Gcore Blog

Gcore and Orange Business launch innovation program piloting joint solution to deliver sovereign inference as a service

Gcore and Orange Business have kicked off a strategic co-innovation program with the mission to deliver a scalable, production-grade AI inference service that is sovereign by design. By combining Orange Business’ secure, trusted cloud infrastructure and Gcore’s AI inference private deployment service, the collaboration empowers European enterprises and public sector organizations to run inference workloads at scale, without compromising on latency, control, or compliance.Gcore’s AI inference private deployment service is already live on Orange Business’ Cloud Avenue infrastructure. Selected enterprises across industries are actively testing it in real-world scenarios. These pilot customers are exploring how fast, secure, and compliant inference can accelerate their AI projects, cut deployment times, and reduce infrastructure overhead.The prototype will be demonstrated at NVIDIA GTC Paris, at the Taiga Cloud booth G26. Stop by any time to see it in action.The inference supercycle is underwayBy 2030, inference will comprise 70% of enterprise AI workloads. Telcos are well positioned to lead this shift due to their dense edge presence, licensed national data infrastructure, and long-standing trust relationships.Gcore’s inference solution provides a sovereign, edge-native inference layer. It enables users to serve real-time, GPU-intensive applications like agentic AI, trusted LLMs, computer vision, and predictive analytics, all while staying compliant with Europe’s evolving data and AI governance frameworks.From complexity to three clicksEnterprise AI doesn’t need to be hard. Deploying inference workloads at scale used to demand Kubernetes fluency, large MLOps teams, and costly trial-and-error.Now? It’s just three clicks:Pick a model: Choose from NVIDIA NIMs, open source, or proprietary libraries.Choose a region: Select one of Orange Business’ accredited EU data centers.Deploy: See your workloads go live in under 10 seconds.Enterprises can launch inference projects faster, test ideas more quickly, and deliver production-ready AI services without spending months on ML plumbing.Explore our blog to watch a demo showing how enterprises can deploy inference workloads in just three clicks and ten seconds.Sovereign by designAll model data, logs, and inference results are stored exclusively within Orange Business’ own data centers in France, Germany, Norway, and Sweden. Cross-border data transfer is opt-in only, helping ensure alignment with GDPR, sector-specific regulations, and the forthcoming EU AI Act.This platform is built for trust, transparency, and sovereignty by default. Customers maintain full control over their data, with governance baked into every layer of the deployment.Performance without trade-offsGcore’s AI inference solution avoids the latency spikes, cold starts, and resource waste common in traditional cloud AI setups. Key design features include:Smart GPU routing: Directs each request to the nearest in-region GPU, delivering real-time performance with sub-50ms latency.Pre-loaded models: Reduces cold start delays and improves response times.Secure multi-tenancy: Isolates customer data while maximizing infrastructure efficiency.The result is a production-ready inference platform optimized for both performance and compliance.Powering the future of AI infrastructureThis partnership marks a step forward for Europe’s sovereign AI capabilities. It highlights how telcos can serve as the backbone of next-generation AI infrastructure, hosting, scaling, and securing workloads at the edge.With hundreds of edge POPs, trusted national networks, and deep ties across vertical industries, Orange Business is uniquely positioned to support a broad range of use cases, including real-time customer service AI, fraud detection, healthcare diagnostics, logistics automation, and public sector digital services.What’s next: validating real-world performanceThis phase of the Gcore and Orange Business program is focused on validating the solution through live customer deployments and performance benchmarks. Orange Business will gather feedback from early access customers to shape its future sovereign inference service offering. These insights will drive refinements and shape the roadmap ahead of a full commercial launch planned for later this year.Gcore and Orange Business are committed to delivering a sovereign inference service that meets Europe’s highest standards for speed, simplicity, and trust. This co-innovation program lays the foundation for that future.Ready to discover how Gcore and Orange Business can deliver sovereign inference as a service for your business?Request a preview

