“Cloud Wisdom Weekly: for tech companies and startups” is a new blog series we’re running this fall to answer common questions our tech and startup customers ask us about how to build apps faster, smarter, and cheaper. In this installment, Google Cloud Product Manager Rachel Tsao explores how to save on compute costs with modern container platforms.

Many tech companies and startups are built to operate under a certain degree of pressure and to efficiently manage costs and resources. These pressures have only increased with inflation, geopolitical shifts, and supply chain concerns, however, creating urgency for companies to find ways to preserve capital while increasing flexibility. The right approach to containers can be crucial to navigating these challenges.

In the last few years, development teams have shifted from virtual machines (VMs) to containers, drawn to the latter because they are faster, more lightweight, and easier to manage and automate. Containers also consume fewer resources than VMs, by leveraging shared operating systems. Perhaps most importantly, containers enable portability, letting developers put an application and all its dependencies into a single package that can run almost anywhere.

Containers are central to an organization’s agility, and in our conversations with customers about why they choose Google Cloud, we hear frequently that services like Google Kubernetes Engine (GKE) and Cloud Run help tech companies and startups to not only go to market quickly, but also save money. In this article, we’ll explore five ways to help your business quickly and easily reduce compute costs with containers.

5 ways to control compute costs with containers

Whether your company is an established player that is modernizing its business or a startup building its first product, managed containerized products can help you reduce costs, optimize development, and innovate. The following tips will help you to evaluate core features you should expect of container services and include specific advice for GKE and Cloud Run.

1. Identify opportunities to reduce cluster administration

Most companies want to dedicate resources to innovation, not infrastructure curation. If your team has existing Kubernetes knowledge or runs workloads that need to leverage machine types or graphics processing units (GPUs), you may be able to simplify provisioning with GKE Autopilot. GKE Autopilot provisions and manages the cluster’s underlying infrastructure, all while you pay for only the workload, not 24/7 access to the underlying node-pool compute VMs. In this way, it can reduce cluster administration while saving you money and giving you hardened security best practices by default.

2. Consider serverless to maximize developer productivity

Serverless platforms continue the theme of empowering your technical talent to focus on the most impactful work. Such platforms can promote productivity by abstracting away aspects of infrastructure creation, letting developers work on projects that drive the business while the platform provider oversees hardware and scalability, aspects of security, and more.

For a broad range of workloads that don’t need machine types or GPUs, going serverless with Cloud Run is a great option for building applications, APIs, internal services, and even real-time data pipelines. Analyst research supports that Cloud Run customers achieve faster deployments with less time spent monitoring services, resulting in reinvested productivity that lets these customers do more with fewer resources.

Designed with high scalability in mind, and an emphasis on the portability of containers, Cloud Run also supports a wide range of stateless workloads, including jobs that run to completion. Moreover, it lets you maximize the skills of your existing team, as it does not require cluster management, a Kubernetes skillset or prior infrastructure experience. Additionally, Cloud Run leverages the Knative spec and a container image as a deployment artifact, enabling an easy migration to GKE if your workload needs change.

With Cloud Run, gone are the days of infrastructure overprovisioning! The platform scales down to zero automatically, meaning your services always have the capacity to meet demand, but do not incur costs if there is no traffic.

3. Save with committed use discounts

Committed use discounts provide discounted pricing in exchange for committing to a minimal level of usage in a region for a specified term. If you are able to reliably predict your resource needs, for instance, you can get a 17% discount for Cloud Run (for either one year or three years), and either a 20% discount (for one year) or a 45% discount (for three years) on GKE Autopilot.

4. Leverage cost management features

Minimum and maximum instances are useful for ensuring your services are ready to receive requests but do not cause cost overages. For Google Cloud customers, best practices for cost management include building your container with Cloud Build, which offers pay-for-use pricing and can be more cost efficient than steady-state build farms.

Relatedly, if you choose to leverage serverless containers with Cloud Run, you can set minimum instances to avoid the lag (i.e., the cold start) when a new container instance is starting up from zero. Minimum instances are billed at one-tenth of the general Cloud Run cost. Likewise, if you are testing and want to avoid costs spiraling, you can set a maximum number of instances to ensure your containers do not scale beyond a certain threshold. These settings can be turned off anytime, resulting in no costs when your service is not processing traffic. To have better oversight of costs, you can also view built-in billing reports and set budget alerts on Cloud Billing.

