CIO Influence Interview with Seva Vayner, Product Director of Edge Cloud and Edge AI at Gcore

Seva Vayner, Product Director of Edge Cloud and Edge AI at Gcore, discussed edge and cloud solutions for the media and entertainment industry, challenges in adopting edge computing, innovations at Gcrore and more in this Q&A:

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To begin, can you share a bit about your career journey? What led you to your current role as Product Director of Edge Cloud and Edge AI at Gcore?

I joined Gcore in 2018 after meeting our CEO, Andre Reitenbach, at a technology innovation event. One of Gcore’s early visions was to create a fully automated compute layer integrated with a single network backbone. The challenge was to build a cloud platform from scratch and integrate it with existing infrastructure, an ambitious task that had to be completed within just a few months.

Within six months of joining, we launched our first cloud region in Luxembourg, providing a fully integrated infrastructure-as-a-service offering. Since then, we have expanded to over 50 locations worldwide, hiring a highly skilled team to enhance our capabilities in computing, networking, storage, Kubernetes, and AI.

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Gcore specializes in powerful edge and cloud solutions for the media and entertainment industry. How do you see Edge AI transforming these sectors?

Edge AI, that is running AI workloads in data centres close to the data’s location is set to revolutionise media and entertainment by significantly reducing latency and enabling real-time interactions. Gaming, in particular, has always pushed the boundaries of low-latency infrastructure. Two decades ago, multiplayer gaming was limited to local network setups, but advancements in cloud and edge computing have made global multiplayer experiences possible. Today, ultra-low latency remains crucial for action games, where even milliseconds can impact gameplay.

A similar shift has occurred in video streaming. Years ago, buffering was a common frustration, but now, platforms like YouTube, Netflix, and TikTok deliver high-quality content instantly. Netflix, for example, initially relied on physical DVD rentals because streaming technology was not yet ready. With the rise of cloud computing and services , they transitioned to streaming, making instant access to content the norm.

We believe AI will follow a similar trajectory. Just as content is now delivered instantly, AI-powered applications – whether voice assistants, generative media, or interactive experiences – will require real-time processing. Edge AI ensures that these applications can run closer to where the  data is, reducing delays and enhancing responsiveness. At Gcore, we are building the foundational infrastructure and software layers to support this future, providing the compute power and network backbone needed to make real-time AI interactions seamless.

What are some of the key challenges businesses face in adopting edge computing, and how does Gcore help them overcome these hurdles?

One of the biggest challenges in edge computing is balancing performance, scalability, and cost. Many businesses struggle with deploying distributed infrastructure that can handle high data loads while maintaining low latency. Traditional cloud solutions often centralise processing, which can introduce delays – especially critical for latency-sensitive applications like gaming, fraud detection, predictive maintenance, live streaming, and AI-driven services.

Gcore addresses these challenges by offering a globally distributed edge infrastructure. With over 180 PoPs (points of presence) worldwide, we provide businesses with low-latency computing power closer to their end users or data. Our solutions integrate computing, security, and networking into a single platform, simplifying deployment and management. Additionally, we ensure seamless scaling, allowing businesses to expand their edge capabilities without complex infrastructure overhauls.

By combining edge computing with AI-driven automation, we help companies optimise performance while reducing operational complexity. Whether it is gaming, media, telecoms, financial services, or AI applications across fields, Gcore’s edge solutions enable businesses to deliver seamless, high-performance experiences to their users worldwide.

Can you share any recent innovations or product enhancements that Gcore has introduced in Edge Cloud or Edge AI?

Over the past three years, Gcore has been heavily focused on AI, particularly in building large-scale clusters for model training. However, we are now seeing a rapid shift to AI inferencing, where real-time processing is essential. To address this shift, we have developed a software platform called Everywhere Inference that allows enterprises to efficiently deploy, tune, and monitor AI workloads, whether in the cloud or on-premises.

A key challenge for enterprises is data security, especially when working with large language models (LLMs) and generative AI. Many organizations, particularly in industries like telecoms, finance, and retail, require air-gapped environments to maintain full control over their data. To meet this need, Gcore has extended Everywhere Inference to on-prem deployments, a solution that enables businesses to run AI inference on their infrastructure while leveraging our serverless inference and distributed edge capabilities.

