Businesses will likely spend money on using the cloud for artificial intelligence and other new ways to provide positive customer experiences.
An April forecast from Gartner expects public cloud service end user spending to grow 21.7% to total $597.3 billion in 2023, up from $491 billion in 2022. Emerging technologies like generative AI, Web3 and the metaverse (or virtual reality) are among the factors driving increased use of public cloud services, Gartner found.
Gartner sees a smooth climb to 2026, by which time they predict 75% of organizations will use a digital transformation model based on the cloud.
Public cloud spending growth broken down by sector
Gartner broke public cloud spending down into several sectors.
- Infrastructure-as-a-service is expected to experience the highest end-user spending growth in 2023 at a 30.9% increase from 2022.
- Platform-as-a-service is expected to grow at 24.1%.
- Software as a service, the largest segment of the cloud market by user spending today, will continue to grow at a rate of 17.9% to $197 billion in 2023.
Sid Nag, vice president analyst at Gartner, said providers on the application layer hear that customers want to “redesign SaaS offerings for increased productivity, leveraging cloud-native capabilities, embedded AI and composability.”
Economic uncertainty may also be a factor in today’s buying decisions, but don’t seem to have slowed down cloud investments in particular.
The Wasabi 2023 Global Cloud Index Storage report in January pointed out that 84% of the the organizations it surveyed plan to increase their public cloud spending in 2023. Many organizations (70%) already store their global storage capacity in public and dedicated clouds.
The main drivers of uptick in cloud spending
“The next phase of IaaS growth will be driven by customer experience, digital and business outcomes and the virtual-first world,” said Nag. “Emerging technologies that help businesses interact more closely and in real time with their customers, such as chatbots and digital twins, are reliant upon cloud infrastructure and platform services to meet growing demands for compute and storage power.”
SEE: How does ChatGPT work?
Digital transformation is still an ongoing concern; many organizations that have not completely made the digital switch want to start out on the cloud.
“Web3 and metaverse ultimately require a massively scalable infrastructure to run on in order to provide the end user experience for applications that leverage these technologies,” Nag told TechRepublic via email.
Tech buyers “should calibrate their cloud spend based on how extensively they plan to leverage these technologies for their applications and workloads,” he said.
Technological developments changing buying decisions
Organizations are looking for technology that can:
- Optimize their operations or the trust they show to their customers.
- Scale their solutions or product delivery.
- Pioneer new audience interactions or business opportunities.
All of these features could be served by software hosted in the cloud, depending on the company’s needs.
“Focus on the move to digital. Don’t hunker down,” Nag advised. “Seize the opportunity to get a leg up on the competition by carefully increasing cloud spend in targeted areas in a prescriptive manner to drive their digital transformational initiatives – both externally from a B2B and B2C perspective, as well as modernizing … internal IT by embracing digital technologies and transformational models.”
With many tech purchases being made by business leaders outside of IT, who is doing the buying can sometimes be hard to predict. However, IT often has a hand somewhere in the process, and is therefore still a key audience for tech buyers.
The effect of generative AI on public cloud spending
Generative AI is now a major factor in companies’ tech buying decisions. It takes a lot of computing power, and also faces a barrier to trust. Privacy is a major concern around generative AI, but it doesn’t behoove organizations today to pause if they plan to implement it, Gartner’s VP Analyst Avivah Litan said.
Since hyperscalers are best equipped to handle the infrastructure for large language models, they are keeping an especially close eye on developments in that space.
“Hyperscalers are positioning themselves for [large language models and foundation models] as they continue to enhance their cloud capabilities to support the needs of these AI models,” Nag said.