Unlocking Potential, Managing Risk: The Cloud AI Conundrum

Imagine being able to tap into AI services without needing expensive infrastructure or software — just like borrowing a garden tool from a neighbor. A cloud artificial intelligence (AI) service works this way. It is a pay-as-you-go service that lets companies access advanced AI capabilities over the internet to do such things as develop AI applications, analyze data and other tasks.

What Is the Cloud?

The “cloud” refers to computers — or servers — software programs and networks owned by third parties that purchase, store, maintain and rent out their use to clients. That means businesses do not have to buy, store and maintain these servers and software programs themselves.

Instead, companies access them over the internet. They only pay when they use these systems. This can be cheaper than businesses buying the hardware and software themselves as well as maintaining them. Since one only needs an internet connection to access the cloud, anyone on any device can use the apps and data stored in the cloud.

It is called the cloud because early on network engineers used to draw a cloud-shape when they needed to come up with a diagram — but didn’t know what devices were on what network and how these connected to the internet. Cloud was a symbol for “unknown domain.”

Traditionally, companies own and manage the servers and software themselves, either in their own data centers or lease space in third-party data centers. This is called on-premises infrastructure or on-prem.

While the idea for cloud computing harkened back to the 1960s, it wasn’t until 2006 that Amazon pioneered modern cloud computing with the introduction of a virtual computing environment in Elastic Compute Cloud (EC2) and Simple Storage Service (S3).

There are different types of clouds: public clouds (shared among organizations), private clouds (dedicated to one organization), hybrid clouds (combining public, private or even on-prem) and others.

The Rise of AI in the Cloud

As AI gained traction, so did its residency in the cloud. Building AI models often requires significant computing power and large datasets, which big cloud companies like AWS, Microsoft Azure and Google Cloud can offer but would be complex and expensive for many companies.

By offering access to AI models in the cloud, companies can budget their AI expenses through the cloud’s pay-as-you-go model. Cloud AI services make advanced AI tools available to everyone, from startups to multinationals.

Here are major cloud service providers that have developed robust platforms that support AI development:

Amazon Web Services (AWS)

AWS is widely recognized as a pioneer in modern cloud computing. It offers a comprehensive suite of AI and machine learning services. One notable service is Amazon SageMaker, which allows developers to build, train, and deploy machine learning models quickly and efficiently.

AWS also offers specialized AI services like Amazon Rekognition for image and video analysis, Amazon Comprehend for natural language processing and Amazon Lex for building AI chatbots.

In generative AI, Amazon Bedrock is a fully managed platform for building and scaling applications. It offers more than 100 AI models, including Anthropic’s Claude, Cohere, Meta’s Llama, Mistral AI and others.

Microsoft Azure

Microsoft Azure provides several AI tools and services. These include Azure Machine Learning, a cloud-based environment for developing and deploying machine learning models.

It also offers Azure AI Services, a collection of pre-built APIs that enable developers to add capabilities like speech recognition, text analytics and image processing to their applications without needing deep AI expertise.

Microsoft provides exclusive access to OpenAI’s AI models, which power ChatGPT, through its Azure OpenAI Service.

Google Cloud Platform (GCP)

Google Cloud is known for its AI research and development. Its tools, such as TensorFlow (an open-source machine learning framework) and AutoML, allow both beginners and experts to build custom machine learning models. Google’s AI services also include pre-trained models for vision, language, and structured data analysis.

Google’s Vertex AI is its fully managed artificial intelligence platform for building and using generative AI apps. It offers access to more than 160 foundation models.

Other Cloud AI Providers

Other notable providers include IBM Cloud, Oracle Cloud and smaller niche platforms that cater to specialized needs.

Pros and Cons of Cloud AI Services

Using cloud-based AI services benefits companies in the following ways:

Cost Efficiency: No need to invest in expensive hardware or maintain dedicated AI infrastructure.

Accessibility: Advanced AI tools are available to businesses of all sizes, leveling the playing field.

Flexibility: Companies can choose from pre-built models or develop custom solutions tailored to their specific needs.

Speed: Rapid deployment and scaling allow businesses to innovate faster and respond to market changes quickly.

Maintenance-free: The cloud provider manages the underlying infrastructure, including updates and security patches.

The downsides of using cloud AI services include:

Vendor lock-in: Once a company has chosen a cloud vendor, it is locked into the pricing, services and policies. Switching to another cloud provider is difficult and can be costly.

Pay as-you-go costs can add up: High volume or complex tasks may mean that a company will need to use services more than expected.

Data privacy and security: A company’s data is processed externally and it is dependent on the cloud provider to keep it secure.

Network dependency: If a cloud provider’s network goes down, so will a company’s access. Latency is another factor as well.

Limited control and flexibility: The cloud provider owns, manages and monitors the infrastructure.

For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.