Cloud computing might have been around for more than 20 years, but there is still plenty to learn when it comes to making it work best for organisations today. And despite the best efforts of the industry, the rapid rise of AI technologies has added new layers of complexity to managing, migrating, and engineering cloud services.
Cloud cost management is now a top concern for organisations, driving the rise of FinOps and AI-driven AIOps to optimise cloud spending and performance. This is spurring significant investment in cloud management and automation tools—ironically, often increasing spend in the short term to reduce long-term operational costs.
At the same time, cloud engineering practices are evolving quickly. Cloud environments that were previously built on virtualisation are increasingly operating in containers or moving to serverless implementations as organisations seek to improve performance and workload portability.
Cloud engineering environments are evolving at a faster pace still, thanks to the rapid evolution of DevOps processes and tools, such as internal developer platforms. AI is playing an increasing role both in the development of software but also the provisioning and management of infrastructure. For many organisations, the future is one where AI will take on much of the hard work of infrastructure, as part of a movement that has come to be dubbed ‘infrastructure from code’.
One benefit of the cloud’s rapid learning curve has been that many organisations are now much more adept at assessing where a workload should reside to gain the best balance of performance and cost – including when that workload should be brought back on premises.
However, the desire to achieve this balance also comes at a cost, in terms of the tools and resources needed to best understand the options that stretch across hyperscaler providers as well as various models of private cloud services.
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