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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%

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

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