June 10, 2025 3 min read

Why on-premises AI is making a comeback

In recent years, cloud AI infrastructure has soared in popularity. With its scalability and ease of deployment, it’s no surprise that organizations rushed to transfer their data to the cloud in a bid to become “cloud-first.”But now, the tide is turning.As AI workloads grow more complex and regulatory pressures increase, many companies are reconsidering their reliance on cloud and turning back toward on-premises AI infrastructure.Rather than doubling down on the cloud, organizations are diversifying—adopting multi-cloud models, sovereign cloud environments, and even hybrid or fully on-prem setups. The era of a single cloud provider handling everything is coming to an end. Why? Control, security, and performance are hard to find in the public cloud.Here’s why more businesses are bringing AI back in-house.#1 Enhanced data security and controlData security remains one of the most urgent concerns driving the return to on-prem infrastructure.For sensitive or high-priority workloads—common in sectors like finance, healthcare, and government—keeping data off the cloud is often non-negotiable. Cloud computing inherently increases risk by exposing data to shared environments, wider attack surfaces, and complex supply chains.Choosing a trusted cloud provider can mitigate some of those risks. But it can’t replace the peace of mind that comes from keeping sensitive data in-house.With on-premises AI, organizations gain fine-grained access control. Encryption keys remain internal and breach exposure shrinks dramatically. It’s also much easier to stay compliant with privacy laws when data never leaves your own secure perimeter.For industries where trust and confidentiality are everything, on-prem solutions offer full visibility into where and how data is stored and processed.#2 Performance enhancement and latency reductionLatency matters—especially in AI.On-premises AI systems excel in environments that require real-time performance and heavy compute loads. Processing data locally avoids the physical delays caused by transferring it across the internet to a cloud data center.By eliminating long-haul network hops, companies get near-instant access to computing resources. They also get to fine-tune their internal networks—using private fiber, low-hop switching, and other low-latency optimizations that cloud customers can’t control.Unlike multi-tenant cloud platforms, on-prem resources aren’t shared. That means consistently low, predictable latency.This is vital for use cases where milliseconds—or even microseconds—make a difference: autonomous vehicles, real-time analytics, robotic control systems, and high-speed trading. Fast feedback loops and localized processing enable better outcomes, tighter control, and faster decision-making at the edge.#3 Regulatory compliance and data sovereigntyAround the world, data privacy regulations are tightening. For most organizations, compliance isn’t optional.On-premises infrastructure helps keep data safely inside the organization’s network. This supports data sovereignty, ensuring that sensitive information remains subject only to local laws—not the policies of another country’s cloud provider.It's also a powerful hedge against geopolitical instability.While hyperscalers operate globally, they’re always headquartered somewhere. That makes their infrastructure vulnerable to political shifts, sanctions, or changes in international data law. Governments may require them to restrict access, share data, or cut off services entirely—especially to organizations in sanctioned or adversarial jurisdictions.Businesses relying on these providers risk disruption when regulations change. On-premises infrastructure, by contrast, offers reliable continuity and greater control—especially in uncertain times.#4 Cost control and operational benefitsCloud pricing may look flexible, but costs can escalate quickly.Data transfers, storage, and compute spikes all add up—fast. In contrast, on-premises infrastructure provides a predictable Total Cost of Ownership (TCO). Although upfront CapEx is higher, OpEx remains more stable over time.Organizations can invest in high-performance hardware tailored to their specific needs and amortize those costs across years. That means no surprise bills, no sudden price hikes, and no dependence on vendor pricing models.Of course, running on-prem infrastructure comes with its own challenges. It demands specialized teams for deployment, maintenance, and support. These experts are costly to recruit and retain—but they’re critical to ensure uptime, security, and performance.Still, for companies with relatively stable compute and storage needs, the long-term savings often outweigh the initial setup effort. On-prem also integrates more smoothly into existing IT workflows, without the need for internet access or additional network setup—another operational bonus.#5 Proactive threat detection and automated responsesOn-premises AI sometimes enables smarter, more customized security.Advanced platforms can continuously analyze live data streams using machine learning to detect anomalies and predict threats. When something suspicious is flagged, the system can respond instantly by quarantining data, blocking traffic, and alerting security teams.That kind of automation is essential for minimizing damage and downtime.With full infrastructure control, organizations can deploy bespoke monitoring systems that align with their threat models. Deep packet inspection, real-time anomaly detection, and behavioral analytics can be easier to configure and maintain on-prem than in shared cloud environments.These systems can also work seamlessly with WAAP and DDoS tools to detect and neutralize threats before they spread. The key is flexibility: whether on-prem or cloud-based, AI-driven security should adapt to your architecture and threat landscape, not the other way around.End-to-end visibility can give security teams a clearer picture and faster response options than generic, one-size-fits-all public cloud security tools.How to combine eon-premises control with cloud scalabilityLet’s be clear: on-premises AI isn’t perfect. It demands upfront investment. It requires skilled personnel to deploy and manage systems. And integrating AI into legacy environments takes thoughtful planning.But today’s tools are helping bridge those gaps. Modern platforms reduce the need for constant manual intervention. They support real-time updates to threat models and detection logic. As a result, security teams can spend more time on strategy and less on maintenance.Meanwhile, the cloud still plays an important role. It offers faster access to new tools, software updates, and next-gen GPU hardware.That’s why many organizations are opting for a hybrid model.Our recommendation: Keep your sensitive, high-priority workloads on-prem. Use the cloud for elastic scale and innovation. Together, they deliver the best of both worlds: performance, control, compliance, and flexibility.Secure your digital infrastructure with Gcore on-premises AI inferenceWhether you’re protecting sensitive data or running high-demand workloads, on-premises AI gives you the control and confidence you need. Securing sensitive data and managing high-demand workloads requires a level of control, performance, and predictability that only on-premises AI infrastructure delivers.Gcore Everywhere Inference Private Deployment makes it easier than ever to bring powerful serverless AI inference capabilities directly into your physical environment. Designed for scalable global performance, Everywhere Inference enables robust and secure multi-tenant AI inference deployments across on-prem and cloud environments, helping you meet data sovereignty requirements, reduce latency, and streamline deployment.Talk to us about your on-prem AI plans

