should be central to accelerating the digital transformation of the modern enterprise.

“The Workspot team looks forward to continuing to evolve our partnership with Google Cloud and NVIDIA. Our customers have been seeing incredible performance leveraging NVIDIA’s T4 GPUs. The new G2 instances with L4 GPUS through Workspot’s remote Cloud PC workstations provide 2x and higher frame rates at 1280×711 and higher resolutions” said Jimmy Chang, Chief Product Officer at Workspot.

Pricing and availability

G2 instances are currently in private preview in the following regions: us-central1, asia-southeast1 and europe-west4. Submit your request here to join the private preview, or to receive a notification as when the public preview begins. Support will be coming to Google Kubernetes Engine (GKE), Vertex AI, and other Google Cloud services as well. We’ll share G2 public availability and pricing information later in the year.

May is Asian Pacific American Heritage Month —a time for us to come together to celebrate and remember the important people and history of Asian and Pacific Island heritage. This feature highlights three AAPI founders from the Google For Startups community.

Read on to learn how these three founders built their businesses, leverage Google tech, and suggestion they have for aspiring entrepreneurs.

CultivatePeople 

Founder: Lola Han

Description: CultivatePeople’s compensation software, Kamsa, provides global market pay rate data and helps companies make data-driven salary decisions so they can attract and retain their most valuable asset: employees. 

Why GCP: “CultivatePeople began using GCP when integrating SSO/SAML authentication after  our SaaS product became a high priority. We were able to exceed our clients’ expectations and increase their level of trust in our platform’s capabilities. We’re currently investigating additional machine learning products, such as Cloud AutoML, that will allow us to quickly deliver exciting features.” 

Note from the Founder: “I grew up with immigrant parents who value stability and are risk-averse. My parents discouraged me from starting my own company because they didn’t want to see me struggle financially or see my health suffer (due to stress). I felt strongly about what CultivatePeople could do, so I started the company as a sole founder in 2017 and watched it double in size year over year since. While the ones I love most may not have cheered me on initially, it was important for me to hang on to the encouraging words of former bosses, executives, and founders to keep me focused on my mission. 

My advice for other AAPI founders is to be a “silent assassin” and believe in the mission and values of your organization. Always remember to stop along the way and: 

 1) Enjoy the journey by celebrating wins and giving yourself credit;

2) Follow your intuition—it’s (almost) always right; 

3) Recognize and invest in your people regularly (ie. give increases more than once a year, if warranted);

4) Give regular words of affirmation to employees on even small achievements.”

Swit

Founder: Josh Lee and Max Lim

Description: Swit is a team collaboration platform that seamlessly combines team chat with task management by allowing teams to turn their conversations into trackable tasks and share tasks to chat with simple drag-and-drop functionality, ensuring everyone is on the same page and projects get done faster.

Why GCP: “Swit is a cross-category hybrid work tool for chat and tasks. This functionality requires more complicated and heavier architecture for performance. So, configuring and managing virtual machines was really challenging to scale up our systems, while handling occasional unexpected traffic surges and frequent updates. Eventually we divided our monolithic architecture into 35 microservices when we launched our official product. The migration to GKE took around one month, and it turned out to be well worth the effort—our systems became able to offer high scalability and enough resilience to keep its uptime no matter what happened. Now we’re operating 84 workloads and 252 microservices with high stability with remarkably low downtime – less than 0.00001%/year.”

Note from the Founder: “As an AAPI founder based in Silicon Valley, I feel proud of the work ethics and diligence fellow Korean American entrepreneurs and professionals have long demonstrated here. Especially with K-pop breaking into the mainstream, I feel even more proud of our culture that strives toward an absolute perfection molded through years of training and dedication. The mission-driven culture of Silicon Valley coupled with Google’s edging technology and creativity really helped us build a product that not only encompasses verticals but also transcends cultures. Swit is growing at an unprecedented rate, and we hope  to join the long list of successful AAPI entrepreneurs here. Swit’s close network with the AAPI community wouldn’t have been possible without Google support. We are grateful for this collaborative environment, and we hope to become the next-generation ambassador for collaboration after Google.” 

