Cloud Functions, Google Cloud’s Function as a Service (FaaS) offering, is a lightweight compute platform for creating single-purpose, standalone functions that respond to events, without needing an administrator to manage a server or runtime environment. 

Over the past year we have shipped many new important capabilities on Cloud Functions: new runtimes (Java, .NET, Ruby, PHP), new regions (now up to 22), an enhanced user and developer experience, fine-grained security, and cost and scaling controls. But as we continue to expand the capabilities of Cloud Functions, the number-one friction point of FaaS is the “startup tax,” a.k.a. cold starts: if your function has been scaled down to zero, it can take a few seconds for it to initialize and start serving requests. 

Today, we’re excited to announce minimum (“min”) instances for Cloud Functions. By specifying a minimum number of instances of your application to keep online during periods of low demand, this new feature can dramatically improve performance for your serverless applications and workflows, minimizing your cold starts.

Min instances in action

Let’s take a deeper look at min instances with a popular, real-world use case: recording, transforming and serving a podcast. When you record a podcast, you need to get the audio in the right format (mp3, wav), and then make the podcast accessible so that users can easily access, download and listen to it. It’s also important to make your podcast accessible to the widest audience possible including those with trouble hearing and those who would prefer to read the transcript of the podcast. 

In this post, we show a demo application that takes a recorded podcast, transcribes the audio, stores the text in Cloud Storage, and then emails an end user with a link to the transcribed file, both with and without min instances. 

Approach 1: Building the application with Cloud Functions and Cloud Workflows

In this approach, we use Cloud Functions and Google Cloud Workflows to chain together three individual cloud functions. The first function (transcribe) transcribes the podcast, the second function (store-transcription) consumes the result of the first function in the workflow and stores it in Cloud Storage, and the third function (send-email) is triggered by Cloud Storage when the transcribed result is stored and sends an email to the user to inform them that the workflow is complete.

Cloud Workflows executes the functions in the right order and can be extended to add additional steps in the workflow in the future. While the architecture in this approach is simple, extensible and easy to understand, the cold start problem remains, impacting end-to-end latency. 

Approach 2: Building the application with Cloud Functions, Cloud Workflows and min instances

In this approach, we follow all the same steps as in Approach 1, with a slightly modified configuration that enables a set of min instances for each of the functions in the given workflow.

This approach presents the best of both worlds. It has the simplicity and elegance of wiring up the application architecture using Cloud Workflows and Cloud Functions. Further, each of the functions in this architecture leverages a set of min instances to mitigate the cold-start problem and time to transcribe the podcast.

Comparison of cold start performance

Now consider executing the Podcast transcription workflow using Approach 1, where no min instances are set on the functions that make up the app. Here is an instance of this run with a snapshot of the log entries. The start and end timestamps are highlighted to show the execution of the run. You can see here that the total runtime in Approach 1 took 17 s. 

Approach 1: Execution Time (without Min Instances)

Now consider executing the podcast transformation workflow using Approach 2, where min instances are set on the functions. Here is an instance of this run with a snapshot of the log entries. The start and end timestamps are highlighted to show the execution of the run, for a total of 6 s. 

Approach 2: Execution Time (with Min Instances)

That’s an 11 second difference between the two approaches. The example set of functions are hardcoded with a 2 to 3 second sleep during function initialization, and when combined with average platform cold-start times, you can clearly see the cost of not using min instances.

You can reproduce the above experiment in your own environment using the tutorial here. 

Check out min instances on Cloud Functions

We are super excited to ship min instances on Cloud Functions, which will allow you to run more latency-sensitive applications such as podcast transcription workflows in the serverless model. You can also learn more about Cloud Functions and Cloud Workflows in the following Quickstarts: Cloud Functions, Cloud Workflows.

Our customers tell us they don’t just want to migrate their applications from point A to point B, they want to modernize their applications with cloud-native technologies and techniques, wherever those applications may be. 

Today, we’re excited to tell you about a variety of new customers that are using Anthos to transform their application portfolio, as well as new cloud migration, API management, and application development offerings:

• New customers leveraging Anthos for a variety of on-prem, cloud and edge use cases
• The general availability of Migrate for Anthos
• Apigee hybrid in general availability
• The general availability of Cloud Code

Accelerating app modernization with Anthos

Anthos was the first open app modernization platform to offer a unified control plane and service delivery across diverse cloud environments—managed cloud, on-premises and edge. Since it became generally available in the spring, organizations across a variety of industries and geographies have turned to Anthos to bring the benefits of cloud, containers and microservices to their applications. 

