That inference workload specifically is driving demand for purpose-built hardware, companies like d-Matrix design AI chips focused on cutting the cost and latency of running large models at scale, as distinct from the hardware used to train them. Google Cloud offers Vertex AI plus its TPU infrastructure, which tends to be the most cost-effective option for large-scale training workloads, though GPU and TPU capacity constraints have been a real, recurring issue across all three providers through 2025 and into 2026. The expansion reflects mounting pressure on enterprises to maintain real-time financial oversight as AI and high-performance workloads drive cloud spend higher. Some 58% of all respondents in the survey of 501 IT executives worldwide expect a slight increase in cloud spending.

Also, IoT use cases involving factory equipment and autonomous vehicles could require running workloads outside the centralized infrastructure to reduce latency and lower AI training costs. Key benefits include cost efficiency through a pay-per-use model, simplified operations as cloud providers handle infrastructure, automatic scaling to meet demand and suitability for event-driven architectures. Today’s tech leaders need cloud infrastructure that can support AI/ML workloads, and they’ll use other cloud providers beyond their primary cloud provider to get what they need. Companies are moving specific workloads back to private clouds or colocated hardware to achieve better long-term economics, creating a dynamic, permanent hybrid state. Organizations are discovering that certain stable, resource-intensive workloads (like training AI models) can be prohibitively expensive in a pay-per-use public cloud.

For many mid-sized businesses, a “primary cloud plus targeted secondary use” approach (like running DR or a specific analytics workload on a second provider) delivers most of the resilience and negotiating benefits without the full operational overhead of running production workloads identically across two clouds. European organizations largely mirror global trends, including increased use of FinOps teams (up nine points) and a strong preference for Azure for significant workloads. This approach yields numerous benefits, such as increased flexibility, access to specific features of different cloud environments, and no fear of vendor lock-in.

Quantum computing is predicted to be one of the most critical cloud computing trends, enabling businesses to solve complex problems that traditional computing cannot address. The increased — or increasing — capacity is one of a few cloud computing trends that has started to take its toll on the ecology of the areas where they are set up. If your workloads involve delivering services over the internet, chances are cloud infrastructure is already a key part of your system.

of organizations are running over half of their workloads in the cloud (Source: Fortinet)

As we approach the new year, it’s essential to take a closer look at the cloud computing trends in 2025 and cloud computing statistics that will shape the future of the industry. Insight into cloud computing trends among technology decision-makers, including usage and plans across multiple cloud services, investments, business drivers and shifting services and applications to the cloud. As AI becomes deeply embedded across engineering organizations, teams are increasingly using software engineering approaches such as infrastructure as code, reusable components, platform engineering, and policy automation to standardize how AI infrastructure is deployed, scaled, and secured across clouds.

Evolving Cloud Cost Optimization Metrics

NetSuite offers all solutions through a SaaS model, meaning customers run their applications over the internet without having to buy, install, or manage IT infrastructure. This is not all about increasing business reliance upon cloud; uptime of critical environments is increasingly becoming a requirement of industry bodies, regulators, and customers. Sustainability initiatives saw a modest increase as regulations shift, with 47% of European respondents reporting defined programs. Nearly half plan to expand into AI consulting or SaaS management, while enterprises increasingly rely on them for specialized support.

Serverless computing and edge architectures are changing how organizations deploy and pay for cloud workloads. Most companies reported that as their cloud spend increased, so did their cloud waste. Also, expect increased integration of AI in cloud management, Avila said, to enable better automation, cost optimization, data analysis and IT support in decision-making, predicting trends and personalizing experiences. Generative AI increases cloud costs by introducing dynamic, harder‑to‑predict workloads and new pricing patterns that affect forecasting and budgeting.

