Cloud vs On-Prem: The Choice is Yours
When digital television was introduced in the United States, networks waited fifteen years to switch off their analog signals. Smartphone applications support generations of operating systems for years. Even leaded gasoline wasn’t phased out overnight.
Yet when it comes to enterprise technology, many software solutions have gone ‘cloud-only’ or ‘cloud-best’, telling customers that if they want the latest and greatest features, they need to rely on their cloud-based services. For some companies that have made the transition, this is a simple shift. And, for the providers themselves, the move to cloud offers cost and elasticity advantages to improve their business.
But, the push into cloud overlooks an important fact: not every business is ready for cloud. And, not every business will go ‘cloud-only’. Many cloud implementations are a combination of public and private, cloud and on-premise, built to meet the varying needs of each enterprise and the environment in which they operate.
When it comes to insight engines, many of the advancements have been restricted to cloud-only applications. Speed, AI, natural language processing: these are all features that businesses demand and can benefit from, but if they are cloud-only, they rely on those businesses having their information in cloud environments.
Businesses may not have that option. In highly-regulated industries, there are often serious restrictions on the type of data that can live in the cloud. Under different privacy laws, certain datasets need to be stored locally and cannot touch infrastructure not directly owned by the company. As a result, many companies have either built on-premise solutions or have developed complex combinations of cloud and private infrastructure.
In these situations, businesses should not have their cloud preferences (or requirements) impact their access to the insight they need to grow. They need an insight engine that can fit their needs, rather than change their environments to access new technologies.
Insight engines, like all enterprise technology, cannot be one-size-fits-all. It needs to be adaptable and flexible to the user, particularly when it comes to cloud – because cloud is not for everyone.
Latest Blogs
How MCP Connects AI to Your Business Information
The Model Context Protocol, commonly called MCP, provides a standard way for AI applications to connect with external information and tools. It gives assistants and agents a consistent way to request help from systems outside the AI model.
How an AI Agent Uses MCP to Complete a Business Task
A customer reports that a production system has failed for the third time in two months. A service employee needs to review the customer’s coverage, understand the earlier incidents, find current technical guidance, and decide whether the issue should be escalated.