Skip to main content

Cloud and Managed Services Spending Forecast

Gartner recently shared their top technology predictions. They said that increased transparency -- and the need to drive business value -- are bringing disruptive change to IT organizations in 2011 and beyond. One of their key findings: cloud computing will enable many organizations to exploit internal capabilities to establish new business service revenue streams.

Other informed industry analysts share a similar point of view.

Businesses are increasingly moving their computing and collaboration applications to the cloud, and their shift in IT spending reflects that change in behavior. A recent market study by In-Stat forecasts cloud computing and managed hosting spending by U.S. businesses will surpass $13 billion in 2014, up from less than 3 billion today.

“Although spending across all sectors and size of business is projected to grow, there are some segments where growth will be staggering,” says Greg Potter, Research Analyst at In-Stat.

Apparently, based on In-Stat’s assessment, the professional services and healthcare verticals will see the largest growth in spending on cloud computing services -- increasing at a rate of over 124 percent between 2010 and 2014.





In-Stat’s market study findings include the following:
  • Software-as-a-Service (SaaS) spending will increase 112 percent between 2010 and 2014.
  • Infrastructure-as-a-Service (IaaS) spending will approach $4 billion in 2014.
  • Platform-as-a-Service (PaaS) spending will increase 113 percent to roughly $460 million in 2014.
  • Small office/home office (SOHO) businesses are leading in the adoption of cloud computing services.

Popular posts from this blog

Why the Future of AI is Agentic but Precarious

We have now entered the AI Agentic era, according to the latest series of reports by Google's artificial intelligence (AI) researchers. The shift from passive generative AI models to autonomous AI agents that can plan, reason, and act on our behalf is the most profound digital transformation in decades. As  Applied-AI Initiatives replace deterministic code, a significant challenge has emerged. Building an AI agent is easy; however, trusting it is complex. The current AI market momentum reveals a stark last-mile gap. While a developer can spin up an AI prototype in minutes, roughly 80 percent of the effort required to reach production is consumed by the work of safety, validation, and infrastructure. The reason is simple: AI agents are non-deterministic. They can pass 100 unit tests but fail catastrophically in the field because of a flaw in their judgment, not a bug in the code. Core Architecture and the Problem-Solving Loop An Applied-AI agent is defined by the synergy of four co...