June 9, 2025 4 min read

3 clicks, 10 seconds: what real serverless AI inference should look like

Deploying a trained AI model could be the easiest part of the AI lifecycle. After the heavy lifting of data collection, training, and optimization, pushing a model into production is where “the rubber hits the road”, meaning the business expects to see the benefits of invested time and resources. In reality, many AI projects fail in production because of poor performance stemming from suboptimal infrastructure conditions.There are, broadly speaking, two paths developers can take when deploying inference: DIY, which is time and resource-consuming and requires domain expertise from several teams within the business, or the ever-so-popular “serverless inference” solution. The latter is supposed to simplify the task at hand and deliver productivity, cutting down effort to seconds, not hours. Yet most platforms offering “serverless” AI inference still feel anything but effortless. They require containers, configs, and custom scripts. They bury users in infrastructure decisions. And they often assume your data scientists are also DevOps engineers. It’s a far cry from what serverless was meant to be.At Gcore, we believe real serverless inference means this: three clicks and ten seconds to deploy a model. That’s not a tagline—it’s the experience we built. And it’s what infrastructure leaders like Mirantis are now enabling for enterprises through partnerships with Gcore.Why deployment UX matters more than you thinkServerless inference isn’t just a backend architecture choice. It’s a business enabler, a go-to-market accelerator, an ROI optimizer, a technology democratizer—or, if poorly executed, a blocker.The reality is that inference workloads are a key point of interface between your AI product or service and the customer. If deployment is clunky, you’re struggling to keep up with demand. If provisioning takes too long, latency spikes, performance is inconsistent, and ultimately your service doesn’t scale. And if the user experience is unclear or inconsistent, customers end up frustrated—or worse, they churn.Developers and data scientists don’t want to manage infrastructure. They want to bring a model and get results without becoming cloud operators in the process.Dom Wilde, SVP Marketing, MirantisThat’s why deployment UX is no longer a nice-to-have. It’s the core of your product.The benchmark: 3 clicks, 10 secondsWe built Gcore Everywhere Inference to remove every unnecessary step between uploading a model and running it in production. That includes GPU provisioning, routing, scaling, isolation, and endpoint generation, all handled behind the scenes.The result is what we believe should be the default:Upload a modelConfirm deployment parametersClick deployAnd within ten seconds, you’re serving live inference.For platform teams supporting AI workloads, this isn’t just a better workflow. It’s a transformation.With Gcore, our customers can deliver not just self-service infrastructure but also inference as a product. End users can deploy models in seconds, and customers don’t have to micromanage the backend to support that.Dom Wilde, MirantisSimple frontend, powerful backendIt’s worth saying: simplifying the frontend doesn’t mean weakening the backend. Gcore’s platform is built for scale and performance, offering the following:Multi-tenant GPU isolationSmart routing based on location and loadAuto-scaling based on demandA unified API and UI for both automation and accessibilityWhat makes this meaningful isn’t just the tech, it’s the way it vanishes behind the scenes. With Gcore, Mirantis customers can deliver low-latency inference, maximize GPU efficiency, and meet data privacy requirements without touching low-level infrastructure.Many enterprises and cloud customers worry about underutilized GPUs. Now, every cycle is optimized. The platform handles the complexity so our customers can focus on building value.Dom Wilde, MirantisIf it’s not 3 clicks and 10 seconds, it’s not really serverlessThere’s a growing gap between what serverless inference promises and what most platforms deliver. Many cloud providers are focused on raw compute or orchestration, but overlook the deployment layer. That’s a mistake. Because when it comes to customer experience, ease of deployment is the product.Mirantis saw that early on and partnered with Gcore to bring inference-as-a-service to CSP and enterprise customers, fast. Now, customers can launch new offerings more quickly, reduce operational overhead, and improve the user experience with a simple, elegant deployment path.Redefine serverless AI with GcoreIf it takes a config file, a container, and a support ticket to deploy a model, it’s not serverless—it’s server-less-ish. With Gcore Everywhere Inference, we’ve set a new benchmark: three clicks and ten seconds to deploy AI. And, our model catalog offers a variety of popular models so you can get started right away.Whether you’re frustrated with slow, inefficient model deployments or looking for the most effective way to start using AI for your company, you need Gcore Everywhere Inference. Give our experts a call to discover how we can simplify your AI so you can focus on scaling and business logic.Let’s talk about your AI project

June 5, 2025 3 min read

Run AI inference faster, smarter, and at scale

Training your AI models is only the beginning. The real challenge lies in running them efficiently, securely, and at scale. AI and reality meet in inference—the continuous process of generating predictions in real time. It is the driving force behind virtual assistants, fraud detection, product recommendations, and everything in between. Unlike training, inference doesn’t happen once; it runs continuously. This means that inference is your operational engine rather than just technical infrastructure. And if you don’t manage it well, you’re looking at skyrocketing costs, compliance risks, and frustrating performance bottlenecks. That’s why it’s critical to rethink where and how inference runs in your infrastructure.The hidden cost of AI inferenceWhile training large models often dominates the AI conversation, it’s inference that carries the greatest operational burden. As more models move into production, teams are discovering that traditional, centralized infrastructure isn’t built to support inference at scale.This is particularly evident when:Real-time performance is critical to user experienceRegulatory frameworks require region-specific data processingCompute demand fluctuates unpredictably across time zones and applicationsIf you don’t have a clear plan to manage inference, the performance and impact of your AI initiatives could be undermined. You risk increasing cloud costs, adding latency, and falling out of compliance.The solution: optimize where and how you run inferenceOptimizing AI inference isn’t just about adding more infrastructure—it’s about running models smarter and more strategically. In our new white paper, “How to Optimize AI Inference for Cost, Speed, and Compliance”, we break it down into three key decisions:1. Choose the right stage of the AI lifecycleNot every workload needs a massive training run. Inference is where value is delivered, so focus your resources on where they matter most. Learn when to use pretrained models, when to fine-tune, and when simple inference will do the job.2. Decide where your inference should runFrom the public cloud to on-prem and edge locations, where your model runs, impacts everything from latency to compliance. We show why edge inference is critical for regulated, real-time use cases—and how to deploy it efficiently.3. Match your model and infrastructure to the taskBigger models aren’t always better. We cover how to choose the right model size and infrastructure setup to reduce costs, maintain performance, and meet privacy and security requirements.Who should read itIf you’re responsible for turning AI from proof of concept into production, this guide is for you.Inference is where your choices immediately impact performance, cost, and customer experience, whether you’re managing infrastructure, developing models, or building AI-powered solutions. This white paper will help you cut through complexity and focus on what matters most: running smarter, faster, and more scalable inference.It’s especially relevant if you’re:A machine learning engineer or AI architect deploying models across environmentsA product manager introducing real-time AI featuresA technical leader or decision-maker managing compute, cloud spend, or complianceOr simply trying to scale AI without sacrificing controlIf inference is the next big challenge on your roadmap, this white paper is where to start.Scale AI inference seamlessly with GcoreEfficient, scalable inference is critical to making AI work in production. Whether you’re optimizing for performance, cost, or compliance, you need infrastructure that adapts to real-world demand. Gcore Everywhere Inference brings your models closer to users and data sources—reducing latency, minimizing costs, and supporting region-specific deployments.Our latest white paper, “How to optimize AI inference for cost, speed, and compliance”, breaks down the strategies and technologies that make this possible. From smart model selection to edge deployment and dynamic scaling, you’ll learn how to build an inference pipeline that delivers at scale.Ready to make AI inference faster, smarter, and easier to manage?Download the white paper