According to the findings from Forrester’s Total Economic Impact study, customers adopting Anthos have seen up to 5x return on investment based on the savings from ongoing license and support costs, and the incremental savings from operations and developer productivity. For one customer in the financial services industry, rolling out new features and updates to their core banking application used to take at least a quarter. Now with Anthos, they were able to eliminate months long development and release cycles, and roll out on a weekly basis. That’s a 13x improvement on time to market. 

This week, several new European Anthos customers will take the stage at Next UK to talk about how they’re using Anthos to transform their IT operations. 

Kaeser Kompressoren SE of Coburg, Germany, is a provider of compressed air products and services. The company needed a consistent platform to deploy and manage existing on-prem SAP workloads, like SAP Data Hub, and also wanted to be able to tap into other services running in Google Cloud to get more value from those environments. 

“Application modernization is enabling business innovation for Kaeser,” said Falko Lameter, CIO. “To gain better insights from data, we knew we needed to incorporate advanced machine learning and data analytics in all our applications. We chose Google Cloud’s Anthos because it offered the flexibility to incrementally modernize our legacy application on-premises without business disruption, while allowing us to run other applications on Anthos in Google Cloud and take advantage of its managed data analytics and ML/AI services.”

Then there’s Denizbank. Based in Turkey, Denizbank provides a variety of commercial banking services, and established the first Digital Banking Department in Turkey in 2012. Denizbank turned to Anthos for an open application modernization platform to help it develop its next-generation mobile banking applications.

“We operate in 11 different countries and have to comply with various regulatory requirements like data locality and sovereignty, which mandates some or all applications to reside on premises in certain countries, while the rest of the apps can move to the cloud in other countries,” said Dilek Duman, COO of DenizBank. “We chose Google Cloud’s Anthos for its flexibility to modernize our existing application investments with ease, and to deliver AI/ML powered software faster while improving operational security and governance. Anthos gives us the ability to have a unified management view of our hybrid deployments, giving us a consistent platform to run our banking workloads across environments.” 

Anthos is even starting to be deployed to edge locations, where, thanks to its 100% software-based design, it can run on any number of hardware form factors. We’re in advanced discussions with customers in telecommunications, retail, manufacturing and entertainment about using Anthos for edge use cases, as well as with global hardware OEMs.

Move and modernize with Migrate for Anthos

In addition to leveraging cloud technology for their on-premises environments with Anthos, customers also want to simultaneously migrate to the cloud and modernize with containers. That’s why we’re happy to announce the general availability of Migrate for Anthos, which provides a fast, low-friction path to convert physical servers or virtual machines from a variety of sources (on-prem, Amazon AWS, Microsoft Azure, or Google Compute Engine) directly into containers in Anthos GKE.

Migrate for Anthos makes it easy to modernize your applications without a lot of manual effort or specialized training. After upgrading your on-prem systems to containers with Migrate for Anthos, you’ll benefit from a reduction in OS-level management and maintenance, more efficient resource utilization, and easy integration with Google Cloud services for data analytics, AI and ML, and more. 

DevFactory aims to offload repetitive tasks in software development so that dev teams can focus on coding and productivity. As advocates for optimization through containers, they found Migrate for Anthos a key way to help deliver on their goals:  

“We usually see less than 1% resource utilization in data centers. Migrate for Anthos is a remarkable tool that allows us to migrate data center workloads to the cloud in a few simple steps,” said Rahul Subramaniam, CEO, Devfactory. “By automatically converting servers and virtual machines into containers with Migrate for Anthos, we get better resource utilization and dramatically reduced costs along with managed infrastructure in the end state, which makes this a very exciting and much-needed solution.” 

Migrate for Anthos is available at no additional cost, and can be used with or without an Anthos subscription.

