In 2013, long before the world was discussing clean energy and sustainable practices, two IIT Madras graduates — Swapnil Jain and Tarun Mehta — had an idea to develop India’s first-ever electrical scooter.

This was at a time when auto manufacturers were still focusing on fossil-fuel-driven vehicles and ‘eco-friendly’ mobility solutions were more a trendy alternative catering to a niche market.

The duo founded Ather Energy in 2013 and launched their first fully-electric scooter, the Ather S340, in Bengaluru in 2016. Since then, the company has released several new models into the market and is planning to expand to eight more cities by the end of the year.

To support the smooth running of their vehicles, lower costs, improve time to market, and create great customer experience, Ather turned to Google Cloud.

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Google Cloud Results

• Enables the speed and agility required to build open APIs
• Connects over 1 million customers through Apigee digital touch points
• Helps enable a range of digital banking and commercial partnerships
• 1billion API requests served annually

In 2016, Macquarie launched a new digital banking experience that was based on empowering customers, creating personalized experiences, and developing intuitive technology. Macquarie had the opportunity to build its digital environment from the ground up and looked beyond financial services to digital companies leading in customer experiences.

Following the launch of its digital banking platform in 2016, Macquarie saw providing customers with a secure way to manage their own data as the logical next step. Macquarie looked to transform its existing technology capabilities into a modern architecture that complements the speed and agility demanded of its digital platform. The Apigee API Management Platform plays an important role in helping Macquarie deliver a highly secure and open digital platform.

“The capability to connect to various platforms with a digital, responsive, technology-agnostic platform is vital. As new digital services emerge, it’s important that our digital banking services are future compatible. The most important part of our approach isn’t what we are doing now but what our platform architecture will allow us to do in the future by creating more human experiences with technology that go beyond just banking,” says Rajay Rai, head of Digital Engineering & Applied Innovation, for Macquarie’s Banking and Financial Services group.

Because Macquarie’s banking platform is based on an open API architecture, it is able to grant controlled access to its business services, enabling others to use, innovate, and build on top of them while increasing the prospects of widespread adoption and developer stickiness.

Empowering the developer

“Macquarie’s strategy has been API-first as it has built and improved its digital capabilities, but it won’t be too long until this approach is superseded by citizen-developers-first,” Rajay says. “We believe that co-creation of value is essential because in the future, we won’t be owning the channels for distribution and engagement. In building a leading digital banking platform it’s important that developers are able to open the front door.”

Macquarie’s API strategy grants internal and external developers with access to its rich repository of APIs exposed via the new developer platform, Macquarie devXchange. With Macquarie devXchange, developers have readily available samples, a sandbox, and simplified connections to all of the bank’s services. Developers are able to test APIs and services through the Apigee platform.

“Not only does the platform provide frictionless access, but it’s also poised to modernize and simplify the way we engage the community beyond our own perimeters,” Rajay says. “The Apigee developer portal is helping us seize new opportunities; access has been democratized and it wouldn’t have been possible without APIs.”

Cloud migration

In order to meet future demand for computing capabilities, Macquarie decided to move to the cloud in order to enable an infrastructure with various configurations on demand. This has cut the provisioning time for Macquarie from months to minutes.

Macquarie has created full end-to-end environments on Kubernetes and can flow traffic to a whole new environment in seconds, encouraging experimentation and learning. This was made possible through the flexibility of APIs.

“APIs and microservices are a great match. Microservices with Apigee provide a powerful, agile ecosystem to form various services topologies and evolve services in an isolated manner. This helps us respond to the fast pace of digital innovation today,” Rajay says.

Empowering consumers

Macquarie’s approach is about delivering customers more personalized banking experiences that are driven by how they want to use their information.

“APIs have enabled us to co-create value with our partners, customers, and developers. You can’t live in isolation; open source tells you that,” Rajay says. “APIs have been vital for us and what we can deliver for our customers as we’ve built our leading digital platform.”

Introduction

In our discussions with technology leaders from around the world for – The Digital Crunch Time: 2022 State of APIs and Applications – two themes emerged 

#1 Cloud — and hybrid cloud specifically — is becoming a driving factor of success with 59% of respondents saying they were looking to increase their hybrid cloud adoption within the next 12 months.

#2 82% of organizations with a mature API strategy and higher API adoption reported increased efficiency, collaboration, and agility. 

It’s clear that hybrid cloud – usually a combination of on-premise infrastructure (or private cloud) and a public cloud computing environment (like Google Cloud) – and APIs are becoming popular keys to success. The challenge now becomes – how to structure your teams and architect your platform to manage APIs at scale in hybrid environments?

In this two-part series, we’ll look at how successful organizations are managing APIs on a large scale across hybrid environments using Apigee. In this post we’ll look at how to structure the right team and set up the platform to help you thrive. In part 2 of this series, we will explore how to operate the platform with optimum clusters, scaling, and automation. 

Why is it difficult to manage APIs at scale in hybrid environments?

