Multi-Cloud refers to the ability of a software application to run on different cloud infrastructures (e.g., AWS, Azure, private cloud) without having to rewrite application code. The benefits of multi-cloud capability include significantly reduced development and maintenance costs, as well as increased business flexibility and resilience. Multi-cloud capability also allows organizations to more effectively future-proof their enterprise AI applications by giving them the freedom to easily move from one cloud provider to another.
An enterprise AI platform must support multi-cloud operation. For example, the platform should be able to operate on AWS and invoke Google Translate or speech recognition services and access data stored on a private cloud. It should also be possible for an instance of the platform to be deployed in-country – for example, on Azure Stack – so that it conforms to data sovereignty regulations. The platform must also support installation in a customer’s virtual private cloud account (e.g., Azure or AWS account) and support deployment in specialized clouds such as AWS GovCloud or C2S with industry- or government-specific security certifications.
Enterprise AI applications are generally designed to run on cloud infrastructure, whether a private cloud or, more frequently, a public cloud such as AWS, Azure, Google, or IBM, to take advantage of the vast, low-cost storage and compute resources available from these providers. Cloud vendors are innovating at a furious pace and are competing aggressively on price and features. Organizations therefore want and need the ability to run their enterprise AI applications in the cloud of their choice, and to easily switch cloud providers or swap out specific microservices from one provider for those from another.
Without multi-cloud capability, organizations lose the flexibility to easily and affordably change cloud providers or to leverage innovations from different cloud vendors as they become available. If an AI application does not support multi-cloud operation, it would have to be almost entirely rewritten to be ported from one cloud service to another, at significant cost, time, and complexity.
Multi-cloud capability optimally is achieved through a model-driven architecture. That is at the heart of the C3 AI® Suite – a complete, end-to-end platform for designing, developing, deploying, and operating enterprise AI applications at industrial scale. C3.ai’s revolutionary model-driven architecture provides an abstraction layer that enables the underlying cloud services used by an application to be declaratively configured in metadata, rather than explicitly hard-coded within the application. This means that enterprise AI applications developed on the C3 AI Suite – including all of C3.ai’s prebuilt SaaS applications – are multi-cloud-enabled out of the box.
Applications developed with the C3 AI Suite can run on any of the leading cloud infrastructures (for example AWS or Azure) or on private cloud infrastructure without having to rewrite the application code. This capability gives organizations maximum flexibility in choosing cloud service providers, and the ability to shift from one cloud to another without prohibitive costs or impact on application performance.