5. Match workload needs to pricing models

GKE Autopilot is great for running highly reliable workloads thanks to its Pod-level SLA. But if you have workloads that do not need a high level of reliability (e.g., fault tolerant batch workloads, dev/test clusters), you can leverage spot pricing to receive a discount of 60% to 91% compared to regularly-priced pods. Spot Pods run on spare Google Cloud compute capacity as long as resources are available. GKE will evict your Spot Pod with a grace period of 25 seconds during times of high resource demand, but you can automatically redeploy as soon as there is available capability. This can result in significant savings for workloads that are a fit.

Innovation requires balance

Put into practice, these tips can help you and your business to get the most out of containers while controlling management and resource costs. That said, it is worth noting that while managing cloud costs is important, the relationship between “cloud” and “cost” is often complex. If you are adopting cloud computing with only the primary goal of saving money, you may soon run into other challenges. Cloud services can save your business money in many ways, but they can also help you get the most value for your money. This balance between cost efficiency and absolute cost is important to keep in mind so that even in challenging economic landscapes, your tech company or startup can continue growing and innovating.

Beyond cost savings, many tech and startup companies are seeking improved business agility, which is the ability to deploy new products and features frequently and with high quality. With deployment best practices built into GKE Autopilot and Cloud Run, you can transform the way your team operates while maximizing productivity with every new deployment.

You can learn if your existing workloads are appropriate for containers with this fit assessment and these guides for migrating to containers. For new workloads, you can leverage these guides for GKE Autopilot and Cloud Run. And for more tips on cost optimization, check out our Architecture Framework for compute, containers, and serverless.

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 apply for our Google for Startups Cloud Program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

Financial markets were among the first to adopt new technologies, and that has certainly been true of the derivatives markets, which were early adopters of electronic trading. Going forward, new capabilities will transform the way industry participants communicate, analyze, and trade.

I sat down with Google Cloud’s Phil Moyer and former SEC Commissioner, Troy Paredes, for a fireside chat at FIA Boca 2022 to discuss the future of markets and policy, the new technologies that are already paving the way for greater speed and transparency, and how cloud can help promote greater resiliency, performance, and security to enable the long-term vision for the market. The following is a summary of our discussion.

The current state of cloud technology

When it comes to technology adoption, we’re seeing the market and participants adopt cloud technologies, and increasingly, machine learning (ML) on a wider scale. Cloud technology allows for easier, faster, and much more secure experimentation with large datasets and ML.

A recent Google sponsored study by Coalition Greenwich (September, 2021) showed that more than 93% of trading systems, exchanges, and data providers are in some way providing services on the cloud. The same study, revealed that about 72% of the financial industry across the buy side and sell side, intend to consume public cloud-data based market data within the next 12 months.

Data-driven decision-making and risk management have always been, and continue to remain, the cornerstones of the financial markets. Over time, technology innovation has facilitated access to better insights from data, and therefore, better decision-making and the ability to manage risk. That expectation is now mainstream, and will continue to grow in sophistication.

The multi-phased technology trajectory

The movement of exchanges to the cloud will occur in a “crawl-walk-run” fashion, with low-hanging fruits the first to be picked in the near term while bigger, paradigmatic changes will occur over the medium and long term. Some organizations are starting all three stages simultaneously, understanding that each will move at an independent cadence.

The “crawl” phase is one in which foundations are built, starting with organizations moving data to the cloud and experimenting with some degree of analytics. It’s one of the most important phases because it’s where the opportunity to increase transparency and risk management takes shape.

In moving to the cloud, the infrastructure – which in the past relied on a combination of people, processes, and some technology – becomes the code that runs applications. This early phase is key to empowering organizations to shift to a cloud-based, agile-first operating model that makes it easier and more seamless to launch new products in the future, including by freeing up people and resources from IT management to more mission-focused work.