API-first, everywhere, with Apigee hybrid 

To drive modernization and innovation, enterprises are increasingly adopting API-first approaches to connecting services across hybrid and multi-cloud environments. To address the need for hybrid API management, we’re announcing the general availability of Apigee hybrid, giving you the flexibility to deploy your API runtimes in a hybrid environment, while using cloud-based Apigee capabilities such as developer portals, API monitoring, and analytics. Apigee hybrid can be deployed as a workload on Anthos, giving you the benefits of an integrated Google Cloud stack, with Anthos’ automation and security benefits. 

Gap Inc. uses Apigee to publish, secure, and analyze APIs and easily onboard the development teams working with those APIs. Apigee hybrid will help Gap Inc. overcome the traditional tradeoffs between on-premises and cloud, providing the best of both worlds.   

“With Apigee hybrid, we can have an easy to manage, localized runtime for scenarios where latency or data sensitivity require it. At the same time, we can continue to enjoy all the benefits of Apigee such as Apigee’s developer portal and its rich API-lifecycle management capabilities,” said Patrick McMichael, Enterprise Architect at Gap Inc. 

Simplifying the developer experience

Google Cloud application development tools are designed to help you simplify creating apps for containers and Kubernetes, incorporate security and compliance into your pipelines, and scale up or down depending on demand, so you only pay for what you use. 

With these goals in mind, last week, we announced the general availability of Cloud Run and Cloud Run for Anthos. Cloud Run is a managed compute platform on Google Cloud that lets you run serverless containers on on a fully managed environment or on Anthos. With Cloud Run fully managed, you can easily deploy and run stateless containers written in any language, and enjoy serverless benefits such as automatic scale up and scale down and pay-for-use—without having to manage the underlying infrastructure. 

Cloud Run for Anthos, meanwhile, brings those same serverless developer experience to Anthos managed clusters, giving developers access to a modern, serverless compute platform while their organization modernizes its on-prem environment with Kubernetes. 

Easier Kubernetes development with Cloud Code

Today, we’re excited to announce the general availability of another important member of the Google Cloud application development stack: Cloud Code, which lets developers write, debug and deploy code to Google Cloud or any Kubernetes cluster through extensions to popular Integrated Developer Environments (IDEs) such as Visual Studio Code and IntelliJ. 

Developers are most productive while working in their favorite IDE. By embracing developers’ existing workflow and tools, Cloud Code makes working with Kubernetes feel like you are working with a local application, while preserving the investment you’ve made to configure your tools to your own specific needs. Cloud Code dramatically simplifies the creation and maintenance of Kubernetes applications.

In addition, Cloud Code speeds up development against Kubernetes by extending the edit-debug-review “inner loop” to the cloud. You get rapid feedback on your changes, ensuring that they’re of high quality. And when it comes to moving code to the production environment, Cloud Code supports popular continuous integration and delivery (CI/CD) tools like Cloud Build. 

Finally, with Cloud Code, diagnosing issues does not require a deep understanding of Kubernetes, thanks to connected debuggers and cluster-wide logging that help you address issues all from the context of your favorite tool. 

Toward modern, efficient applications

Application modernization means a lot of things to a lot of people. Depending on your environment, it can mean updating VMs to containers and Kubernetes, it can mean moving them to the cloud, or it can mean distributing them to edge locations and unifying workloads with consistent API and service management. For others, application modernization means using cloud-native tools and concepts like serverless and CI/CD. Whatever your definition, we can help you realize your business and modernization goals, achieving greater agility while improving overall governance.

Generative AI continues to develop at a blistering pace, making it more important than ever that organizations have access to enterprise-ready capabilities to help them leverage this disruptive technology. 

Harnessing the power of decades of Google’s research, innovation, and investment in AI, Google Cloud continues to make generative AI available with baked-in security, data governance, and scalability across the board. 

To this end, last month, we announced the general availability of Generative AI support on Vertex AI, giving our customers the ability to access powerful foundation models from Google Research and tools for customizing and applying them. 

Today we are announcing the general availability (GA) of four important foundation models for Vertex AI. These include Imagen, PaLM 2 for Chat, Codey, and Chirp. For each of these models, organizations can access APIs on Model Garden and do prompt design and tuning on Generative AI Studio.

