When you open a weather app, you probably expect it to know at what precise moment it will start raining in your neighborhood. When you’re shopping online, you can see how many of a particular item exist “on the shelves” at any given moment. You’ve also likely had the experience of having a credit card transaction flagged because it didn’t adhere to your normal spending patterns. These capabilities are made possible by real-time AI processing, a technology that is expected to help the global AI market grow to $4 trillion by 2030.
While real-time data processing has existed for decades, AI has amplified its impact. AI-driven real-time processing impacts industries like healthcare, finance, and manufacturing by enabling instant decision-making and process optimization. This is essential for customer experiences and applications like autonomous systems, fraud detection, and smart home management.
The state of real-time AI processing in 2025
Demand for real-time applications in IoT, telecom, and autonomous vehicles drives innovation in data processing. Everyday applications like consumer banking, customer service, and supply chain optimization rely on similar capabilities. However, organizations face technical challenges such as managing large data volumes and providing reliable, low-latency infrastructure.
In 2024, 70% of projected data center demand was for facilities capable of hosting advanced AI workloads. This, and the following examples, show the necessity of low-latency infrastructure to support AI-driven real-time processing.
- The financial services industry increasingly relies on real-time fraud detection systems that can halt unauthorized transactions within milliseconds. Visa’s network, VisaNet, can process over 65,000 transaction messages per second globally, requiring near-instantaneous fraud detection capabilities.
- In retail, dynamic pricing systems adjust product prices based on supply and demand fluctuations in real time. Amazon’s dynamic pricing engine reportedly changes prices more than 2.5 million times a day, driving optimal profitability and customer engagement.
- Smart traffic systems prevent accidents through instant traffic data analysis. Singapore’s Land Transport Authority has implemented a smart traffic control system that uses AI to monitor and adjust traffic lights in real time. By analyzing traffic flow and congestion levels, the system optimizes signal timings to reduce wait times and improve overall traffic efficiency, leading to decreased congestion and accident rates.
- In healthcare, real-time data can save lives by enabling instant medical insights, like detecting high-risk pregnancies early. By providing instant insights, real-time AI empowers healthcare practitioners to make informed, prescriptive decisions that can save lives and improve patient outcomes.
The technological advancements powering real-time AI
GPU upgrades and edge computing are the two key areas for hardware improvements in 2025.
GPU upgrade
In 2025, new and upgraded high-performance GPUs will enable innovative use cases. These GPUs are built to handle streaming data with minimal delay, providing the rapid computations required to keep up with high-speed live data.
For example, NVIDIA is expected to introduce significant advancements in its GPU chips in 2025, particularly with the launch of the Blackwell architecture and its successor, Rubin. The Blackwell series, including the GB200, is anticipated to feature enhanced performance and efficiency, making it well-suited for real-time AI applications. Following Blackwell, NVIDIA plans to introduce the Rubin architecture. Scheduled for mass production in late 2025 and availability in early 2026, Rubin GPUs will utilize TSMC’s 3nm process and HBM4 memory, promising further improvements in performance and energy efficiency.
With the introduction of these new chips, we can expect increased availability of GPUs leading to a price drop in cloud-based service models. Older models, like the A100, will likely decrease in price substantially compared to just a couple of years ago, making AI more accessible to companies globally.
Edge computing
Real-time AI processing will continue to rely heavily on AI at the edge, enabling low-latency data processing on devices like sensors, smartphones, and industrial robots, as well as on localized cloud servers. New high-performance GPUs designed for AI workloads will support fast, local computations, making real-time analytics scalable and efficient. Businesses will benefit from edge computing by reducing cloud bandwidth costs and distributing workloads across multiple nodes rather than relying on central servers. This setup will prove invaluable for applications with fluctuating traffic, like e-commerce, live sports streaming, and emergency response systems.
3 real-time processing AI use cases for 2025
Let’s look at three use cases for real-time AI processing set to become popular in 2025 and beyond.
- Video analytics and features: Instant insights from live video feeds will transform security, enabling automated responses to potential threats. For example, AI-powered surveillance systems can detect unusual activities like unauthorized access in restricted areas. In the retail sector, Walmart employs AI to streamline its checkout process and reduce customer waiting times. Gcore’s AI ASR and AI Content Moderation features, available to all Gcore Video Streaming customers, are helping to democratize access to real-time AI video features, keeping communities safer and bringing content to wider audiences at the click of a button.