Establishing the cloud operating model simplifies the “walk” and “run” phases where compliance is more automated, latency-sensitive applications are more readily available, and the next generation of exchanges, market participants, and regulators is better prepared to meet future challenges.

The “walk” phase is where much of the innovation happens. Exchanges are making significant progress in leveraging foundational data decisions in the “crawl” phase and innovations in the cloud to improve settlement, clearing, risk management, collateral management, and compliance, and launch new products.

And finally, the “run” phase is where organizations will start to move the latency-sensitive markets to the cloud, as the markets increasingly will demand low-latency and high performance along with transparency and analytics to solve historical obstacles to market access.

Opportunities for both regulators and market participants

Any time significant technological change takes place, regulators explore its implications, particularly with respect to their ability to meet their regulatory objectives.

Increasingly, we are seeing technological change driving more opportunities for regulators and market participants alike. Such changes may also allow better protection of the marketplace, with greater integrity and transparency.

Over time, regulatory regimes – rules, regulations, statutes, interpretations, and guidance – will also adjust to new technologies, both benefiting the marketplace and advancing regulatory goals.

As one example, the cloud is increasing the ability to meet compliance obligations by allowing compliance to be built into transactions. Moreover, predicated on the vision of real-time regulatory reporting, and given the pace of technological change in the marketplace over the last several years, various regulators have been using more advanced analytics. This trend will continue to help them more effectively and efficiently meet their objectives, and monitor and meet the expectations they have for the entire market.

Machine learning’s role in the financial markets

Google Cloud’s head of AI and Industry Solutions, Andrew Moore, said that ML will be doing three key things for us in the next 10 years: giving us meaning, providing concierge services, and serving as a guardian. Extracting information that is critical to investor decision-making can be extremely important. With more data than ever, ML can increase the ability to process it while also becoming more accessible in the cloud and better supporting regulatory objectives.

The technology will likely manifest in trading and anti-money laundering activities as they relate market functions, as well as managing a wide variety of risks – supporting the interests of both investors and regulators in terms of decision-making, surveillance, and protections.

Rather than taking individuals out of the equation, the digitization of markets, assets, and guard rails combined with ML will allow people to focus their expertise in different ways to achieve key objectives.

Building the market foundation for the future

The goals of operational resiliency, security, and privacy will continue to be critical for building the market foundation for both participants and regulators. While technology promises to create advantages in concrete, tangible ways, it will be important to scrutinize potential risks and concerns.

Priority one for technology providers is to build an environment of trustless security, including encryption at motion and encryption at rest, ensuring that markets are operationally resilient while instilling confidence for any exchange that runs on top of that infrastructure. Multicloud architectures and approaches are likely also to be part of the solution for operational resilience.

Throughout time, liquidity has been the outcome of improved access, transparency, and security. Technology providers are responding by sharing both the responsibility for, and fate of, the markets of the future to build an efficient, faster, and more transparent and secure financial industry.

You can learn more about our approach in our newest white paper, Building the financial markets foundation for the future.

Organizations in every region and industry are developing APIs to enable easier and more standardized delivery of services and data for digital experiences. This increasing shift to digital experiences has grown API usage and traffic volumes. However, as malicious API attacks also have grown, API security has become an important battleground over business risk.

To help customers more easily address their growing API security needs, Google Cloud is announcing today the Preview of Advanced API Security, a comprehensive set of API security capabilities built on Apigee, our API management platform. Advanced API Security enables organizations to more easily detect security threats. Here’s a closer look at the two key functionality included in this launch: identifying API misconfigurations and detecting bots.

Identify API misconfigurations

Misconfigured APIs are one of the leading reasons for API security incidents. In 2017, Gartner® predicted that by 2022 API abuses will be the most frequent attack vector resulting in data breaches for enterprise web applications. Today, our customers tell us application API security is one of their top concerns, which is supported by an independent study from 2021 by Fugue and Sonatype. The report found that misconfigurations are the number one cause of data breaches, and that “too many cloud APIs and interfaces to adequately govern” are frequently the main point of attack in cyberattacks.

While identifying and resolving API misconfigurations is a top priority for many organizations, the configuration management process can be time consuming and require considerable resources.