• Imagen includes four key features:
  • Image generation for creating studio-grade images at scale
  • Image editing to edit generated or existing images via text prompts 
  • Image captioning for creating captions of images at scale
  • Visual Question & Answering (VQA) for interacting with, analyzing, and explaining images
• PaLM 2 for Chat follows the general availability of PaLM 2 for Text in June 
• Codey supports code generation, completion, and code chat
• Chirp supports multilingual Speech AI 

We’re also announcing Multimodal Embeddings API in preview, which lets customers combine the power of Vertex AI’s generative AI models with their proprietary data, to generate embeddings, or interchangeable vector representations, of their text and image data. These capabilities can enable data science teams to deliver a variety of downstream tasks such as image classification, content recommendations, and visual search. 

In this blog post, we’ll explore what your organization can do with these powerful models and how Vertex AI provides the enterprise-ready capabilities you can use to get up and running with generative AI. 

Helping to drive enterprise value from Generative AI models

Powerful models are the foundation of generative AI, but the software, tools, and infrastructure that surround these models are equally important for enterprise adoption. Organizations face challenges not only accessing these models, but also integrating AI while maintaining protection over intellectual property, adhering to regulations around data security and privacy, and ensuring models and applications are safe to use. Many organizations also want to use generative AI without incurring large costs or managing huge clusters.

We help address these challenges head-on with Vertex AI’s platform capabilities for scalable application integration, purpose-built AI infrastructure, secure and private data customization, and responsible use of this technology. 

Let’s see how each of these pillars can help your organization. 

Access models to build production-ready generative applications 
Vertex AI can make it easy to access foundation models, as today’s model announcements attest. While models are an inextricable part of generative AI, the software that helps enterprises use this technology is equally important—which is why Vertex AI also offers a range of tools for tuning, deploying, monitoring, and maintaining models, so you can build differentiated applications using your own data. 

Turning to today’s announcements, in May we announced Imagen, our foundation model for image generation. Now, we are excited to announce Imagen is generally available with an allowlist (i.e., approved access via your sales representative), letting onboarded customers start using image generation and editing capabilities. Visual Q&A and Captioning for production workloads are also generally available for all customers. Visual Q&A provides new ways to engage with image-based data like retail products or image libraries. This new capability can give you answers to questions about an image, helping you analyze large amounts of data quickly, and it can even help the visually impaired understand images or graphs that they wouldn’t be able to otherwise. Captioning, meanwhile, can make it easy to generate relevant descriptions for your images. Captions can help with indexing and searching, as well as assigning image descriptions to product listings on eCommerce websites. 

“Imagen is beginning to power key capabilities within Omni, Omnicom’s open operating system, that will enable 17,000+ trained and certified users to create audience-driven customized images in minutes. Imagen has been instrumental in offering a scalable platform for image generation and customization. Integrating it into our platform allows us to expand the scope of audience-powered creative inspiration, at a scale that wasn’t previously possible,” said Art Schram, Annalect Chief Product Officer at Omnicom. “We’re starting to adopt the latest features like styles and fine tuning, and engineering data-driven prompts. We look forward to continuing to provide our users relevant visual inspiration in a responsible way.”

“The latest improvements in Imagen’s product preservation capabilities are a perfect match for Typeface’s focus on personalized AI for brands,” explained Vishal Sood, Head of Product at Typeface. “By combining Google Vertex AI’s Imagen with Typeface’s brand-personalized AI, we are able to help enterprises to create 10x personalized content in a fraction of time.”

Google Shopping recently built an application called Product Studio using Imagen on Vertex AI. Product Studio can enable merchants to create rich product images quickly and easily, at a fraction of the time it takes to do professional product photo shoots. “We’re excited about the feedback we’re getting from merchants in our early pilots, who say that Product Studio, which leverages Imagen on Vertex AI, helps them generate and publish lifestyle product photos directly to their product catalogs,” says Jeff Harrell, Google’s Senior Director of Product Management for Merchant Shopping. 

Announced in May, PaLM 2 is a family of models that power dozens of Google products, including Bard and Duet AI in Google Cloud. With the PaLM 2 for Chat model, now generally available, you can leverage Google’s PaLM’s variety of abilities for multi-turn chat applications, such as shopping assistants, customer support agents, and more. 

ThoughtSpot, provider of a widely-adopted business intelligence platform, is using PaLM 2 to build a new feature in ThoughtSpot for Google Sheets called “AI Explain,” which can instantly generate explanations of charts, visuals, and anomalies, and will launch new conversational AI and ML-enabled predictive forecasting capabilities into its analytics platform.

