While the use of cloud infrastructure to build and deploy a wide variety of workloads is likely to continue, there are a number of emerging trends that will play an increasingly important role in 2023. The three below are not entirely newbut in 2023 they will become more established and significantly more widespread.

Cloud optimization takes center stage

Cloud optimization is all about ensuring that workloads run with the most efficient use of resources, both in terms of performance and cost.

With recession and inflation fears front and center in 2023, it’s up to businesses to cut costs wherever possible. Companies have largely moved to the cloud in recent years, realizing that a general cloud-based deployment is not always the best solution. This has led to the development of several cost control approaches and fine-tuned services to help optimize cloud-based workloads.

Cloud optimization is beneficial for any business that faces budget constraints and is therefore trying to get more out of less. “Organizations have, in some cases, become aware of the exploding costs of the cloud, in part due to technology tunnel vision, which sees moving to the cloud as just a technical migration exercise. Cloud optimization will shift from a mere focus on eliminating cloud resource waste to determining the most efficient way to optimize and allocate cloud resources.” , especially in distributed hybrid or multi-cloud cases,” said Axway’s Brian Otten.

Cloud optimization capabilities come in many forms. Microsoft, Google, and Amazon Web Services (AWS) all offer their own cost and optimization dashboards for organizations. Third-party services, including HPE GreenLake, also offer cost management features. Beyond specific services, the FinOps discipline offers guidance and best practices for managing cloud costs.

Cloud applications are moving to the edge

In the traditional approach to the cloud, services are centralized in public cloud service regions. With edge computing, compute and storage resources are brought closer to where the application is delivered. Edge service platforms, often JavaScript-based and increasingly using WebAssembly (WASM) technology, enable rapid deployment and delivery at the edge of the network.

It is more important than ever for organizations to provide the best possible experience for their users. Speed ​​and latency are key attributes that make this possible, and both are the primary promise of edge application delivery platforms. The introduction of WASM support now also makes edge applications even more attractive, as it allows developers to write in any number of languages, rather than having to code in just one programming language.

This is useful for users as they can access applications faster. Developers are also benefiting from the trend, as they can deploy applications more quickly in locations closest to users. Services for production deployment networks exist from several vendors. These include Cloudflare Workers, Fastly Compute@Edge, Vercel Edge Functions, Netlify Functions, and AWS Lambda@Edge.

The cloud looks beyond x86

Cloud providers are increasingly offering different instance types beyond those powered by Intel x86 CPUs. Cloud providers already have custom machine learning inference chips, GPU offerings, and Arm-based silicon.

Better efficiency and cost are the main reasons users look to non-x86 cloud instances. Also, for certain types of workloads, including artificial intelligence, x86 does not provide optimal performance, so organizations turn to custom silicon.

This trend benefits organizations that are not dependent on x86-based silicon and are looking for alternatives for better performance and lower costs. According to a Forrester research report, Arm microchips have enabled cloud providers to create their own chip development and supply chain, leading to cheaper and more power-efficient instance types. This helps cloud providers manage their operational costs while also helping customers meet their sustainability goals.

AWS offers its users a number of options, including Graviton’s Arm-based chips; Microsoft gives users access to Nvidia-based GPUs; and Google maintains the cloud-based Tensor Processing Unit (TPU) for optimized machine learning, reports ITPro Today.

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