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Gcore CDN updates: Dedicated IP and BYOIP now available

We’re pleased to announce two new premium features for Gcore CDN: Dedicated IP and Bring Your Own IP (BYOIP). These capabilities give customers more control over their CDN configuration, helping you meet strict security, compliance, and branding requirements.Many organizations, especially in finance and other regulated sectors, require full control over their network identity. With these new features, Gcore enables customers to use unique, dedicated IP addresses to meet compliance or security standards; retain ownership and visibility over IP reputation and routing, and deliver content globally while maintaining trusted, verifiable IP associations.Read on for more information about the benefits of both updates.Dedicated IP: exclusive addresses for your CDN resourcesThe Dedicated IP feature enables customers to assign a private IP address to their CDN configuration, rather than using shared ones. This is ideal for:Businesses that are subject to strict security or legal frameworks.Customers who want isolated IP resources to ensure consistent access and reputation.Teams using WAAP or other advanced security solutions where dedicated IPs simplify policy management.BYOIP: bring your own IP range to Gcore CDNWith Bring Your Own IP (BYOIP), customers can use their own public IP address range while leveraging the performance and global reach of Gcore CDN. This option is especially useful for:Resellers who prefer to keep Gcore infrastructure invisible to end clients.Enterprises maintaining brand consistency and control over IP reputation.How to get startedBoth features are currently available as paid add-ons and are configured manually by the Gcore team. To request activation or learn more, please contact Gcore Support or your account manager.We’re working on making these features easier to manage and automate in future releases. As always, we welcome your feedback on both the feature functionality and the request process—your insights help us improve the Gcore CDN experience for everyone.Get in touch for more information

October 13, 2025 1 min read

Introducing AI Cloud Stack: turning GPU clusters into revenue-generating AI clouds