Advanced API Security can make it easier for API teams to identify API proxies that do not conform to security standards. To help identify APIs that are misconfigured or experiencing abuse, Advanced API Security regularly assesses managed APIs and provides API teams with a recommended action when configuration issues are detected.

Advanced API Security identifies misconfigured API proxies, including the missing CORS policy.
APIs form an integral part of the digital connective tissue that make modern medicine run smoothly for patients and healthcare staff. One common healthcare API use case occurs when a healthcare organization inputs a patient’s medical coverage information into a system that works with insurance companies. Almost instantly, that system determines the patient’s coverage for a specific medication or procedure, a process which is enabled by APIs. Because of the often-sensitive personal healthcare data being transmitted, it is important that the required authentication and authorization policies are implemented so that only authorized users, such as an insurance company, can access the API.

Advanced API Security can detect if those required policies have not been applied, an alert which can help reduce the surface area of API security risks. By leveraging Advanced API Security, API teams at healthcare organizations can more easily detect misconfiguration issues and can reduce security risks to sensitive information.

Detect Bots

Because of the increasing volume of API traffic, there is also an increase in cybercrime in the form of API bot attacks—the automated software programs deployed over the Internet for malicious purposes like identity theft.

Advanced API Security uses pre-configured rules to help provide API teams an easier way to identify malicious bots within API traffic. Each rule represents a different type of unusual traffic from a single IP address. If an API traffic pattern meets any of the rules, Advanced API Security reports it as a bot.

Additionally, Advanced API Security can speed up the process of identifying data breaches by identifying bots that successfully resulted in the HTTP 200 OK success status response code.

Financial services APIs are frequently the target of malicious bot attacks due to the high-value data that is processed. A bank that has adopted open banking standards by making APIs accessible to customers and partners can use Advanced API Security to make it easier to analyze traffic patterns and identify the sources of malicious traffic. You may experience this when your bank allows you to access your data with a third-party application. While a malicious hacker could try to use a bot to access this information, Advanced API Security can help the bank’s API team to identify and stop malicious bot activity in API traffic.

API Security at Equinix

Equinix powers the world’s digital leaders, bringing together and interconnecting infrastructure to fast-track digital advantage. Operating a global network of more than 240 data centers with a 99.999% or greater uptime, Equinix simplifies global interconnections for organizations, saving customers time and effort with the Apigee API management platform.

“A key enabler of our success is Google’s Apigee, delivering digital infrastructure services securely and quickly to our customers and partners,” said Yun Freund, senior vice president of Platform at Equinix. “Security is a key pillar to our API-first strategy and Apigee has been instrumental in enabling our customers to securely bridge the connections they need for their businesses to easily identify potential security risks and mitigate threats in a timely fashion. As our API traffic has grown, so has the amount of time and effort required to secure our APIs. Having a bundled solution in one managed platform gives us a differentiated high-performing solution.”

Getting started

To learn more, check out the documentation or contact us to request access to get started with Advanced API Security.

To learn more about API security best practices, please register to attend our Cloud OnAir webcast on Thursday, July 28th, 2:00 pm PT.

Cloud developers — you’ve got it all. You can code in your choice of languages, enjoy portability with containers, minimize complexity with serverless, and manage the entire software lifecycle by following DevOps principles. But let’s face it, building and onboarding new cloud applications still requires a lot of manual planning, synthesis, and, yes, hard work. You need to research and plan your deployment, create a workable, secure architecture, and of course, you need to write the actual code,

For the last several decades, the cloud has been primarily a “do it yourself” model with volumes of options that have made development more complicated. The cloud went from overwhelmingly exciting to…. a bit overwhelming.

What if we all could bring that excitement back? What if you had some help that was available whenever and wherever you needed it? 

Say hello to Duet AI for Google Cloud

Powered by Google’s state-of-the-art generative-AI foundation models, Duet AI for Google Cloud is an always-on AI collaborator that provides help to users of all skill levels where they need it. With Duet AI, we’re on a mission to deliver a new cloud experience that’s personalized and intent-driven, and can deeply understand your environment to assist you in building secure, scalable applications, while providing expert guidance. 