June 2, 2025 2 min read

How to comply with NIS2: practical tips and key requirements

The European Union is boosting cybersecurity legislation with the introduction of the NIS2 Directive. The new rules represent a significant expansion in how organizations across the continent approach digital security. NIS2 establishes specific and clear expectations that impact not just technology departments but also legal teams and top decision-makers. It refines old protocols while introducing additional obligations that companies must meet to operate within the EU.In this article, we explain the role and scope of the NIS2 Directive, break down its key security requirements, analyze the anticipated business impact, and provide a checklist of actions that businesses can take to remain in compliance with continually evolving regulatory demands.Who needs to comply with NIS2?The NIS2 Directive applies to essential and important organizations operating within the European Union in sectors deemed critical to society and the economy. NIS2 also applies to non-EU companies offering services within the EU, requiring non-EU companies that offer covered services in the EU without a local establishment to appoint a representative in one of the member states where they operate.In general, organizations with 50 or more employees and an annual turnover above €10M fall under NIS2. Smaller entities can also be included if they provide key services, including energy, transport, banking, healthcare, water supply, digital infrastructure, and public administration.4 key security requirements of NIS2Under the NIS2 Directive, organizations are required to have an integrated approach to cybersecurity. There are 10 basic measures that companies subject to this legislation must follow: risk policies, incident handling, supply-chain security, MFA, cryptography, backups, BCP/DRP, vulnerability management, security awareness, crypto-control, and “informational hygiene”. In this article, we will cover the four most important of them.These four are necessary steps for limiting disruptions and achieving full compliance with stringent regulatory demands. They include incident response, risk management, corporate accountability, and reporting obligations.#1 Incident responseUnder NIS2, a solid incident response is required. Companies must document processes for the detection, analysis, and management of cyber incidents. Additionally, organizations must have a trained team ready to respond quickly when there's a breach, reducing damage and downtime. Having the right plan in place can make the difference between a minor issue and a major disruption.#2 Risk managementContinuous risk evaluation is paramount within NIS2. Businesses should constantly be scouting out internal vulnerabilities and external dangers while following a clear, defined risk management protocol. Regular audits and monitoring help businesses stay a step ahead of future threats.#3 Corporate accountabilityNIS2 emphasizes corporate accountability by requiring clear cybersecurity responsibilities across all management levels, placing direct oversight on executive leadership. Additionally, due to the dependency of most organizations on third-party suppliers, supply chain security is paramount. Executives need to check the security measures of their partners. One weak link in the chain can destroy the entire system, making stringent security measures a prerequisite for all partners to reduce risks.#4 Reporting obligationsTransparency lies at the heart of NIS2. Serious incidents need to be reported promptly to maintain the culture of accountability the directive encourages. Good reporting mechanisms ensure that vital information is delivered to the concerned authorities in a timely manner, akin to formal channels in data protection legislation such as the GDPR.What NIS2 means for applicable organizationsSome of the potential implications of NIS2 include an increased regulatory burden, financial and reputational risks, and operational challenges. These apply to all businesses that are already established in the European Union. With compliance now becoming mandatory in all member states, businesses that have lagged behind in implementing effective cybersecurity measures will be put under increased pressure to improve their processes and systems.Increased regulatory burdenFor most firms, the new directive means a huge increase in their regulatory burden. The broadened scope of the directive applies to more industries, and this may lead to additional administrative tasks. Legal personnel and compliance officers will need to sift through current cybersecurity policies and ensure all parts of the organization are in line with the new requirements. This exercise can entail considerable coordination between different departments, including IT, risk management, and supply chain management.Financial and reputational risksThe penalty for non-compliance is steep. The fines for failure to comply with the NIS2 Directive are comparable to the GDPR fines for non-compliance, up to €10 million or 2% of a company's worldwide annual turnover for critical entities, while important organizations face a fine of up to €7M or 1.4% of their global annual turnover. Financial fines and reputational damage are significant risks that organizations must take into account. A single cybersecurity incident can lead to costly investigations, legal battles, and a loss of trust among customers and partners. For companies that depend on digital infrastructure for their day-to-day operations, the cost of non-compliance can be crippling.Operational challengesNIS2 compliance requires more than administrative change. Firms may have to make investments into new technology when trying to meet the directive's requirements, such as expanded monitoring, expanded protection of data, and sophisticated incident response protocols. Legacy system firms can be put at a disadvantage with the need for rapid cybersecurity improvements.NIS2 compliance checklistDue to the comprehensive nature of the NIS2 Directive, organizations will need to adopt a systematic compliance strategy. Here are 5 practical steps organizations can take to comply:Start with a thorough audit. Organizations must review their current cybersecurity infrastructure and identify areas of vulnerability. This kind of audit helps reveal areas of weakness and makes it easier to decide where to invest funds in new tools and training employees.Develop a realistic incident response plan. It is essential to have a short, actionable plan in place when things inevitably go wrong. Organizations need to develop step-by-step procedures for handling breaches and rehearse them through regular training exercises. The plan needs to be constantly updated as new lessons are learned and industry practices evolve.Sustain continued risk management. Risk management is not a static activity. Organizations need to keep their systems safe at all times and update risk analyses from time to time to combat new issues. This allows for timely adjustments to their approach.Check supply chain security. Organizations need to find out how secure their third-party vendors are. They need to have clear-cut security standards and check periodically to help ensure that all members of the supply chain adhere to those standards.Establish clear reporting channels. Organizations must have easy ways of communicating with regulators. They must establish proper reporting schedules and maintain good records. Training reporting groups to report issues early can avoid delays and penalties.Partner with Gcore for NIS2 successGcore’s integrated platform helps organizations address key security concerns relevant to NIS2 and reduce cybersecurity risk:WAAP: Real-time bot mitigation, API protection, and DDoS defense support incident response and ongoing threat monitoring.Edge Cloud: Hosted in ISO 27001 and PCI DSS-compliant EU data centers, offering scalable, resilient infrastructure that aligns with NIS2’s focus on operational resilience and data protection.CDN: Provides fast, secure content delivery while improving redundancy and reducing exposure to availability-related disruptions.Integrated ecosystem: Offers unified visibility across services to strengthen risk management and simplify compliance.Our infrastructure emphasizes data and infrastructure sovereignty, critical for EU-based companies subject to local and cross-border data regulation. With fully-owned data centers across Europe and no reliance on third-party hyperscalers, Gcore enables businesses to maintain full control over where and how their data is processed.Explore our secure infrastructure overview to learn how Gcore’s ecosystem can support your NIS2 compliance journey with continuous monitoring and threat mitigation.Please note that while Gcore’s services support many of the directive’s core pillars, they do not in themselves guarantee full compliance.Ready to get compliant?NIS2 compliance doesn’t have to be overwhelming. We offer tailored solutions to help businesses strengthen their security posture, align with key requirements, and prepare for audits.Interested in expert guidance? Get in touch for a free consultation on compliance planning and implementation. We’ll help you build a roadmap based on your current security posture, business needs, and regulatory deadlines.Get a free NIS2 consultation