- Real-time cybersecurity measures: AI-driven cybersecurity tools will continuously analyze network traffic and system behavior for threats. Instant alerts and automated responses will minimize data breaches and mitigate system damage. Gcore WAAP uses AI to analyze traffic patterns, automatically detecting and mitigating threats before they can cause harm. These intelligent algorithms evolve with each attack, enhancing protection against zero-day threats. The AI-driven engine continually refines its responses, providing a proactive approach to cybersecurity and keeping you one step ahead of cyber attackers.
- Real-time customer experience personalization: AI-powered customer experience tools will deliver personalized interactions by analyzing user behavior, preferences, and contextual data in real time. For example, e-commerce platforms can offer dynamic product recommendations based on browsing history and current site activity. Gcore Inference at the Edge can enable real-time personalization for businesses by processing data close to users, thereby reducing latency and enhancing customer satisfaction. Retailers, streaming platforms, and gaming companies can tailor experiences on the fly, boosting engagement, retention, and revenue.
Experience real-time AI processing at the edge with Gcore
Real-time AI processing is revolutionizing industries by enabling instant decision-making, optimizing processes, and enhancing customer experiences. As data volumes grow, advancements in edge computing, high-performance GPUs, and AI-driven analytics will be critical for scaling real-time applications. Businesses are increasingly harnessing real-time AI capabilities to improve operational efficiency, strengthen security, and deliver personalized experiences with minimal latency.
Gcore Edge AI solutions reduce data transfer delays, improve user experience, strengthen data security, and enable cost-effective scaling. Businesses can optimize performance while keeping sensitive data within designated regions, helping ensure compliance and enhancing privacy. Discover how Gcore can help your business unlock the full potential of real-time AI at the edge.
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Deploy GPT-OSS-120B privately on Gcore
OpenAI’s release of GPT-OSS-120B is a turning point for LLM developers. It’s a 120B parameter model trained from scratch, licensed for commercial use, and available with open weights. This is a serious asset for serious builders.Gcore now supports private GPT-OSS-120B deployments via our Everywhere Inference platform. That means you can stand up your own endpoint in minutes, run inference at scale, and control the full stack, without API limits, vendor lock-in, or hidden usage fees. Just fast, secure, controlled deployment on your terms. Deploy now in three clicks or read on to learn more.Why GPT-OSS-120B is big news for buildersThis model changes the game for anyone developing AI apps, platforms, or infrastructure. It brings GPT-3-level reasoning to the open-source ecosystem and frees developers from closed APIs.With GPT-OSS-120B, you get:Full access to model weights and architectureSelf-hosting for maximum data control and privacySupport for fine-tuning and model editingOffline deployment for secure or air-gapped useMassive cost savings at scaleYou can deploy in any Gcore region (or leverage Gcore’s three-click serverless inference on your own infrastructure), route traffic through your own stack, and fully control load, latency, and logs. This is LLM deployment for real-world apps, not just playground prompts.How to deploy GPT-OSS-120B with Gcore Everywhere InferenceGcore Everywhere Inference gives you a clean path from open model to production endpoint. You can spin up a dedicated deployment in just three clicks. We offer configuration options to suit your business needs:Choose your location (cloud or on-prem)Integrate via standard APIs (OpenAI-compatible)Control usage, autoscale, and costsDeploying GPT-OSS-120B on Gcore takes just three clicks in the Gcore Customer Portal.There are no shared endpoints. You get dedicated compute, low-latency routing, and full control and observability.You can also bring your own trained variant if you’ve fine-tuned GPT-OSS-120B elsewhere. We’ll help you host it reliably, close to your users.Use cases: where GPT-OSS-120B fits bestCommercial GPTs still outperform OSS models on some general tasks, but GPT-OSS-120B gives you control, portability, and flexibility where it counts. Most importantly, it gives you the ability to