With Codey, your organization’s developers can accelerate a wide variety of coding tasks, helping to empower them to work efficiently and close skills gaps. The model enables not only code completion and code generation capabilities, but also chat to help with debugging, documentation, learning new concepts, and more. Since launching in preview in May, we’ve added additional programming languages including Go, Google Standard SQL, Java, Javascript, Python, and Typescript. We’ve also improved the quality of code responses and increased serving capacity, enabling your developers with the right tools to enter the era of generative engineering.  

“Security and privacy are key to incorporating AI into the software development lifecycle,” said David DeSanto, Chief Product Officer at GitLab. “GitLab leverages Vertex AI to deliver new, AI-powered features with a privacy-first approach, including the ability to run our own models and leverage Codey foundation models built on top of PaLM 2. The GitLab DevSecOps platform empowers organizations to harness the benefits of AI for faster software delivery, while ensuring their data, intellectual property, and source code are protected.”

Originally released in May in preview, Chirp is a version of our 2 billion-parameter speech model, which was trained on millions of hours of audio and supports over 100 languages. Chirp achieves 98% accuracy on English and relative improvement of up to 300% in languages with less than 10 million speakers. Whether the use case involves customer support, transcriptions, or voice control, Chirp can help your organization communicate with customers and constituents inclusively, by engaging audiences in their native languages. 

Last but not least, our Multimodal Embeddings API, now in preview, can unlock an array of new applications, such as image and text-based recommendations, by enabling the processing of text and images interchangeably. This capability complements our Text Embeddings API, which became generally available in June, and remains a recommended choice for those with fully text-based use cases. Multimodal Embeddings API makes it possible to categorize images and text together and can be crucial for use cases like retail recommendation systems that can provide relevant outputs from both images of products and text descriptions.

Match generative AI with infrastructure 
Beyond access to models and tools for building generative AI apps, you need infrastructure to make sure your apps can scale and reliably perform — ideally without running into daunting compute costs or management overhead that distracts your technical talent from building innovative products. Google Cloud offers the choice and power to run smaller models running finite tasks at the lowest latency levels, as well as to run large models capable of cutting-edge experiments. 

As our large language model customers are looking to scale up their projects and applications using our models, they often need assurances that their requests will be serviced with acceptable performance. This is especially critical for delivering real-time applications where customer service is paramount. Starting in August, Vertex AI will support provisioned, dedicated generative AI capacity that can deliver guaranteed throughput. This feature can be especially beneficial to customers who have a high volume of sustained workloads.  

Leverage generative AI while protecting data and privacy 
One capability enabled by Google Cloud is the ability to customize models using your own data. Vertex AI can help customers keep their data protected, secure, and private. When a company tunes a foundation model in Vertex AI, private data, model outputs, and prompts can be kept private, and they are never used in the foundation model training corpus. We recently published a whitepaper, “Adaptation of Large Foundation Models,” which outlines how we help protect customer data. 

Auditability and compliance are essential to helping ensure the security and privacy of customer data. We also engage in comprehensive GDPR privacy efforts, including our transparency commitments for customer data usage and the support for our customer’s Data Protection Impact Assessments (DPIAs). Now, we’re excited to support HIPAA compliance for many of our generally available models on Vertex AI, so that healthcare and life science customers with whom we have a Business Associate Agreement can run workloads with Protected Health Information (PHI) data on Google Cloud. 

Innovate responsibly 
Our AI Principles put beneficial use, user safety, and avoidance of harms above business outcomes and are embedded in how we develop our AI products. We’ve conducted extensive reviews on our generative AI products to identify potential risks and have developed guardrails to mitigate these impacts. For example, to address concerns around safety, we’ve implemented safety filters for bias, toxicity, and other harmful content. We also equip our customers with the tools they need to help reduce risk within their applications and provide recommendations to help navigate responsible AI. 

Bring the power of generative AI to your organization

With both a wide selection of foundation models and extensive, enterprise-grade platform capabilities, Vertex AI continues to unlock ways for your business or organization to access foundation models, tune them on your proprietary data, and leverage them for differentiated apps and digital experiences. To take the next step, visit our product page or reach out to our sales representatives to gain access to our latest capabilities.