Enterprises and cloud providers face major roadblocks when trying to deploy GPU infrastructure at scale: long time-to-market, operational inefficiencies, and difficulty bringing new capacity to market profitably. Establishing AI environments with hyperscaler-grade functionality typically requires years of engineering effort, multiple partner integrations, and complex operational tooling.Not anymore.With Gcore AI Cloud Stack, organizations can transform bare Nvidia GPU clusters into a fully cloud-enabled environment—complete with orchestration, observability, billing, and go-to-market support—all in a fraction of the time it would take to build from scratch, maximizing GPU utilization.This proven solution marks the latest addition to the Gcore AI product suite, enabling enterprises and cloud providers to accelerate AI cloud deployment through better GPU utilization, monetization, reduced complexity, and hyperscaler-grade functionality in their own AI environments. Gcore AI Cloud Stack is already powering leading technology providers, including VAST and Nokia.Why we built AI Cloud StackBuying and efficiently operating GPUs at a large scale requires significant investment, time, and expertise. Most organizations need to hit the ground running, bypassing years of in-house R&D. Without a robust reference architecture, infrastructure and network preparation, 24/7 monitoring, dynamic resource allocation, orchestration abstraction, and clear paths to utilization or commercialization, enterprises can spend years before seeing ROI.“Gcore brings together the key pieces—compute, networking, and storage—into a usable stack. That integration helps service providers stand up AI clouds faster and onboard clients sooner, accelerating time to revenue. Combined with the advanced multi-tenant capabilities of VAST’s AI Operating System, it delivers a reliable, scalable, and futureproof AI infrastructure. Gcore offers operators a valuable option to move quickly without building everything themselves.”— Dan Chester, CSP Director EMEA, VAST DataAt Gcore, we understand that organizations across industries will continue to invest heavily in GPUs to power the next wave of AI innovation—meaning these challenges aren’t going away. AI Cloud Stack solves today’s challenges and anticipates tomorrow’s. It ensures that GPU infrastructure at the core of AI innovation delivers maximum value to enterprises.How AI Cloud Stack worksThis comprehensive solution is structured across three stages.1. Provision and launchGcore handles the complexities of initial deployment, from physical infrastructure setup to orchestration, enabling enterprises to go live quickly with a reliable GPU cloud.2. Operations and managementThe solution includes monitoring, orchestration, ticket management, and ongoing support to keep environments stable, secure, and efficient. This includes automated GPU failure handling and optimized resource management.3. Go-to-market supportUnlike other solutions, AI Cloud Stack goes beyond infrastructure. Building on Gcore’s experience as a trusted NVIDIA Cloud Provider (NCP), it helps customers sell their capacity, including through established reseller channels. This integrated GTM support ensures capacity doesn’t sit idle, losing value and potential.What sets Gcore apartUnlike many providers entering this market, Gcore has operated as a global cloud provider for over a decade and has been an early player in the global AI landscape. Gcore knows what it takes to build, scale, and sell cloud and AI services—because it has done it for customers and partners worldwide. Gcore AI Cloud Stack has already been deployed on thousands of NVIDIA Hopper GPUs across Europe to build a commercial-grade AI cloud with full orchestration, abstraction, and monetization layers. That real-world experience allows Gcore to deliver the infrastructure, operational playbook, and sales enablement customers need to succeed.“We’re pleased to collaborate with Gcore, a strong European ISV, to advance a networking reference architecture for AI clouds. Combining Nokia’s open, programmable, and reliable networking with Gcore’s cloud software accelerates deployable blueprints that customers can adopt across data centers and the edge.”