Local vs Cloud AI

As AI continues to gain adoption across consumer and businesse use cases, the industry is experimenting with a variety of forms to access the outputs. When LLM-based AI was initially introduced on a large scale, costs were at the forefront of the discussion. Between 2021 and 2024, the cost to process a million tokens dropped from $60 to $0.06  (a factor of 1,000x).

This has not only made AI more efficient (more output for a lower cost) but also far more accessible to a broader range of users and use cases.

‍We have written about the hardware components for local AI inference in the past (see Local AI’s Impact on Gaming), but today we will focus on the software strategies by examining the benefits and applications of running AI locally and in the cloud.

Note: we are specifically considering model inference, not training. Training is typically done on large-scale clusters of GPUs.

‍Local AI: What Is It & Where Is The Value

‍Local AI refers to running AI models and applications directly on your device, eliminating the need for remote cloud servers for inference. Models are downloaded to the device and then loaded into local memory.

Local AI took longer to gain popularity compared to cloud AI for several reasons. Initially, customers required powerful GPUs and significant memory, which created hardware and computational barriers. Models were complex and large, which wasn’t aligned with consumer or edge devices. Lastly, infrastructure for managing and securing data locally was a significant burden and required strong technical knowledge to operate efficiently.

Local AI fits best when security, privacy, and real-time performance are required. It provides the following value propositions:

• Privacy and Security: Sensitive data remains on-site, reducing breach risk and facilitating compliance with regulations such as GDPR or HIPAA.
• Reduced Latency: Data is processed instantly without needing to transfer to a remote server.
• Offline Functionality: AI applications function without internet access, which is beneficial in remote or secure environments.
• Customization and Control: Direct access to hardware and software enables fine-tuning models and optimization for specific use cases (Latenode).
• Costs: Upfront costs will generally be higher due to initial setup up but run-time will be cost-free as all computation is done locally.
  • Note: there are ongoing costs related to hardware maintenance and depreciation that will be maintained by the user/company

Local AI is powerful for products and services such as smart home devices, autonomous vehicles, voice assistants, healthcare diagnostics, and industrial automation. Local AI is well-suited to these types of applications because it processes data directly on-site, ensuring user privacy and allowing the system to function reliably even if internet connectivity is lost.On-device processing delivers instant responses for automation, making real-time features like security alerts or voice control far more effective. This approach also minimizes bandwidth usage and reduces exposure to external security threats, resulting in more resilient, private, and responsive everyday technology.

‍Cloud AI: What Is It and Where Is The Value

‍Cloud AI refers to the deployment and utilization of AI models, software tools, and services on remote infrastructure provided and operated by third-party providers, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, and others. This means that the actual computation and inference occur on powerful servers housed in global data centers, rather than on the user's local machine. This creates several value propositions:

• Scalability: Easily scale resources for intensive training, processing, and storage needs, as cloud providers offer hardware on demand. Unlike Local AI, you are not limited to a singular piece of hardware.
• Low Entry Cost: No need for significant upfront hardware investment; payment models are pay-as-you-go, which are ideal for experimentation.
• Reduced Maintenance Burden: Cloud platforms handle updates, maintenance, and security patches.
• Big Data: Optimal for tasks requiring large datasets, frequent model updates, and cloud-native architectures.
• Accessibility: Multiple users and teams can access shared models and data from anywhere, supporting distributed workflows.

Cloud AI is best suited for running applications such as advanced chatbots and generative AI that require large-scale language models, personalized recommendations for e-commerce and streaming (with large user datasets), fraud detection for financial institutions, and enterprise SaaS that must scale seamlessly.

It provides massive computational power, flexible scaling, and instant global access, all managed by enterprise-grade infrastructure. For generative chatbots, recommendations, fraud detection, and collaborative data science, cloud platforms can efficiently process vast and complex datasets, supporting millions of concurrent users.

There are also organizations, such as Apple, that offer hybrid approaches to AI. Apple’s latest architecture, with "Apple Intelligence," supports running models directly on Apple devices, leveraging Apple Silicon and the Neural Engine for on-device processing. If tasks get too complex, they can be offloaded to server-side foundation models while still benefiting from increased security and performance through Apple’s Private Cloud.

‍Takeaway: Local AI is gaining traction thanks to advances in specialized hardware and more efficient inference, making models deployable directly on devices. This is resulting in stronger privacy, lower latency, and offline capability for everyday applications. Local processing enables users to customize AI for sensitive, real-time scenarios, while avoiding ongoing cloud fees; however, it remains limited by hardware constraints and higher initial setup costs. Meanwhile, cloud AI centralizes inference on powerful remote servers, lowering barriers for experimentation and scaling with pay-as-you-go pricing, which is ideal for large datasets and collaborative teams.