A new breed of cloud providers alters where companies perform their AI workloads. Neoclouds, companies that provide cloud infrastructure focused on AI training and inference, are shifting meaningful AI workloads from AWS and other hyperscale vendors. This entirely reshapes how companies make decisions on where to place their IT resources.
Aqlix helps companies address precisely this type of dilemma. With our services in cloud and DevOps, we assist companies in determining where AI workloads likely call for the use of neoclouds and where the existing hyperscale vendors still fit the bill.
Why Neoclouds Are Gaining Ground in AI Infrastructure
Neoclouds came into existence to serve the rapidly increasing need for AI compute, building infrastructure around the availability of many GPUs, rather than treating them as a part of a large offering. Neoclouds can optimize for both performance and cost, a structured impossibility for the general-purpose hyperscalers. Every design decision, from networking to the physical layout of the data center, is made with one workload in mind.
1. Purpose-Built Architecture for GPU Workloads
The hyperscalers built their infrastructure around general-purpose computers and then added support for GPUs as an offering, among many others. Neoclouds took the opposite approach, building infrastructure tailored around GPU clusters and high-speed networking.
This purpose-built design often provides better performance for AI training and inference. Aqlix takes into account these differences in architecture to assist our clients in determining the most appropriate placement of their respective AI workloads as part of a broader digital transformation strategy.
2. Faster Provisioning and Lower Compute Costs
When large AI training jobs are run by businesses on the traditional hyperscalers, problems begin to crop up, such as delays and capacity constraints during peak demand. Many neoclouds are able to provision GPU clusters much faster compared to the rest, sometimes even within minutes instead of the hours or days that others take.
Neoclouds keep prices lower for GPU hardware within the same ballpark as other hardware, as they are not covering the costs of running a large, broad catalog service. For businesses that train AI systems frequently and for extended periods, the savings add up quickly.
3. Meeting Surging Demand Hyperscalers Can’t Match
The growth in AI computing far outpaces the largest hyperscalers’ ability to build data centers. This has made neoclouds able to supply the GPU computing power, some dedicated infrastructure, and the power when the traditional hyperscalers and cloud vendors couldn’t.
The large AI labs and enterprises have no choice but to opt for neocloud contracts for their training capacity. This has created an environment where the neoclouds are now offering the competitive edge for project deadlines based upon the infrastructure offered.
What This Shift Means for Businesses Building AI
The neoclouds disrupting the field don’t mean hyperscalers will go away. They have simply created a genuine choice for businesses based upon the workload. Infrastructure choice means that a strategic decision must be made for each project with neoclouds and traditional providers. Most businesses rely on hyperscalers for compliance, maturity, and general day-to-day venue that the neoclouds have yet to offer.
1. Choosing the Right Infrastructure for Each Workload
Neoclouds prove to be an excellent choice for high-performance workloads, such as machine learning and GPU-intensive workloads. On the other hand, hosting general-purpose applications and customer-facing services and databases is better served by hyperscalers.
Businesses running both types of workloads increasingly split infrastructure accordingly rather than committing entirely to one provider. Aqlix’s Technology Consulting team helps businesses map workloads to the infrastructure that fits each one best.
2. Weighing Compliance and Enterprise Requirements
Compared to mature hyperscalers, neoclouds are relatively new and have not had time to catch up with the same breadth of compliance certifications and enterprise offerings. Businesses, which have strict enterprise requirements, should pay extra attention to this.
There is a strong case to utilize hyperscalers for compliance workloads, which process sensitive data. Neoclouds will have a greater opportunity to provide core enterprise solutions, and the trend will likely change in the future, but for now, it drives the majority of enterprise decisions.
3. Planning for a Multi-Cloud AI Strategy
There is a clear preference for a multi-cloud strategy by most businesses. Aqlix retains core workloads on trusted hyperscalers while using neoclouds for training workloads requiring many GPUs. These strategies require planning for movement of data between the different cloud providers.
We design this kind of infrastructure for clients to facilitate movement of digitized workloads without creating a heavy operational burden. Our AI development services help create this infrastructure in parallel with the development of actual AI.
Conclusion
Neoclouds give businesses more flexibility than ever when it comes to AI infrastructure. They provide businesses with a new option outside of general cloud computing services for optimization of GPU-intensive workloads. However, this is not another AWS alternative. Neoclouds provide a new option.
Aqlix IT Solutions offers businesses the resources to understand how and where neoclouds should be integrated into their structural and service offerings. Aqlix’s services can aid you in understanding alternatives to your AI workloads that may be dealing with constraints of cost and capacity.
Frequently Asked Questions
What is a neocloud?
A neocloud is a cloud infrastructure provider built specifically around GPU compute for AI training and inference. Unlike hyperscalers offering hundreds of general-purpose services, neoclouds focus exclusively on delivering high-performance GPU capacity.
Are neoclouds actually cheaper than AWS for AI workloads?
For a majority of the cases, yes, especially for GPU-intensive training. Neoclouds offer more cost-effective pricing due to the lack of a general service offering as most traditional hyperscalers do.
Should my business move all AI workloads to a neocloud?
This is a no. Many companies utilize both hyperscalers for their services and neoclouds for their AI training. Businesses utilize AI services in different ways depending on the business’s compliance, so neoclouds are utilized differently.
Are neoclouds suitable for businesses with strict compliance requirements?
It’s understandable. Neocloud services are fairly new and are likely still in their early stages of traditional compliance as hyperscalers. Companies with strict regulations should be cautious of which neoclouds they consider.
How can my business decide between a neocloud and a traditional hyperscaler?
Consider your workload types, cost sensitivity, and what your compliance requires. Aqlix helps their clients answer these questions to assist in the construction of a relevant architecture for AI and operational services.