— Mark Vanderhaegen, Head of Business Development, Data Center Networks, NokiaKey features of AI Cloud StackCloudification of GPU clusters: Transform raw infrastructure into cloud-like consumption: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), GPU as a Service (GPUaaS), or Model as a Service (MaaS).Gcore AI suite integration: Enable serverless inference and training capabilities through Gcore’s enterprise AI suite.Hyperscaler functionality: Built-in billing, observability, orchestration, and professional services deliver the tools CSPs and enterprises need to operate—similar to what they’re used to getting on public cloud.White-label options: Deliver capacity under your own brand while relying on Gcore’s proven global cloud backbone.NVIDIA AI Enterprise-ready: Integrate pretrained models, chatbots, and NVIDIA AI blueprints to accelerate time-to-market.The future of AI cloudsWith Gcore AI Cloud Stack, enterprises no longer need to spend years building the operational, technical, and commercial capabilities required to utilize and monetize GPU infrastructure. Instead, they can launch in a few months with a hyperscaler-grade solution designed for today’s AI demands.Whether you’re a cloud service provider, an enterprise investing in AI infrastructure, or a partner looking to accelerate GPU monetization, AI Cloud Stack gives you the speed, scalability, and GTM support you need.Ready to turn your GPU clusters into a fully monetized, production-grade AI cloud? Talk with our AI experts to learn how you can go from bare metal to model-as-a-service in months, not years.Get a customized consultation

October 7, 2025 3 min read

Gcore Radar Q1–Q2 2025: three insights into evolving attack trends

Cyberattacks are becoming more frequent, larger in scale, and more sophisticated in execution. For businesses across industries, this means protecting digital resources is more important than ever. Staying ahead of attackers requires not only robust defense solutions but also a clear understanding of how attack patterns are changing.The latest edition of the Gcore Radar report, covering the first half of 2025, highlights important shifts in attack volumes, industry targets, and attacker strategies. Together, these findings show how the DDoS landscape is evolving, and why adaptive defense has never been more important.Here are three key insights from the report, which you can download in full here.#1. DDoS attack volumes continue to riseIn Q1–Q2 2025, the total number of DDoS attacks grew by 21% compared to H2 2024 and 41% year-on-year.The largest single attack peaked at 2.2 Tbps, surpassing the previous record of 2 Tbps in late 2024.The growth is driven by several factors, including the increasing availability of DDoS-for-hire services, the rise of insecure IoT devices feeding into botnets, and heightened geopolitical and economic tensions worldwide. Together, these factors make attacks not only more common but also harder to mitigate.#2. Technology overtakes gaming as the top targetThe distribution of attacks by industry has shifted significantly. Technology now represents 30% of all attacks, overtaking gaming, which dropped from 34% in H2 2024 to 19% in H1 2025. Financial services remain a prime target, accounting for 21% of attacks.This trend reflects attackers’ growing focus on industries with broader downstream impact. Hosting providers, SaaS platforms, and payment systems are attractive targets because a single disruption can affect entire ecosystems of dependent businesses.#3. Attacks are getting smarter and more complexAttackers are increasingly blending high-volume assaults with application-layer exploits aimed at web apps and APIs. These multi-layered tactics target customer-facing systems such as inventory platforms, payment flows, and authentication processes.At the same time, attack durations are shifting. While maximum duration has shortened from five hours to three, mid-range attacks lasting 10–30 minutes have nearly quadrupled. This suggests attackers are testing new strategies designed to bypass automated defenses and maximize disruption.How Gcore helps businesses stay protectedAs attack methods evolve, businesses need equally advanced protection. Gcore DDoS Protection offers over 200 Tbps filtering capacity across 210+ points of presence worldwide, neutralizing threats in real time. Integrated Web Application and API Protection (WAAP) extends defense beyond network perimeters, protecting against sophisticated application-layer and business-logic attacks. To explore the report’s full findings, download the complete Gcore Radar report here.Download Gcore Radar Q1-Q2 2025