Everywhere Inference simplifies AI inference deployment by providing a ready-to-use platform that eliminates the complexity of managing GPU hardware, file systems, or Kubernetes configurations. Our goal is to make AI inference as seamless as possible, allowing businesses to run chatbots, image generation tools, and other AI-driven applications with minimal setup and expertise. We have already partnered with key enterprises to integrate this solution, and we are excited about its potential to revolutionise real-time AI processing.

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What are some of the most exciting advancements in edge cloud and AI that you’re seeing in the industry?

Industry demand for real-time AI experiences is growing exponentially, for which latency is a critical factor. One of the most exciting trends is seeing how compute and networking set ups are evolving to support the expansion of AI inferencing at the edge. Often, these applications are running various models in the background, which means for the performance of the end application, continuous monitoring of the model and GPU health is critical. While large-scale training still happens in centralised data centres, we’re seeing a growing need for real-time AI processing closer to end users, whether for voice assistants, predictive maintenance, automated customer support, or AI-powered retail analytics.

Another major development is the push towards more accessible AI infrastructure. Previously, running AI workloads required specialised knowledge in Kubernetes, cloud orchestration, and hardware optimisation. However, new platforms are making it easier for businesses to deploy AI models without deep technical expertise. This democratisation of AI is increasing adoption across industries.

Additionally, enterprises are prioritising control over their AI environments. As regulations around data privacy tighten, many companies want the flexibility to run AI workloads in their own secure environments while still benefiting from cloud-like scalability. Hybrid AI deployments, where businesses can process sensitive data on-prem while leveraging cloud resources for scalability, are becoming more popular.

At Gcore, we’re actively working to bridge these gaps by offering edge AI solutions that combine low-latency performance, enterprise-level security, and ease of deployment. The future of AI is real-time, and edge computing is the key to making that a reality.

How are enterprises outside of the media industry leveraging Gcore’s edge computing and AI capabilities?

Beyond media and entertainment, large-scale enterprises – especially in industries like telecom are turning to Gcore’s edge computing and AI solutions to streamline operations and stay competitive. For enterprise, AI is a tool that can make in-field maintenance, customer relationship management, fraud detection, risk assessment and many other business applications be more-data driven and efficient. These companies, often generating billions in revenue, face significant challenges in integrating new technologies while maintaining compliance with internal workflows and regulatory requirements.

One of the biggest pain points for these enterprises is reducing time to market. They need to deliver innovative solutions to their customers quickly, but building infrastructure from scratch is costly and complex. Gcore helps by providing a scalable, ready-to-use platform that simplifies AI and edge computing deployments. This allows businesses to focus on innovation rather than infrastructure management.

Additionally, many enterprises have legacy systems that limit their ability to adopt the latest technologies. Gcore bridges this gap by offering solutions that integrate seamlessly with existing workflows while providing the flexibility to scale as needed. This makes the teams within the business more productive, as it significantly simplifies the process of deploying AI at a large scale. Whether it is optimising network performance for telcos, enhancing real-time analytics for retail, or supporting AI-driven automation in industrial sectors, our edge computing and AI capabilities empower enterprises to stay ahead in an increasingly digital world.

Looking ahead, how do you envision the future of edge AI and cloud computing in the next 3-5 years?

The next few years will see a rapid increase in AI adoption, alongside significant challenges in scalability and cost. At present, businesses are still in the early stages of understanding how AI integrates into their operations. However, within the next two years, we anticipate a much faster adoption rate, particularly in industries such as telco, finance, healthcare, and creative industries. AI-generated content is set to become the norm, potentially surpassing a 50/50 split between human and AI-driven creation.

From a software perspective, conversational AI will take centre stage. Chatbots, virtual assistants, and AI-powered interfaces will evolve into “super apps,” providing seamless, voice-driven interactions. As AI models become more sophisticated, their ability to generate content, offer personalised recommendations, and engage in realistic conversations will continue to advance.

Nevertheless, AI remains costly to develop and implement. Over the next 3-5 years, we will see increased pricing pressures and the inevitable commoditisation of AI technologies, like the evolution of cloud computing. Inference and real-time AI processing will become more efficient, making AI more accessible to businesses of all sizes. AI will transition from being merely a tool to becoming an integral interface, deeply embedded within every aspect of our digital experiences.

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