{
  "id": 19314958,
  "title": "Beyond the cloud: Why businesses are turning to on-device AI",
  "url": "https://urgent.news/2026/11/10/beyond-the-cloud-why-businesses-are-turning-to-on-device-ai",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-10T09:16:11.000Z",
  "source": {
    "name": "YourStory",
    "slug": "yourstory",
    "url": "https://yourstory.com/2026/11/beyond-the-cloud-why-businesses-are-turning-to-on-device-ai"
  },
  "original_language": "en",
  "account": "The shift from cloud-based AI to on-device AI is gaining traction among businesses seeking to manage costs, protect data, and reduce reliance on internet connectivity. At TechSparks 2026, Akash Saxena, CEO of Brilyant, highlighted the financial and privacy concerns associated with using cloud-based AI tools. He argued that businesses should consider moving routine AI workloads to local devices.\n\nSaxena pointed out that while the cost of individual tokens may be decreasing, inference costs could rise significantly as businesses deploy more AI agents and consume more tokens. He cited Gartner's predictions that inference costs could increase more than fivefold. Furthermore, using cloud-based AI tools raises concerns about data privacy, intellectual property protection, and compliance with data protection requirements. In India, for instance, 83% of enterprises have not implemented the Digital Personal Data Protection (DPDP) Act, which could result in significant financial consequences for non-compliance.\n\nSaxena recommended bringing the model to the data rather than taking the data to the model. On-device AI offers several advantages over cloud-based AI, including lower latency, no recurring token charges for local inference, and full control over where data is processed. Most tasks can be moved to on-device AI, although some complex tasks will still require large, advanced models in the cloud. A balanced approach involves using cloud-based models for complex tasks and moving suitable everyday workloads to local devices.\n\nBrilyant collaborates with partners like Apple and Jamf to provide device provisioning, AI setup, and lifecycle management for large-scale on-device AI deployments. By leveraging hardware with neural processing units, businesses can reduce recurring cloud costs while enhancing data governance and operational resilience. The shift to on-device AI should be about strategically deciding which AI workloads should run locally and where business data should remain, rather than using AI uniformly across all tasks.",
  "summary": "At TechSparks 2026, Brilyant CEO Akash Saxenaa explained how running AI models locally can help businesses control costs, protect sensitive data, and reduce their dependence on the cloud. AI tools have made it easier for businesses to analyze documents, generate content, and automate tasks. But",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}