September 23, 2025 2 min read

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 cloudAI 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 latencyCuts down on international transit for reduced costsHelps companies stay compliant with local lawsPrivate edge: control over convenienceMany 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 powerDeploying 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 takeawayAs 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.Ready to get started? Deploy a model in just three clicks with Gcore Everywhere Inference.Discover Everywhere Inference

September 11, 2025 2 min read

Smart caching and predictive streaming: the next generation of content delivery

As streaming demand surges worldwide, providers face mounting pressure to deliver high-quality video without buffering, lag, or quality dips, no matter where the viewer is or what device they're using. That pressure is only growing as audiences consume content across mobile, desktop, smart TVs, and edge-connected devices.Traditional content delivery networks (CDNs) were built to handle scale, but not prediction. They reacted to demand, but couldn’t anticipate it. That’s changing.Today, predictive streaming and AI-powered smart caching are enabling a proactive, intelligent approach to content delivery. These technologies go beyond delivering content by forecasting what users will need and making sure it's there before it's even requested. For network engineers, platform teams, and content providers, this marks a major evolution in performance, reliability, and cost control.What are predictive streaming and smart caching?Predictive streaming is a technology that uses AI to anticipate what a viewer will watch next, so the content can be ready before it's requested. That might mean preloading the next episode in a series, caching popular highlights from a live event, or delivering region-specific content based on localized viewing trends.Smart caching supports this by storing that predicted content on servers closer to the viewer, reducing delays and buffering. Together, they make streaming faster and smoother by preparing content in advance based on user behavior.Unlike traditional caching, which relies on static popularity metrics or simple geolocation, predictive streaming is dynamic. It adapts in real time to what’s happening on the platform: user actions, traffic spikes, network conditions, and content trends. This results in:Faster playback with minimal bufferingReduced bandwidth and server loadHigher quality of experience (QoE) scores across user segmentsFor example, during the 2024 UEFA European Championship, several broadcasters used predictive caching to preload high-traffic game segments and highlight reels based on past viewer drop-off points. This allowed for instant replay delivery in multiple languages without overloading central servers.Why predictive streaming matters for viewersGlobally, viewers tend to binge-watch new streaming platform releases. For example, sci-fi-action drama Fallout got 25% of its annual US viewing minutes (2.9 billion minutes) in its first few days of release. The South Korean series Queen of Tears became Netflix's most-watched Korean drama of all time in 2024, amassing over 682.6 million hours viewed globally, with more than half of those watch hours occurring during its six-week broadcast run.A predictive caching system can take advantage of this launch-day momentum by pre-positioning likely-to-be-watched episodes, trailers, or bonus content at the edge, customized by region, device, or time of day.The result is a seamless, high-performance experience that anticipates user behavior and scales intelligently to meet it.Benefits for streaming providersTraditional CDNs often waste resources caching content that may never be viewed. Predictive caching focuses only on content that is likely to be accessed, leading to:Lower egress costsReduced server loadMore efficient cache hit ratiosOne of the core benefits of predictive streaming is latency reduction. By caching content at the edge before it’s requested, platforms avoid the delay caused by round-trips to origin servers. This is especially critical for:Live sports and eventsInteractive or real-time formats (e.g., polls, chats, synchronized streams)Edge environments with unreliable last-mile connectivityFor instance, during the 2024 Copa América, mobile viewers in remote areas of Argentina were able to stream matches without delay thanks to proactive edge caching based on geo-temporal viewing predictions.How it worksAt the core of predictive streaming is smart caching: the process of storing data closer to the end user before it’s explicitly requested. Here’s how it works:Data ingestion: The system gathers data on user behavior, device types, content popularity, and location-based trends.Behavior modeling: AI models identify patterns (e.g., binge-watching behaviors, peak-hour traffic, or regional content spikes).Pre-positioning: Based on predictions, the system caches video segments, trailers, or interactive assets to edge servers closest to where demand is expected.Real-time adaptation: As user behavior changes, the system continuously updates its caching strategy.Use cases across streaming ecosystemsSmart caching and predictive delivery benefit nearly every vertical of streaming.Esports and gaming platforms: Live tournaments generate unpredictable traffic surges, especially when underdog teams advance. Predictive caching helps preload high-interest match content, post-game analysis, and multilingual commentary before traffic spikes hit. This helps provide global availability with minimal delay.Corporate webcasts and investor events: Virtual AGMs or earnings calls need to stream seamlessly to thousands of stakeholders, often under compliance pressure. Predictive systems can cache frequently accessed segments, like executive speeches or financial summaries, at regional nodes.Education platforms: In EdTech environments, predictive delivery ensures that recorded lectures, supplemental materials, and quizzes are ready for users based on their course progression. This reduces lag for remote learners on mobile connections.VOD platforms with regional licensing: Content availability differs across geographies. Predictive caching allows platforms to cache licensed material efficiently and avoid serving geo-blocked content by mistake, while also meeting local performance expectations.Government or emergency broadcasts: During public health updates or crisis communications, predictive streaming can support multi-language delivery, instant replay, and mobile-first optimization without overloading networks during peak alerts.Looking forward: Personalization and platform governanceWe predict that the next wave of predictive streaming will likely include innovations that help platforms scale faster while protecting performance and compliance:Viewer-personalized caching, where individual user profiles guide what’s cached locally (e.g., continuing series, genre preferences)Programmatic cache governance, giving DevOps and marketing teams finer control over how and when content is distributedCross-platform intelligence, allowing syndicated content across services to benefit from shared predictions and joint caching strategiesGcore’s role in the predictive futureAt Gcore, we’re building AI-powered delivery infrastructure that makes the future of streaming a practical reality. Our smart caching, real-time analytics, and global edge network work together to help reduce latency and cost, optimize resource usage, and improve user retention and stream stability.If you’re ready to unlock the next level of content delivery, Gcore’s team is here to help you assess your current setup and plan your predictive evolution.Discover how Gcore streaming technologies helped fan.at boost subscription revenue by 133%

