Should we use cloud-based AI or on-device AI?

Cloud infrastructure serves as the engine for fully exploiting AI’s capabilities, offering significant scalability to harness vast computing resources. For many organizations, AI either acts as a primary use case for cloud adoption or becomes fully enabled once they transition to the cloud. Cloud service providers offer scalable resources that help control and minimize the cost of AI development and deployment, making advanced AI more accessible. Moreover, specialized hardware in the cloud can significantly drive down costs for massive compute needs, particularly for training large foundation models.

The cloud also simplifies AI workflows and democratizes access to state-of-the-art AI infrastructure and services, enabling teams to achieve more at a lower cost. It facilitates collaboration across an enterprise and among individual teams, allowing users to seamlessly share real-time insights and resources. Cloud-native machine learning (ML) services can scale with large amounts of data and numerous processing and ML jobs, reducing operational overhead. Furthermore, pre-built AI and ML APIs offer ready access to easy-to-use building blocks, requiring no extensive in-house ML expertise.

However, the widespread use of cloud for AI also necessitates robust security and governance. Cloud helps classify data access, providing permissions for employees to access data only when needed and with appropriate controls. This is crucial for maintaining visibility, securing access, and controlling data used for AI development and use, especially sensitive or regulated data.

The Case for On-Device AI

On-device AI, also known as edge computing, processes data closer to its source, offering distinct advantages for certain use cases. A primary benefit is low latency, as data does not need to travel to a central cloud server and back, which is particularly beneficial in remote locations or scenarios requiring immediate responses.

Another significant advantage is enhanced data privacy and security. By keeping data local on the device, on-device AI can reduce the risk of data leakage and unauthorized access that can occur when data is transmitted to and stored in the cloud. This is especially important for applications dealing with highly sensitive information, such as medical images or credit scoring in banking, where privacy and compliance are paramount. Industrial AI, for example, often deals with smaller, specialized datasets derived continuously from operational technology (OT) layers, where processing on the edge can be highly efficient.

However, on-device AI typically faces limitations in compute power compared to the vast resources available in the cloud, which can restrict the complexity and scale of models that can be deployed. It may also involve higher initial hardware investment and require specialized talent to manage and maintain these distributed systems.

Factors Influencing the Decision

The choice between cloud-based and on-device AI is not one-size-fits-all and should be guided by several key considerations:

Data Requirements
AI workloads demand massive volumes of diverse and high-quality data for model training and validation. Cloud architectures are well-suited for managing this diversity and volume efficiently, bringing together data lakes, data warehouses, and other purpose-built data stores under one umbrella. For scenarios with limited or domain-specific datasets, on-device AI or cognitive AI, which combines human knowledge with ML techniques, might be more appropriate.

Cost Management
While cloud services offer scalable resources to control costs, the training of larger models, especially generative AI, can quickly become expensive. Hybrid cloud approaches, which combine on-premises infrastructure with cloud resources, allow organizations to use the most cost-effective solution for each workload, balancing raw processing power with cloud agility.

Performance and Latency
For applications demanding real-time processing and minimal latency, such as autonomous systems or industrial control, on-device AI can be critical. Cloud solutions are typically suitable for tasks where some latency is acceptable.

Security and Governance
Security must be a top priority, integrated from the start of any AI strategy. Both cloud and on-device AI solutions require robust governance frameworks, data protection measures (like encryption), and continuous monitoring to mitigate risks, ensure compliance, and address ethical concerns such as bias and data leakage. The "black box" nature of complex AI models, particularly generative AI, further complicates security and explainability, making robust monitoring essential regardless of deployment location.

Scalability and Flexibility
Cloud offers unparalleled scalability for evolving AI initiatives, allowing organizations to expand capabilities without significant additional upfront investments. On-device solutions might be less flexible in adapting to rapidly changing AI models or data volumes without hardware upgrades.

Organizational Maturity and Talent
Organizations at different stages of AI maturity—tactical, strategic, or transformational—will have varying needs. Those just starting may benefit from the ease of use and managed services offered by cloud providers. Building and retaining specialized AI talent is crucial for success, whether it's for developing custom models or leveraging existing services. Strategic partnerships with technology providers can also help bridge skill gaps and accelerate adoption.

The Hybrid Future

Many organizations are finding that a hybrid approach often provides the best of both worlds, offering flexibility, cost-effectiveness, and optimal performance across diverse use cases. This strategy allows businesses to leverage the scalability and advanced services of the cloud for intensive training and broad applications, while utilizing on-device processing for latency-sensitive tasks or data that must remain local due to privacy or regulatory constraints.

Ultimately, the decision rests on a thorough understanding of an organization's specific business goals, risk appetite, and the unique characteristics of each AI application. By carefully considering these factors, companies can design an AI architecture that not only harnesses the transformative power of AI but also ensures long-term resilience, security, and value creation.

Talk to us to explore your options.