AI and ML: The new cloud workload

• The scalability is unmatched; servers automatically scale with demand, permitting companies to efficiently handle variable workloads while only paying for actual usage, thus significantly reducing operational expenses.
• As organizations scale both traditional cloud workloads and AI-driven systems, IaC has become critical for achieving secure, repeatable, and high-velocity operations across every environment.
• For many mid-sized businesses, a “primary cloud plus targeted secondary use” approach (like running DR or a specific analytics workload on a second provider) delivers most of the resilience and negotiating benefits without the full operational overhead of running production workloads identically across two clouds.
• This approach yields numerous benefits, such as increased flexibility, access to specific features of different cloud environments, and no fear of vendor lock-in.
• To support this shift, Pulumi’s perspective on Superintelligence Infrastructure explains why AI workloads, from pre-training to inference at massive scale, require dynamic infrastructure orchestration rather than static configuration.
• European organizations largely mirror global trends, including increased use of FinOps teams (up nine points) and a strong preference for Azure for significant workloads.

Despite the increase in FinOps maturity placing focus on value, this year’s report shows cloud-based AI workloads are surging causing an increase in wasted cloud spend (29%) for the first time in five years. Teams aren’t just looking at what cloud costs add up to, but they’re also looking at what cloud delivers, and they’re shaping decisions long before workloads hit the cloud. Edge computing can help you unlock many benefits, including reduced latency, increased productivity, remote data collection, improved data security and compliance, and https://chickencoopplansmanual.com/simple-chicken-coop-plans-learn-how-to-easily/using-tensorflow-on-the-raspberry-pi-inside-a.html reliable performance. Today, we are at the cusp of a new era of cloud computing, firmly supported by a series of innovative and powerful cloud computing trends such as serverless computing and multi-cloud adoption. The Everest Group survey highlights that many current automation-driven FinOps tools are not meeting expectations, creating a rising demand for a more comprehensive and integrated approach.

Cloud cost optimization will continue to be a critical topic in boardroom discussion

Cloud spend scales with organization size, with large enterprises dominating $2M+ monthly spend while SMBs remain concentrated below $50K. This shift reflects a more proactive and mature approach, as FinOps teams move from simply explaining past spend to shaping architectural and investment decisions early in the process. Through our AWS consulting solutions, we help customers understand the critical role the cloud can play in their digital transformation programs. The first trend on our list of most promising cloud computing trends is the increasing usage of serverless computing. Cloud computing allows companies and individuals to access computing resources, such as storage, databases, and software, via the Internet and pay for only the resources they consume. “This shift is being driven by an increased sophistication of attacks, the rise of generative AI threats, and growing regulatory pressure to protect private information.”

Today, over 94% of enterprises use cloud services, with half of all workloads now running in public cloud environments. Application depth and integration expertise support expansion, while budget overruns and localization requirements can postpone the workloads that would use additional capacity. This virtualized approach offers unparalleled advantages, including scalability to meet fluctuating demands, universal accessibility from any device, and cost-efficiency through a pay-as-you-go model. Serverless computing, one of the common top cloud computing trends that enables developers to run code without managing servers, paying only for compute time used. Also, expect increased demand for cloud security skills that cover tooling and processes, said Matt Middleton-Leal, managing director for EMEA North at cloud security and compliance platform provider Qualys.

The Cloud Adoption Statistics

The scalability is unmatched; servers automatically scale with demand, permitting companies to efficiently handle variable workloads while https://pagemakers.net/leveraging-technology-for-business-growth/ only paying for actual usage, thus significantly reducing operational expenses. As organizations scale both traditional cloud workloads and AI-driven systems, IaC has become critical for achieving secure, repeatable, and high-velocity operations across every environment. To support this shift, Pulumi’s perspective on Superintelligence Infrastructure explains why AI workloads, from pre-training to inference at massive scale, require dynamic infrastructure orchestration rather than static configuration. It becomes a direct concern if you’re fine-tuning models, running significant inference workloads in-house, or building products where AI features are central enough that latency and cost at scale matter. The trend in 2026 isn’t “Kubernetes is dead,” it’s “use the simplest thing that meets your requirements,” and for a growing share of workloads, that’s serverless containers.