As we evolve Google Cloud with Duet AI, we are looking to build a cloud platform that is more human-centric, holistic, and helpful, with responsible AI at the center of the experience:

• Human-centric: With Duet AI, we are making Google Cloud more accessible and personal to any type of user at any skill level by providing them with support whenever they need it, from code recommendations for developers, to prompt-based data insights for data engineers, to chat-based app creation for business users.
• Holistic: With generative AI at the center of the cloud experience, cloud development can be more cohesive, with fewer silos across functions, services, and tech stacks, providing a holistic picture in the format you want, wherever you are in Google Cloud.
• Helpful: To deliver smarter, contextual recommendations for building and operating apps with Google Cloud, we pre-trained Codey, one of the foundation models that powers Duet AI, with Google Cloud-specific content like documentation and sample code, and fine-tuned it based on Google Cloud  user behaviors and patterns. 
• Responsible: Our AI Principles set out our commitment to developing technology responsibly. Your code and recommendations will not be reused for any model learning and development. This helps ensure the privacy of your data and code, and also the integrity of the knowledge space from which our AI models are trained.

New capabilities available in Duet AI for Google Cloud

Here are some of the new capabilities available to get us started on our mission to deliver a new personalized and intent-driven cloud experience:

• Code assistance provides AI-driven code assistance for cloud users such as application developers and data engineers. It gives code recommendations as they type in real time, generates full functions and code blocks, and identifies vulnerabilities and errors in the code, while suggesting fixes.

Code assistance auto-generates code for creating a Google Cloud Storage bucket

Code assistance will be available through multiple products and services across Google Cloud, such as in Cloud Workstations, our fully-managed secure development environment, and other code-editing experiences in the Google Cloud Console. Developers will also find code assistance in Cloud Shell Editor or via our Cloud Code IDE extensions for VSCode and JetBrains IDEs. It supports multiple languages including Go, Java, Javascript, Python, and SQL.

• Chat assistance allows people to use simple natural language to get answers on specific development or cloud-related questions. Users can engage with chat assistance to get real-time guidance on various topics, such as how to use certain cloud services or functions, or get detailed implementation plans for their cloud projects. It can also provide architectural or coding best practices, helping to reduce the need to go searching for relevant documents.

Use chat assistance to get the detailed steps for deploying an app on Cloud Run

Chat assistance will also be available across multiple Google Cloud surface areas, for example IDEs, the Cloud Console, and through products and services. Whether you’re a developer, operator, data engineer, or security professional, you’ll be able to leverage chat assistance to help get more work done faster. 

Looking to optimize these features further for developers specialized in one particular area? With Generative AI support in Vertex AI, enterprises can fine-tune Codey using their own code base. They can consume these customized Codey models directly from Vertex AI today, and later this year, they will be able to connect it to the built-in Duet AI experience. And don’t worry, if you choose to train Codey with your code, your private data is kept private, and not used in the broader foundation model training corpus. You will have transparency and control over where data is stored and how or if it is used. 

• Duet AI for AppSheet will let users create intelligent business applications,  connect their data, and build workflows into Google Workspace via natural language.  With no coding required, users will be able to build apps by describing their needs in a chat guided by AI-powered prompts. This makes app creation accessible to more users, which can allow developer teams to focus their time on other high-impact work.

Create business applications with Duet AI for AppSheet using natural language

Experiment with Duet AI for Google Cloud today

We believe that having an assistant who is constantly evolving by your side will not only reduce an already overwhelmed developer’s workload, but also bring back the excitement of cloud development. With Duet AI, you can navigate the cloud with more confidence, ease, and — dare we say it — fun. 

And this is just the beginning. The future of the cloud experience that we are shaping with Duet AI is full of possibilities. We believe the future of developer productivity is more targeted personalized assistance. Check here to see our vision for Duet AI for Google Cloud – the redefinition of productivity in the workplace through unique end-to-end AI assisted technologies. 

These early features of Duet AI for Google Cloud are available today for limited users and we will be expanding access very soon. Sign up here to join Google Cloud’s AI Trusted Tester Program.