Check out more from Swit in their founder story.

WISY 

Founder: Min Chen

Description: Wisy develops technology to bring digital efficiency into the physical world, supporting consumer products businesses and making them thrive in the new economy. All of us have a bad experience when we can’t find the product we want to buy. That is a $1.9T problem in the consumer-packaged goods industry that Wisy is solving with AI and analytics to help manufacturers and retailers sell more by reducing out-of-stocks and waste at a global scale.

Why GCP: “GCP has an intuitive, easy to use interface, was lower cost, and offered preemptible instances with flexible compute options. Some of the reasons why Wisy decided to use GCP include instance and payment configurability, privacy and traffic security, cost-efficiency, and Machine Learning.

Wisy has been able to advance quickly with product development, as well as collaborate better and iterate faster in the creation of our AI models, while reducing costs by 40%. At Wisy, we are solving a problem that affects everyone who shops at a store.“

Note from the Founder: “Two years ago, I moved to San Francisco to expand my second startup, Wisy. This is when I learned that my name ‘Min’ stands for ‘minority.’ I was born in China, raised in a Black community in Panama, received scholarships to attend both Carnegie Mellon and UC Berkeley. I worked for 20 years in several countries, but I have never felt so discriminated against due to my race, ethnicity, gender and age than during my time in Silicon Valley. However, this is also the place I learned that my diverse life experience is my competitive advantage. My background enables me to recruit and relate to people in different countries, create scalable and flexible products for multinational customers, and run global operations efficiently.

My recommendation to AAPI founders is to find strength in their multicultural background. Don’t hide what makes you unique, do not limit yourselves, and do not let others limit you. You will lose your edge when trading authenticity for validation. Be proud and own your story.”

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.To learn more about how you can help #StopAsianHate during Asian Pacific American Heritage Month and beyond, visit their website here.

Over the last decade, financial service organizations have been adopting a cloud-first mindset. According to InformationWeek, lower costs and enhanced scalability were the biggest drivers for cloud adoption in financial services, and cloud-native applications allow access to the latest technology and talent, enabling adopters to rebuild transaction processing systems capable of supporting very high volumes and low latency.

Both Neo4j and Google Cloud have been using relationship-based data representations since the beginning, and we’re dedicated to using this technology to help financial services customers drive business transformation. We are excited about the prospects of financial services (FinServ) cloud systems and believe that graph data in the cloud can help solve significant challenges in the industry.

Data Challenge #1: Risk Management and Compliance

First among the top concerns for any CIO moving to the cloud is risk management and compliance. Disconnected, uncontextualized, or stale data create opportunities for fraud and financial crimes to occur. The fact is when it comes to FinServ, the question is not “if” but rather how often an attack will occur.  Unfortunately, incidents have been trending upward over the last decade, and COVID has only exacerbated this reality. Financial crimes affect the bottom line both in the remediation of these crimes and in intangibles like brand value.  

Add to this the complexity of international banking, which makes “compliance” a moving target. Penalties due to noncompliance are a constant concern to any FinServ organization.

The tabular representation of information with a fixed number of columns that never change prevents a description of an ever changing world with changing characteristics. Relational databases are great if the world you describe does not move fast but have limitations when data structures are highly interlinked and not homogeneous.

Neo4j Aura on Google Cloud provides a foundation for creating dynamic, futureproof, scalable applications that adhere to the security standards and protocols today’s financial services organizations require to meet the challenges of finding and preventing bad actors. This also includes enterprise scalability; reaching over 1 Billion nodes and relationships to streamline queries and provide solutions that meet regulatory and privacy compliance across geographies. Neo4j has helped some organizations save billions of USD in fraud in the first year of deployment alone.  

What makes graph technology the best choice for fraud detection use cases is that the relationships between the data-points are as important as the data-points themselves. Let’s take as an example, one John Smith approaches a multi-national banking institution to manage the primary account for his new holding corporation.  