May 27, 2025 5 min read

Securing vibe coding: balancing speed with cybersecurity

Vibe coding has emerged as a cultural phenomenon in 2025 software development. It’s a style defined by coding on instinct and moving fast, often with the help of AI, rather than following rigid plans. It lets developers skip exhaustive design phases and dive straight into building, writing code (or prompting an AI to write it) in a rapid, conversational loop. It has caught on fast and boasts a dedicated following of developers hosting vibe coding game jams.So why all the buzz? For one, vibe coding delivers speed and spontaneity. Enthusiasts say it frees them to prototype at the speed of thought, without overthinking architecture. A working feature can be blinked into existence after a few AI-assisted prompts, which is intoxicating for startups chasing product-market fit. But as with any trend that favors speed over process, there’s a flip side.This article explores the benefits of vibe coding and the cybersecurity risks it introduces, examines real incidents where "just ship it" coding backfired, and outlines how security leaders can keep up without slowing innovation.The upside: innovation at breakneck speedVibe coding addresses real development needs and has major benefits:Allows lightning-fast prototyping with AI assistance. Speed is a major advantage, especially for startups, and allows faster validation of ideas and product-market fit.Prioritizes creativity over perfection, rewarding flow and iteration over perfection.Lowers barriers to entry for non-experts. AI tooling lowers the skill floor, letting more people code.Produces real success stories, like a game built via vibe coding hitting $1M ARR in 17 days.Vibe coding aligns well with lean, agile, and continuous delivery environments by removing overhead and empowering rapid iteration.When speed bites backVibe coding isn’t inherently insecure, but the culture of speed it promotes can lead to critical oversights, especially when paired with AI tooling and lax process discipline. The following real-world incidents aren’t all examples of vibe coding per se, but they illustrate the kinds of risks that arise when developers prioritize velocity over security, skip reviews, or lean too heavily on AI without safeguards. These three cases show how fast-moving or under-documented development practices can open serious vulnerabilities.xAI API key leak (2025)A developer at Elon Musk’s AI company, xAI, accidentally committed internal API keys to a public GitHub repo. These keys provided access to proprietary LLMs trained on Tesla and SpaceX data. The leak went undetected for two months, exposing critical intellectual property until a researcher reported it. The error likely stemmed from fast-moving development where secrets were hardcoded for convenience.Malicious NPM packages (2024)In January 2024, attackers uploaded npm packages like warbeast2000 and kodiak2k, which exfiltrated SSH keys from developer machines. These were downloaded over 1,600 times before detection. Developers, trusting AI suggestions or searching hastily for functionality, unknowingly included these malicious libraries.OpenAI API key abuse via Replit (2024)Hackers scraped thousands of OpenAI API keys from public Replit projects, which developers had left in plaintext. These keys were abused to access GPT-4 for free, racking up massive bills for unsuspecting users. This incident shows how projects with weak secret hygiene, which is a risk of vibe coding, become easy targets.Securing the vibe: smart risk mitigationCybersecurity teams can enable innovation without compromising safety by following a few simple cybersecurity best practices. While these don’t offer 100% security, they do mitigate many of the major vulnerabilities of vibe coding.Integrate scanning tools: Use SAST, SCA, and secret scanners in CI/CD. Supplement with AI-based code analyzers to assess LLM-generated code.Shift security left: Embed secure-by-default templates and dev-friendly checklists. Make secure SDKs and CLI wrappers easily available.Use guardrails, not gates: Enable runtime protections like WAF, bot filtering, DDoS defense, and rate limiting. Leverage progressive delivery to limit blast radius.Educate, don’t block: Provide lightweight, modular security learning paths for developers. Encourage experimentation in secure sandboxes with audit trails.Consult security experts: Consider outsourcing your cybersecurity to an expert like Gcore to keep your app or AI safe.Secure innovation sustainably with GcoreVibe coding is here to stay, and for good reason. It unlocks creativity and accelerates delivery. But it also invites mistakes that attackers can exploit. Rather than fight the vibe, cybersecurity leaders must adapt: automating protections, partnering with devs, and building a culture where shipping fast doesn't mean shipping insecure.Want to secure your edge-built AI or fast-moving app infrastructure? Gcore’s Edge Security platform offers robust, low-latency protection with next-gen WAAP and DDoS mitigation to help you innovate confidently, even at speed. As AI and security experts, we understand the risks and rewards of vibe coding, and we’re ideally positioned to help you secure your workloads without slowing down development.Into vibe coding? Talk to us about how to keep it secure.