build privacy-sensitive applications.Great fits include:Internal dev tools and copilotsRetrieval-augmented generation (RAG) pipelinesSecure, private enterprise assistantsData-sensitive, on-prem AI workloadsModels requiring full customization or fine-tuningIt’s especially relevant for finance, healthcare, government, and legal teams operating under strict compliance rules.Deploy GPT-OSS-120B todayWant to learn more about GPT-OSS-120B and why Gcore is an ideal provider for deployment? Get all the information you need on our dedicated page.And if you’re ready to deploy in just three clicks, head on over to the Gcore Customer Portal. GPT-OSS-120B is waiting for you in the Application Catalog.Learn more about deploying GPT-OSS-120B on Gcore
Announcing new tools, apps, and regions for your real-world AI use cases
Three updates, one shared goal: helping builders move faster with AI. Our latest releases for Gcore Edge AI bring real-world AI deployments within reach, whether you’re a developer integrating genAI into a workflow, an MLOps team scaling inference workloads, or a business that simply needs access to performant GPUs in the UK.MCP: make AI do moreGcore’s MCP server implementation is now live on GitHub. The Model Context Protocol (MCP) is an open standard, originally developed by Anthropic, that turns AI models into agents that can carry out real-world tasks. It allows you to plug genAI models into everyday tools like Slack, email, Jira, and databases, so your genAI can read, write, and reason directly across systems. Think of it as a way to turn “give me a summary” into “send that summary to the right person and log the action.”“AI needs to be useful, not just impressive. MCP is a critical step toward building AI systems that drive desirable business outcomes, like automating workflows, integrating with enterprise tools, and operating reliably at scale. At Gcore, we’re focused on delivering that kind of production-grade AI through developer-friendly services and top-of-the-range infrastructure that make real-world deployment fast and easy.” — Seva Vayner, Product Director of Edge Cloud and AI, GcoreTo get started, clone the repo, explore the toolsets, and test your own automations.Gcore Application Catalog: inference without overheadWe’ve upgraded the Gcore Model Catalog into something even more powerful: an Application Catalog for AI inference. You can still deploy the latest open models with three clicks. But now, you can also tune, share, and scale them like real applications.We’ve re-architected our inference solution so you can:Run prefill and decode stages in parallelShare KV cache across pods (it’s not tied to individual GPUs) from August 2025Toggle WebUI and secure API independently from August 2025These changes cut down on GPU memory usage, make deployments more flexible, and reduce time to first token, especially at scale. And because everything is application-based, you’ll soon be able to optimize for specific business goals like cost, latency, or throughput.Here’s who benefits:ML engineers can deploy high-throughput workloads without worrying about memory overheadBackend developers get a secure API, no infra setup neededProduct teams can launch demos instantly with the WebUI toggleInnovation labs can move from prototype to production without reconfiguringPlatform engineers get centralized caching and predictable scalingThe new Application Catalog is available now through the Gcore Customer Portal.Chester data center: NVIDIA H200 capacity in the UKGcore’s newest AI cloud region is now live in Chester, UK. This marks our first UK location in partnership with Northern Data. Chester offers 2000 NVIDIA H200 GPUs with BlueField-3 DPUs for secure, high-throughput compute on Gcore GPU Cloud, serving your training and inference workloads. You can reserve your H200 GPU immediately via the Gcore Customer Portal.This launch solves a growing problem: UK-based companies building with AI often face regional capacity shortages, long wait times, or poor performance when routing inference to overseas data centers. Chester fixes that with immediate availability on performant GPUs.Whether you’re training LLMs or deploying inference for UK and European users, Chester offers local capacity, low latency, and impressive capacity and availability.Next stepsExplore the MCP server and start building agentic workflowsTry the new Application Catalog via the Gcore Customer PortalDeploy your workloads in Chester for high-performance UK-based computeDeploy your AI workload in three clicks today!