As your business grows, it makes sense that you will need to scale and build more APIs to keep pace. This can mean thousands of APIs with tens of thousands of transactions per second. And these APIs are often operated across completely different business units with many development teams. Like with any large scale operation, building a central governance across these hundreds of APIs is extremely challenging.

Operating APIs in a hybrid environment offers a number of benefits — less latency, compliance with regulations etc., — but to get the most out of them, you need a clinical and pragmatic approach to solving the two challenges outlined below. 

#1 How to structure your API teams?

One of the most successful patterns when structuring your API teams is a Center for Enablement team. This team is made up of many federated API producer teams throughout the enterprise with one central team of deep subject matter experts. This central team builds out the guardrails for the API program, develops reusable content, helps automate things like the CI/CD pipeline, and delivers best practices. 

Note that the level of implementation handled by the centralized team may vary. For example, they may have a full CI/CD pipeline to maintain, or they may simply provide templates for the federated dev teams to build their own pipeline. However implementation is coordinated, having this structure ensures a consistent API model with appropriate governance and security in place across the enterprise.

Another successful strategy is to reduce the size or redefine the role of the central teams over time. For example, one of our customers recently adopted a model where their largest business units are fully responsible for operating their own infrastructure using a GitOps model. In this case, the centralized team created the automation for installing and upgrading Apigee hybrid, but the business units themselves operated their own copy of the platform, allowing them complete autonomy. In this way, gradually increasing the autonomy for the federated teams will help avoid delivery bottlenecks while ensuring consistent governance.

You can visualize this change over time pretty easily using the following diagram.

#2 How to architect your platform? 

Another challenge is designing your Apigee organizations (top-level container that contains all your API proxies and related resources) and environments (software environment for creating and deploying API proxies). To tackle this, you first need to understand the relationship between different entities within an Apigee organization. Apigee organization is the top level entity – nothing shared between Apigee organizations. Using an organization in Apigee, customers can segregate resources and manage access to these resources based on their own requirements. In practice, this means that each Apigee organization has its own apps, API keys, developers, proxies, and so on. The diagram below provides a visual representation of how different entities in an Apigee organization interact with each other.

Another key factor to consider is the technical limits of the platform – designing around these limits leads to higher platform performance and stability.

In the design phase, it is vital to articulate fundamental requirements of your architecture and agree on these baseline principles with all the stakeholders involved. Some of these fundamental questions include:

• How many regions need to host runtimes?
  • Host runtime instances for organizations that handle production traffic in at least 2 regions to meet aggressive uptime SLAs. During upgrades, ensure only one of these regions upgrades at a time.
• What are the steps in your Software Development Lifecycle (SDLC) and how does Apigee fit into it?
  • Use Apigee organizations as the SDLC perimeter — such as a dev organization and a test organization — to give yourself the capacity for a large number of proxies. It is also common that the dev organization in this model has relatively permissive access for developers, allowing them to work more efficiently. 
• What level of access or separation do you need between business units or development teams?
  • Create separation between different teams by leveraging environments with conditional identity and access management to restrict access as necessary
  • If your team is very large and you plan to have multiple organizations, you may want to divide the operational ownership for these different Apigee organizations among different teams in your enterprise while keeping them operating under the centralized team.
    • In such a shared responsibility model, the largest consumers of the system stand up and operate their own instances. Teams with many APIs or distinct operational needs can use the automation from the central team to stand up and operate their own Apigee hybrid clusters. This reduces the burden on the centralized team while allowing business units to be self-sufficient and ensuring that the clusters are optimized to meet that team’s needs.
    • For very large scale programs where additional separation is necessary, consider creating separate organizations for each business unit, each of which has the full complement of SDLC orgs. Some customers have this level of scale, and often it makes logical sense to divide up into different Apigee orgs because it’s rare that 5000 APIs are all related and used together. 

By working off these baseline recommendations, you will come up with your own logical design of Apigee organizations and environments. Using Apigee organizations to represent your SDLC with business units — or functional areas or some other dividing mechanism — is a good way to design different environments.

For large scale deployments — involving thousands of proxies — we recommend dividing organizations into different logical business units while still following the SDLC model. In many cases, only the largest business units will need their own organizations, and it is possible to have a shared organization that contains multiple business units.

Conclusion

Like with any large scale IT project, there isn’t one“correct” way to operate APIs in a hybrid cloud environment. Finding the right approach for your enterprise starts with knowing the organizational requirements and tailoring Apigee for your use case. In the next installment of this series, we will explore how to operate Apigee with the optimal resources (clusters, automation, and monitoring etc.) for a large scale hybrid API program. 

Check out our documentation to learn more or start using Apigee hybrid.

The digital era turned the traditional formula for media and entertainment success on its head, ushering in new technologies that have changed how content is produced, distributed, experienced, and monetized. Audiences have more choice, flexibility, and power over what they consume, and today’s media companies have to embrace ongoing transformation or risk falling behind – or becoming irrelevant. 