May 21, 2025 3 min read

Qwen3 models available now on Gcore Everywhere Inference

We’ve expanded our model library for Gcore Everywhere Inference with three powerful additions from the Qwen3 series. These new models bring advanced reasoning, faster response times, and even better multilingual support, helping you power everything from chatbots and coding tools to complex R&D workloads.With Gcore Everywhere Inference, you can deploy Qwen3 models in just three clicks. Read on to discover what makes Qwen3 special, which Qwen3 model best suits your needs, and how to deploy it with Gcore today.Introducing the new Qwen3 modelsQwen3 is the latest evolution of the Qwen series, featuring both dense and Mixture-of-Experts (MoE) architectures. It introduces dual-mode reasoning, letting you toggle between “thinking” and “non-thinking” modes to balance depth and speed:Thinking mode (enable_thinking=True): The model adds a <think>…</think> block to reason step-by-step before generating the final response. Ideal for tasks like code generation or math where accuracy and logic matter.Non-thinking mode (enable_thinking=False): Skips the reasoning phase to respond faster. Best for straightforward tasks where speed is a priority.Model sizes and use casesWith three new sizes available, you can choose the level of performance required for your use case:Qwen3-14B: A 14B parameter model tuned for responsive, multilingual chat and instruction-following. Fast, versatile, and ready for real-time applications with lightning-fast responses.Qwen3-30B-A3B: Built on the Arch-3 backbone, this 30B model offers advanced reasoning and coding capabilities. It’s ideal for applications that demand deeper understanding and precision while balancing performance. It provides high-quality output with faster inference and better efficiency.Qwen3-32B: The largest Qwen3 model yet, designed for complex, high-performance tasks across reasoning, generation, and multilingual domains. It sets a new standard for what’s achievable with Gcore Everywhere Inference, delivering exceptional results with maximum reasoning power. Ideal for complex computation and generation tasks where every detail matters.ModelArchitectureTotal parametersActive parametersContext lengthBest suited forQwen3-14BDense14B14B128KMultilingual chatbots, instruction-following tasks, and applications requiring strong reasoning capabilities with moderate resource consumption.Qwen3-30B-A3BMoE30B3B128KScenarios requiring advanced reasoning and coding capabilities with efficient resource usage; suitable for real-time applications due to faster inference times.Qwen3-32BDense32B32B128KHigh-performance tasks demanding maximum reasoning power and accuracy; ideal for complex R&D workloads and precision-critical applications.How to deploy Qwen3 models with Gcore in just a few clicksGetting started with Qwen3 on Gcore Everywhere Inference is fast and frictionless. Simply log in to the Gcore Portal, navigate to the AI Inference section, and select your desired Qwen3 model. From there, deployment takes just three clicks—no setup scripts, no GPU wrangling, no DevOps overhead. Check out our docs to discover how it works.Deploying Qwen3 via the Gcore Customer Portal takes just three clicksPrefer to deploy programmatically? Use the Gcore API with your project credentials. We offer quick-start examples in Python and cURL to get you up and running fast.Why choose Qwen3 + Gcore?Flexible performance: Choose from three models tailored to different workloads and cost-performance needs.Immediate availability: All models are live now and deployable via portal or API.Next-gen architecture: Dense and MoE options give you more control over reasoning, speed, and output quality.Scalable by design: Built for production-grade performance across industries and use cases.With the latest Qwen3 additions, Gcore Everywhere Inference continues to deliver on performance, scalability, and choice. Ready to get started? Get a free account today to explore Qwen3 and deploy with Gcore in just a few clicks.Sign up free to deploy Qwen3 today

May 15, 2025 2 min read

Run AI workloads faster with our new cloud region in Southern Europe

Good news for businesses operating in Southern Europe! Our newest cloud region in Sines, Portugal, gives 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.Strategically positioned in Portugal, Sines-2 enhances 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, this new region is designed to meet escalating demand with cutting-edge GPU and storage capabilities.Built for AI, designed to scaleSines-2 brings with it 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 is an easy way to access state-of-the-art GPUs with simplified compliance. Whether you're building AI models, running simulations, or managing rendering pipelines, Sines-2 offers the performance and proximity you need.And best of all, servers are available and ready to deploy today.Run your AI workloads in Portugal todayWith Sines-2 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 today

May 12, 2025 2 min read

Edge Cloud news: more regions and volume options available

At Gcore, we’re committed to delivering high-performance, globally distributed infrastructure that adapts to your workloads—wherever they run. This month, we’re excited to share major updates to our Edge Cloud platform: two new cloud IaaS regions in Europe and expanded storage options in São Paulo.New IaaS regions in Luxembourg and Portugal available nowLuxembourg‑3 and Sines‑2 mark the next step in the Gcore mission to bring compute closer to users. From compliance-focused deployments in Central Europe to GPU‑powered workloads in the Iberian Peninsula, these new regions are built to support diverse infrastructure needs at scale.Luxembourg‑3: expanding connectivity in Central EuropeWe’re expanding our European footprint by opening an additional IaaS point of presence (PoP) in Luxembourg. Strategically located in the heart of Europe, this region offers low-latency connectivity across the EU and is a strong compliance choice for data residency requirements.Here’s what’s available in Luxembourg‑3:Virtual Machines: High-performance, reliable, and scalable compute power for a wide range of workloads - with free egress traffic and pay-as-you-go billing for active instances only.Volumes: Standard, High IOPS, and Low Latency block storage for any workload profile.Load Balancers: Distribute traffic intelligently across instances to boost availability, performance, and fault tolerance.Managed Kubernetes: Fully managed Kubernetes clusters with automated provisioning, scaling, and updates optimized for production-ready deployments.Sines‑2, Portugal: a new hub for Southern Europe and a boost for AI workloadsWe’re also opening a brand-new location: Sines‑2, Portugal. This location enhances coverage across Southern Europe and boosts our AI and compute capabilities with more GPU availability.In addition to offering the same IaaS services as Luxembourg‑3, Sines‑2 also includes:H100 NVIDIA GPUs for AI/ML, high-performance computing, and rendering workloads.New VAST NFS Fileshare support for scalable, high-throughput file storage.This new region is ideal for organizations looking to deploy close to the Iberian Peninsula, reducing latency for regional users while gaining access to powerful GPU resources.Enhanced volume types in São PauloVolumes are the backbone of any cloud workload. They store the OS, applications, and essential data for your virtual machines. Developers and businesses building latency-sensitive or I/O-intensive applications now have more options in the São Paulo-2 region, thanks to two newly added volume types optimized for speed and responsiveness:Low-latency volumesDesigned for applications where every millisecond matters, Low Latency Volumes are non-redundant block storage ideal for:ETCD clustersTransactional databasesOther real-time, latency-critical workloadsBy minimizing overhead and focusing on speed, this volume type delivers faster response times for performance-sensitive use cases. This block storage offers IOPS up to 5000 and an average latency of 300 microseconds.High-IOPS volumesFor applications that demand both speed and resilience, High IOPS Volumes offer a faster alternative to our Standard Volumes:Higher IOPS and increased throughputSuitable for high-traffic web apps, analytics engines, and demanding databasesThis volume type accelerates data-heavy workloads and keeps performance consistent under peak demand by delivering significantly higher throughput and IOPS. The block storage offers IOPS up to 9,000 and a 500 MB/s bandwidth limit.Ready to deploy with Gcore?These new additions help to fine-tune your performance strategy, whether you're optimizing for throughput, latency, or both.From scaling in LATAM to expanding into the EU or pushing performance at the edge, Gcore continues to evolve with your needs. Explore our new capabilities in Luxembourg‑3, Sines‑2, and São Paulo‑2.Discover more about Gcore Cloud Edge Services