September 3, 2025 4 min read

From budget strain to AI gain: Watch how studios are building smarter with AI

Game development is in a pressure cooker. Budgets are ballooning, infrastructure and labor costs are rising, and players expect more complexity and polish with every release. All studios, from the major AAAs to smaller indies, are feeling the strain.But there is a way forward. In a recent webinar, Sean Hammond, Territory Manager for the UK and Nordics at Gcore, explained how AI is reshaping game development workflows and how the right infrastructure strategy can reduce costs, speed up production, and create better player experiences.Scroll on to watch key moments from Sean's talk and explore how studios can make AI work for them.Rising costs are threatening game developmentGame revenue has slowed, but development costs continue to rise. Some AAA titles now surpass $100 million in development budgets. The complexity of modern games demands more powerful servers, scalable infrastructure, and larger teams, making the industry increasingly unsustainable.Personnel and infrastructure costs are also climbing. Developers, artists, and QA testers with specialized skills are in high demand, as are technologies like VR, AR, and AI. Studios are also having to invest more in cybersecurity to protect player data, detect cheating, and safeguard in-game economies.AI is revolutionizing GameDev, even without a perfect use caseWhile the perfect use case for AI in gaming may not have been found yet, it’s already transforming how games are built, tested, and personalized.Sean highlighted emerging applications, including:Smarter QA testingAI-driven player personalizationReal-time motion and animationAccelerated environment and character designMultilingual localizationAdaptive game balancingStudios are already applying these technologies to reduce production timelines and improve immersion.The challenge of secure, scalable AI adoptionOf course, AI adoption doesn’t come without its challenges. Chief among them is security. Public models pose risks: no studio wants their proprietary assets to end up training a competitor’s model.The solution? Deploy AI models on infrastructure you trust so you’re in complete control. That’s where Gcore comes in.Gcore Everywhere Inference reduces compute costs and infrastructure bloat by allowing you to deploy only what you need, where you need it.The future of gaming is AI at scaleTo power real-time player experiences, your studio needs to deploy AI globally, close to your users.Gcore Everywhere Inference lets you deploy models worldwide at the edge with minimal latency because data is not routed back to central servers. This means fast, responsive gameplay and a new generation of real-time, AI-driven features.As a company originally built by gamers, we’ve developed AI solutions with gaming studios in mind. Here’s what we offer:Global edge inference for real-time gameplay: Deploy your AI models close to players worldwide, enabling fast, responsive player experiences without routing data to central servers.Full control over AI model deployment and IP protection: Avoid public APIs and retain full ownership of your assets with on-prem options, preventing your proprietary data from being available to competitors.Scalable, cost-efficient infrastructure tailored to gaming workloads: Deploy only what you need to avoid overprovisioning and reduce compute costs without sacrificing performance.Enhanced player retention through AI-driven personalization and matchmaking: Real-time inference powers smarter NPCs and dynamic matchmaking, improving engagement and keeping players coming back for more.Deploy models in 3 clicks and under 10 seconds: Our developer-friendly platform lets you go from trained model to global deployment in seconds. No complex DevOps setup required.Final takeawayAI is advancing game development fast, but only if it’s deployed right. Gcore offers scalable, secure, and cost-efficient AI infrastructure that helps studios create smarter, faster, and more immersive games.Want to see how it works? Deploy your first model in just a few clicks.Check out our blog on how AI is transforming gaming in 2025

August 25, 2025 2 min read

No capacity = no defense: rethinking DDoS resilience at scale

DDoS attacks are growing so massive they are overwhelming the very infrastructure designed to stop them. Earlier this year, a peak attack exceeding 7 Tbps was recorded, while 1–2 Tbps attacks have become everyday occurrences. Such volumes were unimaginable just a few years ago.Yet many businesses still depend on mitigation systems that were not designed to scale alongside this rapid attack growth. While these systems may have smart detection, that advantage is moot if physical infrastructure cannot handle the load. Today, raw capacity is non-negotiable — intelligent filtering alone isn’t enough; you need vast, globally distributed throughput.Lukasz Karwacki, Gcore’s Security Solution Architect specializing in DDoS, explains why modern DDoS protection requires immense capacity, global distribution, and resilient routing. Scroll down to watch him describe why a globally distributed defense model is now the minimum standard for mitigating devastating DDoS attacks.DDoS is a capacity war, not just a traffic spikeThe central challenge in DDoS mitigation today is the total attack volume versus total available throughput.Attacks do not originate from a single location. Global botnets harness compromised devices across Asia, Africa, Europe, and the Americas. When all this traffic converges on a single data center, it creates a structural mismatch: a single site’s limited capacity pitted against the full bandwidth of the internet.Anycast is non-negotiable for global capacityTo counter today’s attack volumes, mitigation capacity must be distributed globally, and that’s where Anycast routing plays a critical role.Anycast routes incoming traffic to the nearest available scrubbing center. If one region is overwhelmed or offline, traffic is automatically redirected elsewhere. This eliminates single points of failure and enables the absorption of massive attacks without compromising service availability.By contrast, static mitigation pipelines create bottlenecks: all traffic funnels through a single point, making it easy for attackers to overwhelm that location. Centralized mitigation means centralized failure. The more distributed your infrastructure, the harder it is to take down — that’s resilient network design.Why always-on cloud defense outperforms on-demand protectionSome DDoS defenses activate only when an attack is detected. These on-demand models may save costs but introduce a brief delay while traffic is rerouted and protections come online.Even a few seconds of delay can allow a high-speed attack to inflict damage.Gcore’s cloud-native DDoS protection is always-on, continuously monitoring, filtering, and balancing traffic across all scrubbing centers. This means no activation lag and no dependency on manual triggers.Capacity is the new baseline for protectionModern DDoS attacks focus less on sophistication and more on sheer scale. Attackers simply overwhelm infrastructure by flooding it with more traffic than it can handle.True DDoS protection begins with capacity planning — not just signatures or rulesets. You need sufficient bandwidth, processing power, and geographic distribution to absorb attacks before they reach your core systems.At Gcore, we’ve built a globally distributed DDoS mitigation network with over 200 Tbps capacity, 40+ protected data centers, and thousands of peering partners. Using Anycast routing and always-on defense, our infrastructure withstands attacks that other systems simply can’t.Many customers turn to Gcore for DDoS protection after other providers fail to keep up with attack capacity.Find out why Fawkes Games turned to Gcore for DDoS protection

