How to Overcome Multicloud Complexity

Managing multicloud environments brings greater cloud complexity and IT challenges, often slowing innovation rather than supporting it.

The early 2000s were landmark years for the cloud. In 2006, Amazon Web Services (AWS) hit the market and Google unveiled its first cloud service. Fast-forward to 2020, when the pandemic accelerated digital transformation efforts by about seven years, and the cloud became a business imperative. Not only did it enable the quick move to remote work, but it continues to be key in supporting business continuity and innovation. In fact, many could argue that the large-scale shift to the cloud in the 2010s was the pre-requisite to delivering the digital-first experiences that remote work and decentralized business demand today.

Now, multicloud and hybrid cloud environments are the new standard. Gartner says most organizations today adopt a multicloud strategy to minimize vendor lock-in or to take better advantage of more modular, best-of-breed solutions.

But managing multicloud environments brings greater cloud complexity and IT challenges, often slowing innovation rather than supporting it. One 2022 report found that the average multicloud environment spans five different platforms — including AWS, Microsoft Azure, Google Cloud, IBM Red Hat and more.

Overcoming Complexity in a Multicloud World

Today, cloud complexity makes the jobs of operations teams considerably harder. As they work to juggle increasingly distributed applications and data with platform-specific nuances, that make maintaining visibility, performance and security at scale difficult. This complexity is only further compounded by the introduction of cloud-native technologies such as containers and microservices that are more dynamic and ephemeral in nature. Over time, this becomes a significant drain on resources and all too often prevents teams from focusing their time on driving innovation, improved customer experiences and in turn, propelling better business outcomes.

While native tooling from cloud service providers and hyperscalers are available to help teams monitor their environments within those platforms, they provide little relief for teams managing applications across multi-cloud environments. This results in “tool sprawl,” or developer teams having to manage and monitor complex, diverse multicloud ecosystems with disparate toolkits. In fact, on average, organizations rely on seven different infrastructure monitoring solutions to manage multicloud environments. As a result, 57% of IT leaders say this use of multiple monitoring solutions makes it challenging to optimize infrastructure performance and resource consumption.

What’s more, 56% of IT leaders agree that traditional infrastructure monitoring solutions are no longer fit for a world of multiple clouds and Kubernetes containers. In response, organizations must find a way to reduce manual processes for infrastructure teams and refocus their time on strategic work that delivers new, high-quality services for customers. That’s where AI and automation can help.

Observability, AI and Automation are the Answer

While cloud services can enhance overall efficiency, especially for developers, an absence of the right tools can create major gaps in productivity. Nearly half (42%) of IT teams’ time is spent on manual, routine work to keep the lights on across infrastructure environments. For example, teams are often forced to switch between different solutions and dashboards to gain insights.

Traditional cloud monitoring tools can’t address dynamic multicloud environments, but automatic intelligence gets to the heart of cloud performance and security issues best. To start, organizations need end-to-end observability to understand the big picture. Add to this capability automation and causal AI and teams can get the precise answers they need to better optimize their environments — freeing them up to focus on accelerating innovation and driving better business outcomes.