While no one has any record of John R Smith Holdings LLC, the bank’s application built on graph technology understands that there are several well-known entities owned by John Smith Holdings. The application also identifies several well-known board members who bank with this institution. Due to this relationship-driven approach, the bank now understands John R Smith is not “John Smith,” who previously attempted to open an account for his holding corporation, which had no information associated with it prior to two months ago.

Data Challenge #2 Manual Processes and Inefficiencies 

The ubiquity of the cloud offers an opportunity to deploy automation at unprecedented levels to tackle the errors and inefficiencies that manual processing allows to creep into processes. When data comes from disparate, perhaps legacy systems – which may have become siloed and “untouchable” over the years – further complexity arises. As an example, if someone in sales types “John Smith” into a CRM system not knowing that John R Smith is the spelling in the customer data master, it may result in two separate and potentially conflicting records. Being able to join those records together in a mastered view helps to solve this problem. In addition, low data quality equates to an increase in risk, costs, and implementation times for new systems. 

Neo4j Aura on Google Cloud provides automation and artificial intelligence (AI) that reduces manual processes and the errors that accompany them. In this graph architecture each node, which can represent a person, will have labels, relationships, and properties associated with it. This allows for the use of AI which can easily understand that John Smith in the CRM is the same John R Smith in the customer master. The information contained in Neo4j can be connected bi-directionally to ensure consistency across applications and data sources. 

One of the benefits of this approach is that linking information allows organizations to keep the full value of the data, rather than forcing the data into predetermined tabular representations, with the risk of losing valuable information and insights.

Data Challenge #3: Customer Engagement and Insight

Another significant concern is the high expectations today’s customers have for every interaction. End users are accustomed to predictable experiences on their digital devices, and FinServ apps are no exception. Added to this, the “Covid economy” has driven digital adoption significantly across demographics; even among customers who might traditionally have used in-person services. This also equates to increased expectations for personalized, predictable experiences with every digital interaction. We know that latency has always been a key consideration for financial trading, but a recent ComputerWeekly study showed that every financial organization should ensure their visible latency is at 10 milliseconds or less. Customers no longer accept their broadband is at fault.

Finally, blind spots in the customer journey often result in dissatisfaction, which ultimately leads to increased churn. Without gaining actionable insights from your customers, there is no room to innovate and iterate on what they are looking for in your products and services. And this translates to losing market share and competitive advantage.

The NoSQL architecture, specifically the dynamic schema and structure of Neo4j Aura gives you the ability to take charge of your data and make changes according to your development cycles or newer data models. This equates to faster builds, more comprehensive releases and a wider, richer data-set that can be contextualized and understood instantly. Graph technology is the logical choice for building a Customer 360 application. Under this approach organizations not only get valuable insight into the individual client’s behavior and patterns, but also those of their family, friends and colleagues. This allows for stronger personalization, targeted campaigns and successful execution, resulting in increased customer satisfaction and retention levels.

Graph Technology on Google Cloud

Neo4j is a recognized leader in graph database technology and the only fully integrated graph solution on Google Cloud, helping to fill a common need for Google Cloud customers. Both Neo4j and Google Cloud are invested in continuing to grow our partnership and mutual product direction.  

You can find and deploy the Neo4j graph database straight from the Google Cloud marketplace, whether you want to download the software for an on-premises deployment, use the virtual machine image, or use the hosted solution, Aura on Google Cloud, the graph database-as-a-service. In any deployment, you get the same enterprise-grade scalability, reliability, and connectivity along with successful, repeatable use cases you can rely on to resolve your particular challenges and integrated billing. 

For a real-world example of how graph technology can optimize financial services, you can read our Case Study with fintech Current. Current, a leading U.S. financial technology platform with over three million members, used Neo4j Aura on Google Cloud to create a personalization engine based on client relationships. 

To learn more about Neo4j Aura on Google Cloud for FinServ organizations, register for our webinar on Thursday, December 16 with Jim Webber, Chief Scientist, CTO Field Ops at Neo4j and Antoine Larmanjat, Technical Director, Office of the CTO, Google Cloud. 

Click here to Register