August 22, 2025 2 min read
How AI-enhanced content moderation is powering safe and compliant streaming

How AI-enhanced content moderation is powering safe and compliant streaming

As streaming experiences a global boom across platforms, regions, and industries, providers face a growing challenge: how to deliver safe, respectful, and compliant content delivery at scale. Viewer expectations have never been higher, likewise the regulatory demands and reputational risks.Live content in particular leaves little room for error. A single offensive comment, inappropriate image, or misinformation segment can cause long-term damage in seconds.Moderation has always been part of the streaming conversation, but tools and strategies are evolving rapidly. AI-powered content moderation is helping providers meet their safety obligations while preserving viewer experience and platform performance.In this article, we explore how AI content moderation works, where it delivers value, and why streaming platforms are adopting it to stay ahead of both audience expectations and regulatory pressures.Real-time problems require real-time solutionsHuman moderators can provide accuracy and context, but they can’t match the scale or speed of modern streaming environments. Live streams often involve thousands of viewers interacting at once, with content being generated every second through audio, video, chat, or on-screen graphics.Manual review systems struggle to keep up with this pace. In some cases, content can go viral before it is flagged, like deepfakes that circulated on Facebook leading up to the 2025 Canadian election. In others, delays in moderation result in regulatory penalties or customer churn, like X’s 2025 fine under the EU Digital Services Act for shortcomings in content moderation and algorithm transparency. This has created a demand for scalable solutions that act instantly, with minimal human intervention.AI-enhanced content moderation platforms address this gap. These systems are trained to identify and filter harmful or non-compliant material as it is being streamed or uploaded. They operate across multiple modalities—video frames, audio tracks, text inputs—and can flag or remove content within milliseconds of detection. The result is a safer environment for end users.How AI moderation systems workModern AI moderation platforms are powered by machine learning algorithms trained on extensive datasets. These datasets include a wide variety of content types, languages, accents, dialects, and contexts. By analyzing this data, the system learns to identify content that violates platform policies or legal regulations.The process typically involves three stages:Input capture: The system monitors live or uploaded content across audio, video, and text layers.Pattern recognition: It uses models to identify offensive content, including nudity, violence, hate speech, misinformation, or abusive language.Contextual decision-making: Based on confidence thresholds and platform rules, the system flags, blocks, or escalates the content for review.This process is continuous and self-improving. As the system receives more inputs and feedback, it adapts to new forms of expression, regional trends, and platform-specific norms.What makes this especially valuable for streaming platforms is its low latency. Content can be flagged and removed in real time, often before viewers even notice. This is critical in high-stakes environments like esports, corporate webinars, or public broadcasts.Multi-language moderation and global streamingStreaming audiences today are truly global. Content crosses borders faster than ever, but moderation standards and cultural norms do not. What’s considered acceptable in one region may be flagged as offensive in another. A word that is considered inappropriate in one language might be completely neutral in another. A piece of nudity in an educational context may be acceptable, while the same image in another setting may not be. Without the ability to understand nuance, AI systems risk either over-filtering or letting harmful content through.That’s why high-quality moderation platforms are designed to incorporate context into their models. This includes:Understanding tone, not just keywordsRecognizing culturally specific gestures or idiomsAdapting to evolving slang or coded languageApplying different standards depending on content type or target audienceThis enables more accurate detection of harmful material and avoids false positives caused by mistranslation.Training