Gcore recognized as a Leader in the 2025 GigaOm Radar for AI Infrastructure
We’re proud to share that Gcore has been named a Leader in the 2025 GigaOm Radar for AI Infrastructure—the only European provider to earn a top-tier spot. GigaOm’s rigorous evaluation highlights our leadership in platform capability and innovation, and our expertise in delivering secure, scalable AI infrastructure.Inside the GigaOm Radar: what’s behind the Leader statusThe GigaOm Radar report is a respected industry analysis that evaluates top vendors in critical technology spaces. In this year’s edition, GigaOm assessed 14 of the world’s leading AI infrastructure providers, measuring their strengths across key technical and business metrics. It ranks providers based on factors such as scalability and performance, deployment flexibility, security and compliance, and interoperability.Alongside the ranking, the report offers valuable insights into the evolving AI infrastructure landscape, including the rise of hybrid AI architectures, advances in accelerated computing, and the increasing adoption of edge deployment to bring AI closer to where data is generated. It also offers strategic takeaways for organizations seeking to build scalable, secure, and sovereign AI capabilities.Why was Gcore named a top provider?The specific areas in which Gcore stood out and earned its Leader status are as follows:A comprehensive AI platform offering Everywhere Inference and GPU Cloud solutions that support scalable AI from model development to productionHigh performance powered by state-of-the-art NVIDIA A100, H100, H200 and GB200 GPUs and a global private network ensuring ultra-low latencyAn extensive model catalogue with flexible deployment options across cloud, on-premises, hybrid, and edge environments, enabling tailored global AI solutionsExtensive capacity of cutting-edge GPUs and technical support in Europe, supporting European sovereign AI initiativesChoosing Gcore AI is a strategic move for organizations prioritizing ultra-low latency, high performance, and flexible deployment options across cloud, on-premises, hybrid, and edge environments. Gcore’s global private network ensures low-latency processing for real-time AI applications, which is a key advantage for businesses with a global footprint.GigaOm Radar, 2025Discover more about the AI infrastructure landscapeAt Gcore, we’re dedicated to driving innovation in AI infrastructure. GPU Cloud and Everywhere Inference empower organizations to deploy AI efficiently and securely, on their terms.If you’re planning your AI infrastructure roadmap or rethinking your current one, this report is a must-read. Explore the report to discover how Gcore can support high-performance AI at scale and help you stay ahead in an AI-driven world.Download the full report
Protecting networks at scale with AI security strategies
Network cyberattacks are no longer isolated incidents. They are a constant, relentless assault on network infrastructure, probing for vulnerabilities in routing, session handling, and authentication flows. With AI at their disposal, threat actors can move faster than ever, shifting tactics mid-attack to bypass static defenses.Legacy systems, designed for simpler threats, cannot keep pace. Modern network security demands a new approach, combining real-time visibility, automated response, AI-driven adaptation, and decentralized protection to secure critical infrastructure without sacrificing speed or availability.At Gcore, we believe security must move as fast as your network does. So, in this article, we explore how L3/L4 network security is evolving to meet new network security challenges and how AI strengthens defenses against today’s most advanced threats.Smarter threat detection across complex network layersModern threats blend into legitimate traffic, using encrypted command-and-control, slow drip API abuse, and DNS tunneling to evade detection. Attackers increasingly embed credential stuffing into regular login activity. Without deep flow analysis, these attempts bypass simple rate limits and avoid triggering alerts until major breaches occur.Effective network defense today means inspection at Layer 3 and Layer 4, looking at:Traffic flow metadata (NetFlow, sFlow)SSL/TLS handshake anomaliesDNS request irregularitiesUnexpected session persistence behaviorsGcore Edge Security applies real-time traffic inspection across multiple layers, correlating flows and behaviors across routers, load balancers, proxies, and cloud edges. Even slight anomalies in NetFlow exports or unexpected east-west traffic inside a VPC can trigger early threat alerts.By combining packet metadata analysis, flow telemetry, and historical modeling, Gcore helps organizations detect stealth attacks long before traditional security controls react.Automated response to contain threats at network speedDetection is only half the battle. Once an anomaly is identified, defenders must act within seconds to prevent damage.Real-world example: DNS amplification attackIf a volumetric DNS amplification attack begins saturating a branch office's upstream link, automated systems can:Apply ACL-based rate limits at the nearest edge routerFilter malicious traffic