May 7, 2025 2 min read

How AI is improving L7 DDoS protection solutions

How AI is improving L7 DDoS protection solutionsDDoS attacks have always been a concern for organizations, but with the recent rise of AI and machine learning, the threat has grown. Layer 7 attacks are particularly damaging, as they focus on the application layer that users utilize to interact with your system. Unlike traditional DDoS attacks, which aim to overwhelm the servers with sheer traffic, these advanced threats imitate real user behavior, making it incredibly difficult for defenses to identify and block malicious traffic.While this challenge is complex, it is far from insurmountable. In this situation, the mantra "fight fire with fire" really applies. By using machine learning and AI against AI-based attacks, organizations can then retaliate with equally advanced Layer 7 protection. These newer technologies can offer something beyond what more traditional techniques could hope to achieve, including significantly faster response times, smarter threat detection, and precision. Here’s how AI and ML are redefining how businesses stay online and secure.Why L7 DDoS attacks are dangerous and hard to stopL7 DDoS attacks are sneaky. Unlike network-based attacks that flood your bandwidth, these attacks go after your application logic. Picture thousands of fake users trying to log in, search for products, or complete transactions all at once. Your systems become overwhelmed, not because they’re receiving a massive amount of data, but because they’re handling what looks like genuine traffic.The big challenge is filtering out the bad traffic while letting legitimate users through. After all, if you accidentally block real customers, you’re essentially doing the attackers’ job for them.Manual defenses used in the past, such as rate limiting with static thresholds, can result in a lose-lose situation. When the threshold is set too high, attackers can enter, often in place of traditional users. If the threshold is set too low, legitimate users are left unable to access the application. This acts as a collective punishment, blocking users out of fear of a few malicious actors rather than an accurate solution that can identify the malicious activity and block it without compromising users’ experience. Traditional defenses, based on static rules or human intervention, simply cannot scale at the speed and intricacy of a modern attack. They’re reactive when they need to be proactive.Filtering traffic without blocking customersAI and ML avoid the pitfalls of traditional security systems by continuously analyzing traffic and identifying anomalies dynamically. One of the biggest pain points in DDoS defense is false positives, which block legitimate users because their behavior looks suspicious.Traditional solutions relying on static rules simply block any IPs displaying suspicious behavior, while AI and ML track the activity of IPs over time, building a detailed profile of legitimate traffic. Sometimes referred to as IP profiling, this process groups together the IP addresses that interact predictably and legitimately with your systems. By analyzing both current and historical data, these systems can differentiate suspicious IPs from legitimate users. In the event of an attack, “safe” IPs are automatically allowed through, while suspicious ones are challenged or blocked.These AI systems learn over time from previous attacks they’ve encountered, adapting for greater accuracy without any manual updating or intervention to counter-changing tactics. This allows the systems to correlate current traffic with historical profiles and continuously reassess the safety of certain profiles. This ensures that legitimate accounts can continue to access services unimpeded while malicious traffic is contained.Traditional systems cannot achieve this level of precision, and instead tend to shut down applications during attacks, essentially allowing the attackers to win. With advanced AI and ML based defenses, businesses can maintain their service undisturbed for real users, even during an attack.Fighting AI attacks with AI defensesDDoS attacks are becoming increasingly adaptive, using AI to mimic real users, leaving the static rules in traditional solutions unable to identify the subtle signs of attack traffic. Attackers constantly change their methods to avoid fixed security rules. Manually updating defenses each time a new attack method pops up is time-consuming and inefficient.AI-powered solutions overcome this limitation by using the same strategy as attackers, continuously learning from data input to adapt to increasingly convincing DDoS traffic in real time. This can stop even zero-day and self-evolving AI cyberattacks.Staying Ahead of Attackers With Smarter DefensesOur AI-driven WAAP solution delivers intelligent, interconnected protection, enabling businesses to stay ahead of even the most advanced and evolving threats, including L7 DDoS attacks. By leveraging deep traffic analysis, heuristic tagging, and adaptive learning, it provides a proactive defense strategy. With cross-domain capabilities and actionable security insights, Gcore WAAP is an essential asset for security architects and key decision-makers, seamlessly blending innovation with practicality to meet the demands of today’s digital landscape.Interested in exploring WAAP further? Download our ebook to discover cybersecurity best practices, the most prevalent threats, and how WAAP can protect your business’s digital infrastructure. Or, reach out to our team to learn more about Gcore WAAP.Discover why WAAP is a must-have for modern businesses—get your free ebook