AI models for multi-language support involves:Gathering large, representative datasets in each languageTeaching the model to detect content-specific risks (e.g., slurs or threats) in the right cultural contextContinuously updating the model as language evolvesThis capability is especially important for platforms that operate in multiple markets or support user-generated content. It enables a more respectful experience for global audiences while providing consistent enforcement of safety standards.Use cases across the streaming ecosystemAI moderation isn’t just a concern for social platforms. It plays a growing role in nearly every streaming vertical, including the following:Live sports: Real-time content scanning helps block offensive chants, gestures, or pitch-side incidents before they reach a wide audience. Fast filtering protects the viewer experience and helps meet broadcast standards.Esports: With millions of viewers and high emotional stakes, esports platforms rely on AI to remove hate speech and adult content from chat, visuals, and commentary. This creates a more inclusive environment for fans and sponsors alike.Corporate live events: From earnings calls to virtual town halls, organizations use AI moderation to help ensure compliance with internal communication guidelines and protect their reputation.Online learning: EdTech platforms use AI to keep classrooms safe and focused. Moderation helps filter distractions, harassment, and inappropriate material in both live and recorded sessions.On-demand entertainment: Even outside of live broadcasts, moderation helps streaming providers meet content standards and licensing obligations across global markets. It also ensures user-submitted content (like comments or video uploads) meets platform guidelines.In each case, the shared goal is to provide a safe and trusted streaming environment for users, advertisers, and creators.Balancing automation with human oversightAI moderation is a powerful tool, but it shouldn’t be the only one. The best systems combine automation with clear review workflows, configurable thresholds, and human input.False positives and edge cases are inevitable. Giving moderators the ability to review, override, or explain decisions is important for both quality control and user trust.Likewise, giving users a way to appeal moderation decisions or report issues ensures that moderation doesn’t become a black box. Transparency and user empowerment are increasingly seen as part of good platform governance.Looking ahead: what’s next for AI moderationAs streaming becomes more interactive and immersive, moderation will need to evolve. AI systems will be expected to handle not only traditional video and chat, but also spatial audio, avatars, and real-time user inputs in virtual environments.We can also expect increased demand for:Personalization, where viewers can set their own content preferencesIntegration with platform APIs for programmatic content governanceCross-platform consistency to support syndicated content across partnersAs these changes unfold, AI moderation will remain central to the success of modern streaming. Platforms that adopt scalable, adaptive moderation systems now will be better positioned to meet the next generation of content challenges without compromising on speed, safety, or user experience.Keep your streaming content safe and compliant with GcoreGcore Video Streaming offers AI Content Moderation that satisfies today’s digital safety concerns while streamlining the human moderation process.To explore how Gcore AI Content Moderation can transform your digital platform, we invite you to contact our streaming team for a demonstration. Our docs provide guidance for using our intuitive Gcore Customer Portal to manage your streaming content. We also provide a clear pricing comparison so you can assess the value for yourself.Embrace the future of content moderation and deliver a safer, more compliant digital space for all your users.Try AI Content Moderation for freeTry AI Content Moderation for free

August 18, 2025 4 min read

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

August 7, 2025 2 min read

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!

July 29, 2025 2 min read
Gcore recognized as a Leader in the 2025 GigaOm Radar for AI Infrastructure

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

July 22, 2025 2 min read

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

July 17, 2025 3 min read

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