AI Inference Strategy: Cloud vs On-Prem vs Neo-Cloud?

Artificial intelligence has become a crucial part of modern businesses. But the question that might always keep organizations up is, where should this AI inference be located? The answer isn’t simple as it highly impacts scalability, security, and performance.

Cloud, on-premises, or mix, i.e., neo cloud. Choosing between them is a challenge. Nevertheless, each platform has its own unique benefits and risks, which you must understand to make better decisions. This blog breaks down the right AI inference strategy, i.e choosing between Cloud vs On-prem vs Neo Cloud. Let’s run through it.

What is AI Inference?

AI inference is basically using trained machine learning models to make predictions on new or unseen data. Consider it in a way, of teaching AI a new skill. It’s an instance of an AI model that generates output from the latest data it takes, such as text, photo, etc. This is the scenario of AI inference.

Let’s understand AI inference with an example: When you ask a voice assistant to set a work reminder, AI processes your voice, understands the purpose, and delivers results accordingly. The process of identifying a new voice is known as inference.

Choosing Between Cloud vs On-Premises vs Neo Cloud

Choosing the right AI inference strategy is an important decision, as it affects many parameters. Evaluating the deployment models can help you make well-informed, focused, and future-proof decisions.

Cloud (AWS, Azure, and Google Cloud)

Public cloud platform offers you a platform to start with your AI workloads. It offers the infrastructure of third-party cloud providers such as AWS, Microsoft Azure, and Google Cloud, and the pay-as-you-go model has become the first choice for most businesses.

Advantages

• Elasticity: Can scale resources up and down easily to match the changing demands of workloads.
• No Upfront Cost: Public cloud services follow a pay-as-you-go model, allowing businesses to cut down on the capital cost and get started on the go.
• Faster Deployment: Cloud providers offer ready-to-use AI services, application programming interfaces, and machine learning frameworks that enable more rapid development and deployment.
• Global Accessibility: Cloud platform supports remote teams and distributed applications, enabling smooth collaboration and the deployment of AI models.

On-Premises Infrastructure

On-premises AI is a platform that runs AI applications on organizations’ own physical infrastructure rather than relying on third-party cloud services. Thus, it offers a high level of security, flexibility, and customization.

Advantages

• Data Control: Organizations have complete control over sensitive data, with compliance to regulations such as GDPR and HIPAA.
• Easily Customizable: You can tailor the AI infrastructure to your specific needs.
• Lower latency and Faster Processing: On premises allows faster processing as data does not need to travel to external servers, making it ideal for heavy applications.
• Full Infrastructure Ownership: Organizations have full control over their updates, system deployment, and more.

Neo Cloud

Neo cloud combines on-premises infrastructure with a cloud environment, bridging the gap between the two. They are primarily AI-first cloud providers that offer high performance GPU as-a-Service GPUaaS and infrastructure at a lower cost than hyperscalers. It makes accessing AI workloads easy. Organizations can rent GPU from Neo cloud providers instead of having their own AI infrastructure.

Advantages 

• AI-Optimized Infrastructure: Neo-Clouds are designed with AI processing, including high-performance GPUs, accelerated networking, and inference-optimized hardware.
• Scalability and Balanced Performance: Neo-Cloud delivers lower latency than public cloud services while maintaining scalability and flexibility.
• Simplified AI Deployment: AI-ready infrastructure is made available to organizations without dealing with physical infrastructure, which makes operations more complex.

Head-to-Head Comparison: Cloud vs On-Prem vs Neo-Cloud?

How to Choose the Right AI Inference Strategy?

It’s quite a job to pick the right inference model depending on the workload characteristics, cost, and business goals. Organizations have already started adopting hybrid or multi-cloud environments to get standout results.

The following are some of the key points one must consider:

In case of high-volume, cost-sensitive inference, go with Neo Cloud

• Latency-critical use cases: On-premises or edge first.
• Elastic or experimental workloads: Can go with public cloud
• Regulated data: On-prem environment can be considered
• Global consumer apps: Cloud or hybrid
• In-house expertise:  If yes, on-premises or hybrid, and if no, cloud platform.

Wrapping it Up!

An organization’s AI success is dependent on AI inference strategy you pick. There is a need for strategic consideration in selecting cloud, on-prem, and Neo-cloud deployment models based on performance, security, scalability, and cost.

Whereas cloud services provide a convenient and scalable platform, on-premises infrastructure provides a level of control and low latency. Neo-cloud is becoming a strong competitor, offering a mix of performance optimization and flexible deployment options. After all, the choice is all up to you! As AI is evolving at the fastest pace, companies should implement inference strategies that fit their business rightly and drive results.

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FAQs  

1. Which are the two types of inferences in AI?

Answer: There are mainly two types of inferences: deductive inference and inductive inference.

2. Which are the key factors that influence AI inference deployment decisions?

Answer: A few factors are: data security, latency, scalability, flexibility, and workload complexity.

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