Edge AI is your next competitive advantage: highlights from Seva Vayner’s webinar

Edge AI isn’t just a technical milestone. It’s a strategic lever for businesses aiming to gain a competitive advantage with AI.

As AI deployments grow more complex and more global, central cloud infrastructure is hitting real-world limits: compliance barriers, latency bottlenecks, and runaway operational costs. The question for businesses isn’t whether they’ll adopt edge AI, but how soon.

In a recent webinar with Mobile World Live, Seva Vayner, Gcore’s Product Director of Edge Cloud and AI, made the business case for edge inference as a competitive differentiator. He outlined what it takes to stay ahead in a world where speed, locality, and control define AI success.

Scroll on to watch Seva explain why your infrastructure choices now shape your market position later.

Location is everything: edge over cloud

AI is no longer something globally operating businesses can afford to run from a central location. Regional regulations and growing user expectations mean models must be served as close to the user as possible. This reduces latency, but perhaps more importantly is essential for compliance with local laws.

Edge AI also keeps costs down by avoiding costly international traffic routes. When your users are global but your infrastructure isn’t, every request becomes an expensive, high-latency journey across the internet.

Edge inference solves three problems at once in an increasingly regionally fragmented AI landscape:

• Keeps compute near users for low latency
• Cuts down on international transit for reduced costs
• Helps companies stay compliant with local laws

Private edge: control over convenience

Many businesses started their AI journey by experimenting with public APIs like OpenAI’s. But as companies and their AI use cases mature, that’s not good enough anymore. They need full control over data residency, model access, and deployment architecture, especially in regulated industries or high-sensitivity environments.

That’s where private edge deployments come in. Instead of relying on public endpoints and shared infrastructure, organizations can fully isolate their AI environments, keeping data secure and models proprietary.

This approach is ideal for healthcare, finance, government, and any sector where data sovereignty and operational security are critical.

Optimizing edge AI: precision over power

Deploying AI at the edge requires right-sizing your infrastructure for the models and tasks at hand. That’s both technically smarter and far more cost-effective than throwing maximum power and size at every use case.

Making smart trade-offs allows businesses to scale edge AI sustainably by using the right hardware for each use case.

AI at the edge helps businesses deliver the experience without the excess. With the control that the edge brings, hardware costs can be cut by using exactly what each device or location requires, reducing financial waste.

Final takeaway

As Seva put it, AI infrastructure decisions are no longer just financial; they’re part of serious business strategy. From regulatory compliance to operational cost to long-term scalability, edge inference is already a necessity for businesses that plan to serve AI at scale and get ahead in the market.

Gcore offers a full suite of public and private edge deployment options across six continents, integrated with local telco infrastructure and optimized for real-time performance. Learn more about Everywhere Inference, our edge AI solution, or get in touch to see how we can help tailor a deployment model to your needs.

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