AI reshapes cloud spend as firms shift from SaaS to agents

Cloud and security executives expect enterprises to overhaul how they buy and secure software in 2026, as artificial intelligence reshapes budgets, risk models and operational technology.

Senior leaders from Cloudflare and Rackspace Technology forecast a decisive shift away from seat-based software subscriptions, a rise in domain-specific AI and new approaches to industrial security built on agentless zero trust.

Mono cloud shift

Ramy Houssaini, Chief Cyber Solutions Officer at Cloudflare, said boards were beginning to see limits in centralised cloud and traditional software-as-a-service (SaaS) models as AI demand grows at the edge.

"The traditional SaaS model-defined by static features and centralized data silos-is nearing its end. Enterprises are now demanding AI-native, real-time, context-aware services. 2026 will accelerate the shift from "application consumption" to AI-as-a-Service. Organisations will prioritize deploying domain-tuned models at the edge, keeping sensitive data local, and paying for intelligence over software seats. While SaaS won't disappear, its dominance ends as AI agents become the primary interface for enterprise workflows." 

The comments reflect a broader move by large organisations to distribute compute and data across multiple clouds and edge locations. Security and compliance teams are weighing the benefits of local processing against centralised control as AI use cases expand.

AI over seats

Houssaini expects enterprises to reassess the economics of software, moving from per-user licences to usage-based models tied to AI outputs.

"The old way of buying software (SaaS) meant paying a monthly fee for every employee ("seats") to use a set of fixed features, with all their data locked in a central silo. That model is breaking. In 2026, the focus will shift to AI-as-a-Service. Companies will demand software that is smart, real-time, and customised. They will pay for the actual intelligence and insights the AI provides, not the right to use the program. This move pushes smart AI assistants to the forefront and requires keeping sensitive data secure and close to home. While SaaS won't disappear, its dominance will end as AI agents become the primary interface for enterprise workflows," said Houssaini.

Such a change would affect how vendors recognise revenue, how CIOs structure contracts and how finance teams forecast technology spending. It may also push providers to expose more granular metering and transparency around how AI models consume compute and data.

Industrial AI push

Cloudflare also anticipates that AI will spread more deeply into industrial environments and operational technology (OT), from factories to utilities.

"Think of old factories and power plants (Operational Technology or OT) like a house alarm-they only react after something breaks. In 2026, Industrial AI changes that. AI models will constantly run the show, tuning machines and optimising systems in real-time, moving from watching to driving operations. This sudden level of automation requires a huge security upgrade. Since you can't install security software on every single robot or sensor (the IoT devices), security needs to become invisible. We'll see a massive switch to a new security model called Agentless Zero Trust, which checks the identity of every machine interaction instantly and automatically, making the whole network fabric the trusted security guard for automated equipment," said Houssaini.

This approach relies on network-level inspection and policy enforcement rather than endpoint agents. It aims to cope with large numbers of constrained IoT devices that are hard to patch or monitor individually.

Spending governance

On the infrastructure side, Rackspace Technology expects organisations to tighten control over AI-related cloud spend as they scale pilots into production.

Adhil Badat, Managing Director at Rackspace Technology, said technology leaders were moving from experimentation to operational discipline. "As we head into 2026, businesses are moving past the hype and into the hard work of making AI practical, affordable, and trustworthy. Here are three big shifts I see coming: Subscription fatigue and multi-cloud complexity will drive a new era of consumption governance. AI workloads are creating budget headaches across cloud platforms, with costs now measured by usage, tracking every query and every response, rather than simple monthly subscriptions. The old budgeting playbook doesn't work anymore. Companies will need real-time dashboards to track usage and spending, and even monitor carbon emissions, because sustainability is now part of the cost equation."

"Domain-specific AI models will eclipse generic LLMs. Large language models (LLMs) were a great starting point, but they're not enough for highly regulated or knowledge-heavy industries. We're entering the era of Domain-Specific Language Models (DSLMs) that deliver better accuracy and compliance. The trade-off? They require cleaner data and stronger governance, so expect big investment in fine-tuning and bias monitoring."

"Third-party AI risk will force procurement models to evolve. AI is now baked into almost every vendor product, which means risk extends beyond software bugs to how models behave over time. Procurement teams will need to shift from simply buying software to also buying intelligence. Contracts will include clauses for bias, model drift, and data use, and risk frameworks will evolve to match," said Badat.

Badat's comments underscore how AI adoption is intersecting with sustainability metrics and risk management, bringing new stakeholders, such as procurement and compliance, into cloud decision-making.