upstream before WAN degradationAlert teams for manual inspection if thresholds escalateSimilarly, if lateral movement is detected inside a cloud deployment, dynamic firewall policies can isolate affected subnets before attackers pivot deeper.Gcore’s network automation frameworks integrate real-time AI decision-making with response workflows, enabling selective throttling, forced reauthentication, or local isolation—without disrupting legitimate users. Automation means threats are contained quickly, minimizing impact without crippling operations.Hardening DDoS mitigation against evolving attack patternsDDoS attacks have moved beyond basic volumetric floods. Today, attackers combine multiple tactics in coordinated strikes. Common attack vectors in modern DDoS include the following:UDP floods targeting bandwidth exhaustionSSL handshake floods overwhelming load balancersHTTP floods simulating legitimate browser sessionsAdaptive multi-vector shifts changing methods mid-attackReal-world case study: ISP under hybrid DDoS attackIn recent years, ISPs and large enterprises have faced hybrid DDoS attacks blending hundreds of gigabits per second of L3/4 UDP flood traffic with targeted SSL handshake floods. Attackers shift vectors dynamically to bypass static defenses and overwhelm infrastructure at multiple layers simultaneously. Static defenses fail in such cases because attackers change vectors every few minutes.Building resilient networks through self-healing capabilitiesEven the best defenses can be breached. When that happens, resilient networks must recover automatically to maintain uptime.If BGP route flapping is detected on a peering session, self-healing networks can:Suppress unstable prefixesReroute traffic through backup transit providersPrevent packet loss and service degradation without manual interventionSimilarly, if a VPN concentrator faces resource exhaustion from targeted attack traffic, automated scaling can:Spin up additional concentratorsRedistribute tunnel sessions dynamicallyMaintain stable access for remote usersGcore’s infrastructure supports self-healing capabilities by combining telemetry analysis, automated failover, and rapid resource scaling across core and edge networks. This resilience prevents localized incidents from escalating into major outages.Securing the edge against decentralized threatsThe network perimeter is now everywhere. Branches, mobile endpoints, IoT devices, and multi-cloud services all represent potential entry points for attackers.Real-world example: IoT malware infection at the branchMalware-infected IoT devices at a branch office can initiate outbound C2 traffic during low-traffic periods. Without local inspection, this activity can go undetected until aggregated telemetry reaches the central SOC, often too late.Modern edge security platforms deploy the following:Real-time traffic inspection at branch and edge routersBehavioral anomaly detection at local points of presenceAutomated enforcement policies blocking malicious flows immediatelyGcore’s edge nodes analyze flows and detect anomalies in near real time, enabling local containment before threats can propagate deeper into cloud or core systems. Decentralized defense shortens attacker dwell time, minimizes potential damage, and offloads pressure from centralized systems.How Gcore is preparing networks for the next generation of threatsThe threat landscape will only grow more complex. Attackers are investing in automation, AI, and adaptive tactics to stay one step ahead. Defending modern networks demands:Full-stack visibility from core to edgeAdaptive defense that adjusts faster than attackersAutomated recovery from disruption or compromiseDecentralized detection and containment at every entry pointGcore Edge Security delivers these capabilities, combining AI-enhanced traffic analysis, real-time mitigation, resilient failover systems, and edge-to-core defense. In a world where minutes of network downtime can cost millions, you can’t afford static defenses. We enable networks to protect critical infrastructure without sacrificing performance, agility, or resilience.Move faster than attackers. Build AI-powered resilience into your network with Gcore.Check out our docs to see how DDoS Protection protects your network
Introducing Gcore for Startups: created for builders, by builders
Building a startup is tough. Every decision about your infrastructure can make or break your speed to market and burn rate. Your time, team, and budget are stretched thin. That’s why you need a partner that helps you scale without compromise.At Gcore, we get it. We’ve been there ourselves, and we’ve helped thousands of engineering teams scale global applications under pressure.That’s why we created the Gcore Startups Program: to give early-stage founders the infrastructure, support, and pricing they actually need to launch and grow.At Gcore, we launched the Startups Program because we’ve been in their shoes. We know what it means to build under pressure, with limited resources, and big ambitions. We wanted to offer early-stage founders more than just short-term credits and fine print; our goal is to give them robust, long-term infrastructure they can rely on.Dmitry