A new wave of transformation is arriving with generative AI, a type of artificial intelligence that can interact with users in natural language and create novel data, ranging from story outlines, reports, and other text outputs to multimodal content like images, videos, and audio. Media and entertainment are inherently about content creation and creativity—so what does this new technology mean for the industry? 

At Google Cloud, we see tremendous opportunity for creative industries, from more efficient creation methods to improved user experiences. Let’s explore.

AI for media with Google Cloud

Google Cloud has a long history with large language models (LLMs) and other generative AI technologies—from their influence over the years on products like Document AI, to recent announcements like Generative AI support in Vertex AI, which lets businesses access and tune generative AI foundation models, and Generative AI App Builder, which lets developers build chatbots and other generative apps in minutes.  

We’ve helped our global media and entertainment customers with AI for personalization, search and recommendations, predictive analytics, and much more — and with generative AI now on the rise, we have some ideas to help media leaders, technologists, and creators think about and prepare to utilize powerful AI in their work. 

Three lenses on innovation in media

The media and entertainment industry is increasingly diverse and complex, with companies spanning over-the-top (OTT) subscription streaming services, 24-hour linear channels, live broadcasts of sporting events, digital journalism, traditional publishing, short-form user-generated social video, and more. More and more, the boundaries between these segments of the media industry are blurring — but common to them all is the focus on providing compelling content in an engaging audience experience that can be directly or indirectly monetized.

With this in mind, we suggest media and entertainment companies look at the application of innovative technologies like generative AI through the following three lenses:

1. Improving content creation, production, and management
2. Enhancing and personalizing audience experiences
3. Improving monetization

Improving content creation, production, and management

Generative AI democratizes many aspects of content creation, opening new ways to create written material, illustrations, sound effects, special effects, and more. Its recent maturation has been so rapid, some in the media industry have expressed concern that generative AI implies the end of creative professions. We think the opposite is more likely: just as photography, audio recordings, and computer generated images have enabled new modes of creativity, rather than making old ones obsolete, generative AI has the potential to both enable new forms of expression and enhance familiar ones. 

For example, journalists could use generative AI to speed up research by helping them synthesize and analyze large volumes of information, or to help them create initial drafts or summaries of editorial content. Film and television producers could leverage the technology to accelerate the post-production editing process, with new AI-enabled interfaces for rapidly adjusting or enhancing scene details such as lighting and color. Broadcasters could use generative AI to make vast libraries of video footage searchable and accessible for use in telling more compelling stories. The potential use cases go on and on.

Far from undermining incredible creative professions, generative AI is poised to free writers, artists, editors, and many others from the tedious and mundane aspects of their work, empowering them to focus more of their time on creativity.

Enhancing and personalizing audience experiences

Every media organization in the world today faces the reality that for most consumers, switching costs are extremely low. This puts incredible pressure on these companies to invest in delivering low-friction and compelling audience experiences that help mitigate subscribers from churning and viewers from abandoning content experiences for competitive platforms. 

Generative AI can help media companies engage and retain viewers, such as by enabling more powerful search and recommendations on their digital content platforms. With its increasingly multimodal capabilities extending from natural language to both audio and video content, generative AI is well-positioned to power more personalized audience experiences. 

Consumers often complain about “the paradox of choice” or their inability to find something interesting to watch on streaming platforms that have incredibly vast libraries of content available on demand. Imagine a not-too-distant future wherein a consumer can simply ask the content platform they’re using to help them find a specific show to watch based on mood, specific types of scenes, combinations of actors, award nominations, or practically anything they can think to ask. And that’s just the tip of the iceberg — imagine generative AI’s potential to curate, assemble, and even create personalized content for a viewer to consume!

Improving monetization

As consumers’ content consumption further expands from traditional theatrical and linear television programming to include digital offerings across an array of platforms, devices, and content types, media companies face the challenge of maintaining and improving monetization. The conventional economics and approaches to advertising and subscription models are proving, in many cases, not to deliver sufficient ROI. 

Generative AI has the potential to help media companies improve their monetization of audience experiences. As mentioned previously, enhanced personalization can play a role in mitigating churn, which in turn can help sustain and grow subscription and advertising revenues. Going beyond this, generative AI can be leveraged to drive even greater advertising revenues via more targeted, contextual, and personalized advertisements. Imagine both display and video advertisements that are generated on the fly to personalize product specifics, messaging, style, colors, and innumerable other characteristics to drive greater engagement and higher click-through rates (CTR), and thus higher advertising CPMs (cost per thousand impressions).

Coming up next

Generative AI presents a significant opportunity for media companies to fundamentally transform content creation, engagement, and monetization. Compelling services are already on the market — but there is far more to come. 

Google Cloud continues to build on its deep experience and expertise with AI, and we are committed to working with the industry to develop compelling, accessible, trusted, and responsible AI solutions that will drive meaningful business outcomes. We are excited to create the future together with our global media customers and partners across the ecosystem. To learn more about this disruptive topic, read “Debunking five generative AI misconceptions” from Google Cloud vice president of AI & Business Solutions Phil Moyer, or explore our Trusted Tester Program for generative AI.