May 5, 2025 3 min read

April 30, 2025 4 min read

How AI is transforming gaming experiences

AI is reshaping how games are played, built, and experienced. Although we are in a period of flux where the optimal combination of human and artificial intelligence is still being ironed out, the potential for AI to greatly enhance both gameplay and development is clear.PlayStation CEO Hermen Hulst recently emphasized the importance of striking the right balance between the handcrafted human touch and the revolutionary advances that AI brings. AI will not replace the decades of design, storytelling, and craft laid down by humans—it will build on that foundation to unlock entirely new possibilities. In addition to an enhanced playing experience, AI is shaking up gaming aspects such as real-time analytics, player interactions, content generation, and security.In this article, we explore three specific developments that are enriching gaming storyworlds, as well as the technology that’s bringing them to life and what the future might hold.#1 Responsive NPC behavior and smarter opponentsAI is evolving to create more realistic, adaptive, and intelligent non-player characters (NPCs) that can react to individual player choices with greater depth and reasoning. The algorithms allow NPCs to respond dynamically to players’ decisions so they can adjust their strategies and behaviors in real time. This provides a more immersive and dynamic gameplay environment and means gamers have endless opportunities to experience new gaming adventures and write their own story every time.A recent example is Red Dead Redemption 2, which enables players to interact with NPCs in the Wild West. Players were impressed by its complexity and the ability to interact with fellow cowboys and bar patrons. Although this is limited for now, eventually, it could become like a video game version of the TV series Westworld, in which visitors pay to interact with incredibly lifelike robots in a Wild West theme park.AI also gives in-game opponents more “agency,” making them more reactive and challenging for players to defeat. This means smarter, more unpredictable enemies who provide a heightened level of achievement, novelty, and excitement for players.For example, AI Limit, released in early 2025, is an action RPG incorporating AI-driven combat mechanics. While drawing comparisons to Soulslike games, the developers emphasize its unique features, including the Sync Rate system, which adds depth to combat interactions.#2 AI-assisted matchmaking and player behavior predictionsAI-powered analytics can identify and predict player skill levels and playing styles, leading to more balanced and competitive matchmaking. A notable example is the implementation of advanced matchmaking systems in competitive games such as Apex Legends and Call of Duty: Modern Warfare III. These titles use AI algorithms to analyze not just skill levels but also playstyle preferences, weapon selections, and playing patterns to create matches optimized for player retention and satisfaction. The systems continuously learn from match outcomes to predict player behavior and create more balanced team compositions across different skill levels.By analyzing a player’s past performance, AI can also create smarter team formations. This makes for fairer and more rewarding multiplayer games, as players are matched with others who complement their skill and strategy.AI can monitor in-game interactions to detect and mitigate toxic behavior. This helps encourage positive social dynamics and foster a more collaborative and friendly online environment.#3 Personalized gaming experiencesMultiplayer games can use AI to analyze player behavior in real time, adjusting difficulty levels and suggesting tailored missions, providing rich experiences unique to each player. This creates personalized, player-centric gameplay that evolves dynamically and can change over time as a player’s knowledge and ability improve.Games like Minecraft and Skyrim already use AI to adjust difficulty and offer dynamic content, while Oasis represents a breakthrough as an AI-generated Minecraft-inspired world. The game uses generative AI to predict and render gameplay frames in real time, creating a uniquely responsive environment.Beyond world generation, modern games are also incorporating AI chatbots that give players real-time coaching and personalized skill development tips.How will AI continue to shape gaming?In the future, AI will continue to impact not just the player experience but also the creation of games. We anticipate AI revolutionizing game development in the following areas:Procedural content generation: AI will create vast, dynamic worlds or generate storylines, allowing for more expansive and diverse game worlds than are currently available.Game testing: AI will simulate millions of player interactions to help developers find bugs and improve gameplay.Art and sound design: AI tools will be used to a greater extent than at present to create game art, music, and voiceovers.How Gcore technology is powering AI gaming innovationIn terms of the technology behind the scenes, Gcore Everywhere Inference brings AI models closer to players by deploying them at the edge, significantly reducing latency for training and inference. This powers dynamic features like adaptive NPC behavior, personalized gameplay, and predictive matchmaking without sacrificing performance.Gcore technology differentiates itself with the following features:Supports all major frameworks, including PyTorch, TensorFlow, ONNX, and Hugging Face Transformers, making deploying your preferred model architecture easy.Offers multiple deployment modes, whether in the cloud, on-premise, or across our distributed edge network with 180+ global locations, allowing you to place inference wherever it delivers the best performance for your users.Delivers sub-50ms latency for inference workloads in most regions, even during peak gaming hours, thanks to our ultra-low-latency CDN and proximity to players.Scales horizontally, so studios can support millions of concurrent inferences for dynamic NPC behavior, matchmaking decisions, or in-game voice/chat moderation, without compromising gameplay speed.Keeps your models and training data private through confidential computing and data sovereignty controls, helping you meet compliance requirements across regions including Europe, LATAM, and MENA.With a low-latency infrastructure that supports popular AI frameworks, Gcore Everywhere Inference allows your studio to deploy custom models and deliver more immersive, responsive player experiences at scale. With our confidential computing solutions, you retain full control over your training assets—no data is shared, exposed, or compromised.Deliver next-gen gaming with Gcore AIAI continues to revolutionize industries, and gaming is no exception. The deployment of artificial intelligence can help make games even more exciting for players, as well as enabling developers to work smarter when creating new games.At Gcore, AI is our core and gaming is our foundation. AI is seamlessly integrated into all our solutions with one goal in mind: to help grow your business. As AI continues to evolve rapidly, we're committed to staying at the cutting edge and changing with the future. Contact us today to discover how Everywhere Inference can enhance your gaming offerings.Get a customized consultation about AI gaming deployment