Maslennikov, Head of Gcore for StartupsWhat you get when you joinThe program is open to startups across industries, whether you’re building in fintech, AI, gaming, media, or something entirely new.Here’s what founders receive:Startup-friendly pricing on Gcore’s cloud and edge servicesCloud credits to help you get started without riskWhite-labeled dashboards to track usage across your team or customersPersonalized onboarding and migration supportGo-to-market resources to accelerate your launchYou also get direct access to all Gcore products, including Everywhere Inference, GPU Cloud, Managed Kubernetes, Object Storage, CDN, and security services. They’re available globally via our single, intuitive Gcore Customer Portal, and ready for your production workloads.When startups join the program, they get access to powerful cloud and edge infrastructure at startup-friendly pricing, personal migration support, white-labeled dashboards for tracking usage, and go-to-market resources. Everything we provide is tailored to the specific startup’s unique needs and designed to help them scale faster and smarter.Dmitry MaslennikovWhy startups are choosing GcoreWe understand that performance and flexibility are key for startups. From high-throughput AI inference to real-time media delivery, our infrastructure was designed to support demanding, distributed applications at scale.But what sets us apart is how we work with founders. We don’t force startups into rigid plans or abstract SLAs. We build with you 24/7, because we know your hustle isn’t a 9–5.One recent success story: an AI startup that migrated from a major hyperscaler told us they cut their inference costs by over 40%…and got actual human support for the first time. What truly sets us apart is our flexibility: we’re not a faceless hyperscaler. We tailor offers, support, and infrastructure to each startup’s stage and needs.Dmitry MaslennikovWe’re excited to support startups working on AI, machine learning, video, gaming, and real-time apps. Gcore for Startups is delivering serious value to founders in industries where performance, cost efficiency, and responsiveness make or break product experience.Ready to scale smarter?Apply today and get hands-on support from engineers who’ve been in your shoes. If you’re an early-stage startup with a working product and funding (pre-seed to Series A), we’ll review your application quickly and tailor infrastructure that matches your stage, stack, and goals.To get started, head on over to our Gcore for Startups page and book a demo.Discover Gcore for Startups
Announcing a new AI-optimized data center in Southern Europe
Good news for businesses operating in Southern Europe! Our newest cloud regions in Sines, Portugal, give you faster, more local access to the infrastructure you need to run advanced AI, ML, and HPC workloads across the Iberian Peninsula and wider region. Sines-2 marks the first region launched in partnership with Northern Data Group, signaling a new chapter in delivering powerful, workload-optimized infrastructure across Europe. And Sines-3 expands capacity and availability for the region.Strategically positioned in Portugal, Sines-2 and Sines-3 enhance coverage in Southern Europe, providing a lower-latency option for customers operating in or targeting this region. With the explosive growth of AI, machine learning, and compute-intensive workloads, these new regions are designed to meet escalating demand with cutting-edge GPU and storage capabilities.You can activate Sines-2 and Sines-3 for GPU Cloud or Everywhere Inference today with just a few clicks.Built for AI, designed to scaleSines-2 and Sines-3 bring with them next-generation infrastructure features, purpose-built for today's most demanding workloads:NVIDIA H100 GPUs: Unlock the full potential of AI/ML training, high-performance computing (HPC), and rendering workloads with access to H100 GPUs.VAST NFS (file sharing protocol) support: Benefit from scalable, high-throughput file storage ideal for data-intensive operations, research, and real-time AI workflows.IaaS portfolio: Deploy Virtual Machines, manage storage, and scale infrastructure with the same consistency and reliability as in our flagship regions.Organizations operating in Portugal, Spain, and nearby regions can now deploy workloads closer to end users, improving application performance. For finance, healthcare, public sector, and other organisations running sensitive workloads that must stay within a country or region, Sines-2 and Sines-3 are easy ways to access state-of-the-art GPUs with simplified compliance. Whether you're building AI models, running simulations, or managing rendering pipelines, Sines-2 and Sines-3 offer the performance, capacity, availability, and proximity you need.And best of all, servers are available and ready to deploy today.Run your AI workloads in Portugal todayWith these new Sines regions and our partnership with Northern Data Group, we're making it easier than ever for you to run AI workloads at scale. If you need speed, flexibility, and global reach, we're ready to power your next AI breakthrough.Unlock the power of Sines-2 and Sines-3 today
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