As the name suggests, multi-cloud involves reducing reliance on just one vendor, which can lead to benefits such as cost reduction. Analysts at 451 Research claim that enterprises can cut direct cloud expenditure by up to 74 percent with a multi-cloud approach.
There are also advantages around risk mitigation, while diversification helps avoid a new generation of lock-in in the cloud.
Of course, there are challenges in managing more than cloud provider. Moving applications between providers is not a simple task either, particularly for legacy applications.
According to 451 Research, the reliance on multiple cloud vendors will open doors for systems integrators and cloud brokers to help businesses to find the right provider.
Hybrid cloud gets boost with AWS and VMware partnership
One of the biggest cloud announcements in 2016 was the partnership between AWS and VMware. It was an acknowledgment that, for most enterprise businesses, moving all of their infrastructure to the cloud is unlikely to be a reality in the short or medium term.
For AWS, which has previously dismissed the "archaic" notion of on-premise private clouds, the deal enables it to tap into VMware's unparalleled presence in corporate data centrs. This provides it with hybrid cloud capabilities that it has lacked thus far - an area that main competitor Microsoft already excels in, and will expand on with the release of its Azure Stack software next year.
Hybrid cloud has been discussed for years, but the concept is now being taken a lot more seriously with the partnership between AWS and VMware and the realisation that on-premise data centres have life left in them yet.
Cloud giants woo developers with machine learning and serverless computing
During 2016 it was clear that the big cloud providers are switching their attentions from commodity cloud server and storage sales to more lucrative services such as machine learning.
The big cloud providers released new APIs this year that allow developers to tap into machine learning expertise. AWS, Microsoft, IBM and Google all provide services around natural language processing and image recognition. There are also open source tools such as Google's TensorFlow.
At the same time, AI personal assistants have gained mainstream attention - even if not in a positive sense, in the case of Microsoft's Tay bot - and more and more businesses are investigating how to use the technology. However, there are lots of other interesting applications for machine learning, and open source libraries such as TensorFlow are enabling companies like Ocado to create more efficiencies in their operations. Read next: Ocado to replace barcode scanning with AI 'vision' to speed packing processes
There has also been a focus on the underlying infrastructure to supercharge machine learning, with the leading vendors offering cloud customers access to GPUs and FPGAs targeted at AI use cases, while Google announced that it is creating its own TPUs [Tensor processing units]. In fact this is where Google - which is currently a distant third behind AWS and Azure - hopes to gain ground in the cloud market.
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