Choosing Between Cloud and On-Premise AI Deployments

Shieldbase

Aug 30, 2024

Choosing Between Cloud and On-Premise AI Deployments

As AI technologies become more accessible, organizations must carefully choose between cloud and on-premise deployments for their AI initiatives. Cloud hosting offers flexibility and eliminates the need for hardware maintenance, making it ideal for experimentation and scaling with variable workloads. However, the costs associated with high GPU usage and large datasets can be significant. Conversely, on-premise solutions provide greater control over data and hardware, potentially reducing long-term costs but requiring substantial initial investment and ongoing maintenance. The decision hinges on factors such as data location, scalability needs, and cost considerations, with a balanced approach often involving initial cloud-based development followed by a strategic transition to on-premise infrastructure.

AI applications have expanded beyond the realms of tech giants like Facebook and Google. Advances in storage technologies and GPUs have democratized access to AI capabilities such as machine learning and robotic process automation. Now, organizations of all sizes must consider where to store the vast amounts of data required for AI initiatives.

Cloud vs. On-Premise Deployment

As businesses explore AI applications, the choice between cloud and on-premise deployment becomes crucial for IT service providers and channel partners. The fundamental question is: "Where do we fall on the AI continuum?"

Understanding this question helps determine whether on-premise data center infrastructure is necessary or if leveraging cloud-based prebuilt models is sufficient to achieve your goals.

Cost Considerations

Cloud services offer a flexible pay-as-you-go model, with costs ranging from a few hundred to tens of thousands of dollars per month based on the scale of the project. Conversely, on-premise solutions involve a significant initial investment for hardware, plus ongoing maintenance and update costs.

Performance and Efficiency

The choice of GPU significantly impacts AI model performance and efficiency. Top GPUs for AI workloads include:

NVIDIA A100 Tensor Core GPU: Priced between $10,000 and $20,000, it is designed for demanding AI and high-performance computing tasks.
NVIDIA H100 Tensor Core GPU: At over $40,000, this GPU is known for its exceptional performance in data centers for AI, HPC, and analytics.
NVIDIA T4 GPU: Costing around $2,000, it is suitable for smaller setups and distributed computing environments.
NVIDIA V100 GPU: Priced between $8,000 and $10,000, it is among the most advanced GPUs for accelerating AI and HPC tasks.

Cloud vs. On-Premise Hosting

The comparison between cloud and on-premise hosting is often likened to renting versus buying a home.

Cloud Hosting

Cloud hosting is akin to renting. It allows flexibility and reduces the burden of hardware maintenance, which remains the responsibility of the hosting provider. However, pre-trained models or storage requirements on the cloud can become costly, especially for extensive GPU usage and large datasets.

On-Premise Hosting

On-premise hosting is similar to purchasing a home. It eliminates the need for renewal contracts, potentially reducing long-term costs but increasing in-house maintenance expenses. Organizations seeking extensive control or dealing with large-scale AI deployments may find on-premise solutions more economical in the long run.

Scalability and Security

Scalability

On-premise solutions offer complete control over hardware and updates but require advanced planning for scaling. Cloud resources, however, can be rapidly adjusted to meet demand, though this can lead to software clutter affecting scalability.

Security

On-premise deployments provide full control over data, minimizing third-party access risks. Cloud providers must ensure data encryption and system updates, but organizations may have less visibility into data storage and backup practices.

Data Gravity

The concept of data gravity highlights the importance of data location. If critical data resides on-premise, deploying AI applications on-site may be more efficient. Conversely, if the data is in the cloud, deploying AI applications there may be preferable. Data gravity refers to data’s ability to attract applications and services, influencing the choice between cloud and on-premise solutions.

The Case for Cloud-Based AI Applications

Cloud-based AI services offer numerous advantages, including access to pre-trained models and substantial computing power without the need for extensive infrastructure investment. These services lower entry barriers and streamline AI experimentation. However, the cost of high GPU counts and extensive training on public clouds can be prohibitive.

The Case for On-Premise AI

On-premise infrastructure can be beneficial for organizations with significant compute needs or those preferring capital expenses over operational ones. For large-scale AI initiatives, investing in on-premise solutions might be more practical than relying on cloud services.

Challenges in Implementing AI

Implementing AI involves navigating data silos and unstructured data, often requiring substantial effort to clean, de-identify, and process data. Ensuring engineers and data scientists have access to this data can be challenging.

Conclusion

Deciding on the infrastructure for AI training and deployment is a significant decision that should consider both requirements and economics. While cloud computing offers flexibility and ease of use, on-premise solutions may be more cost-effective for heavy, continuous AI workloads. Each option has its advantages and drawbacks, and a thorough evaluation of costs and benefits is essential.

Starting with cloud-based AI development and planning for a potential transition to on-premise infrastructure may be a prudent approach, allowing organizations to assess their needs and make informed decisions about scaling and resource allocation.