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As enterprises move AI from experimentation into production, they face a new infrastructure challenge. AI applications, models, and data are no longer confined to a single cloud or data center. Instead, they are distributed across multiple public clouds, on-premises environments, edge locations, and geographic regions.This distributed architecture creates significant operational challenges. Organizations are left managing fragmented infrastructure and disconnected networks, while incurring the high cost of traditional public cloud resources. At the same time, they often lack visibility and control over where enterprise data travels, making it difficult to satisfy data sovereignty requirements, maintain regulatory compliance, and protect sensitive AI workloads.Graphiant and Gcore provide a unified approach to solving these challenges by delivering an enterprise-ready AI infrastructure that combines high-performance AI computing with secure, intelligent networking. Together, the solution provides a more cost-effective and operationally simpler foundation for production AI than traditional public cloud-only architectures. It enables organizations to connect existing data sources across public clouds, private data centers, and edge environments while maintaining complete control over how and where enterprise data moves, helping organizations meet compliance requirements and build truly sovereign AI infrastructure.Gcore delivers scalable GPU infrastructure and AI deployment platforms designed to simplify enterprise AI operations through high-performance AI cloud services and distributed serverless inference capabilities with AI Grid. Graphiant provides secure, cloud-native network connectivity that unifies public cloud, neo-cloud, edge, and enterprise infrastructure into a single operational fabric, eliminating fragmented connectivity and securely connecting enterprise data sources to AI workloads regardless of where they reside.Together, Graphiant and Gcore enable enterprises to securely connect, move, process, and operationalize AI workloads globally while maintaining performance, governance, data sovereignty, and operational simplicity.The AI infrastructure problemAI is creating an entirely new class of infrastructure requirements. As organizations move AI into production, they must support increasingly distributed applications, data, and compute resources while delivering the performance, security, and governance expected of enterprise environments.Modern AI infrastructure requires:High-performance GPU compute and real-time inference. AI workloads demand access to scalable GPU infrastructure capable of supporting both large-scale model training and low-latency inference for real-time AI applications.High-throughput, low-latency data connectivity. AI models depend on moving large volumes of data quickly and efficiently between enterprise data sources, cloud environments, and AI infrastructure without introducing performance bottlenecks.Multi-cloud interoperability. Enterprises need the flexibility to leverage multiple public clouds, neo-cloud providers, private infrastructure, and edge environments without creating isolated operational silos.Compliance and sovereign data controls. Organizations must maintain control over where sensitive data is stored and transmitted to satisfy regulatory requirements, meet data sovereignty mandates, and protect AI workloads from unauthorized access.However, enterprises often struggle with fragmented infrastructure architectures that require separate networking models, provisioning processes, and security frameworks for each cloud or AI provider. At the same time, GPU demand continues to outpace supply, driving organizations toward neo-cloud providers optimized specifically for AI workloads and GPU availability. This creates several major challenges:Complex connectivity between enterprise environments and AI clouds. Connecting data across multiple public clouds, neo-cloud providers, on-premises environments, and edge locations often requires multiple networking technologies and manual integration, increasing complexity and slowing deployments.High cost of data movement and global AI scale. Moving large AI datasets between cloud providers, geographic regions, and enterprise environments generates significant networking and cloud egress costs. As AI deployments expand globally, these costs continue to grow while organizations face increasing complexity in extending AI infrastructure across regions and providers.Security, compliance, and data sovereignty risks. Distributed AI architectures make it more difficult to enforce consistent security policies, maintain visibility into data flows, satisfy regulatory requirements, and ensure sensitive enterprise data remains within approved geographic boundaries.Operational overhead from managing multiple environments. Separate networking, security, and operational models for each cloud and AI platform increase administrative effort, create inconsistent processes, and reduce operational efficiency.Why neo-clouds matterNeo-cloud providers are emerging as a critical layer in the AI ecosystem.Unlike traditional hyperscalers, neo-clouds are purpose-built for AI workloads and optimized for GPU access, AI model training, and inference operations.Gcore's AI infrastructure portfolio includes:GPU Cloud with H100/H200/B300/GB300 GPU infrastructureAI Cloud Stack for AI service providersEverywhere AI for 3-click AI deployment and inferenceCDN-integrated AI Grid routing, supporting latency, cache awareness, and GPU load balancingHybrid and air-gapped deployment modelsGcore supports organizations requiring:Faster AI deploymentLower GPU infrastructure costsReal-time inferenceData sovereigntySecure AI environmentsGlobal AI scalabilityThe connectivity gap in AI infrastructureWhile AI infrastructure innovation has accelerated, networking architectures have lagged. Enterprises adopting AI quickly discover that deploying AI infrastructure extends well beyond provisioning GPU resources. As AI environments become increasingly distributed, organizations face new infrastructure challenges. Provisioning can be slow, and networking across multiple clouds and AI providers adds significant complexity. Many deployments rely on the public internet, creating security and performance concerns. At the same time, maintaining consistent security policies and integrating neo-cloud providers into existing enterprise networks becomes increasingly difficult.As a result, AI infrastructure is no longer simply a compute challenge. It is fundamentally a data movement and connectivity challenge. The ability to securely connect applications, data, users, and AI services across diverse environments has become critical to operationalizing AI at enterprise scale.Graphiant addresses these challenges with a private network fabric that unifies enterprise infrastructure and AI environments. Organizations can connect AI infrastructure to existing data sources without redesigning their networks, they can also do so without deploying additional networking hardware. This creates a secure connectivity layer that scales as AI deployments grow enabling AI workloads and enterprise data to communicate seamlessly across cloud, on-premises, and edge environments.Joint solution overviewGraphiant + GcoreThe combined Graphiant and Gcore architecture enables enterprises to operationalize AI infrastructure faster while simplifying networking, security, and scalability.By combining AI compute with intelligent connectivity, the joint solution enables organizations to build AI infrastructure that is simpler to deploy, more cost-effective to operate, and easier to scale than traditional public cloud-only architectures. Enterprises can securely connect existing data sources to AI workloads while maintaining policy-driven control over how and where enterprise data moves. This helps organizations satisfy compliance requirements, enforce data sovereignty policies, and operationalize AI with confidence.Gcore provides:GPU-as-a-Service multi-tenant infrastructureAI training and inference platformsServerless AI deploymentAI cloud orchestrationEdge AI capabilitiesMulti-region GPU availabilityGraphiant provides:Secure private connectivityCloud and neo-cloud networkingHigh-throughput data transportLow-latency AI networkingEnd-to-end segmentationUnified connectivity policiesGlobal network reachTogether, organizations gain:Simplified AI deploymentFaster onboarding into GPU environmentsConsistent security across AI workloadsSovereign AI infrastructureLower operational complexityAccelerated global AI expansionUse case: SaaS AI workloadsModern AI-driven creative applications require massive GPU resources, rapid data movement, and globally distributed inference environments.A deployment model for cloud-native AI-driven platforms (like cloud-based graphic design platforms) may involve:AI image generationLarge-scale inference workloadsGlobal user trafficMulti-region GPU utilizationHigh-throughput asset movementSecure processing pipelinesIn these environments:Gcore provides scalable GPU compute and AI inference capabilitiesGraphiant provides secure, performant connectivity between enterprise infrastructure, cloud environments, and neo-cloud GPU servicesThis allows organizations to:Avoid public internet bottlenecksImprove AI response timesMaintain secure segmentation policiesScale globally without redesigning infrastructureOperate production AI workloads cost-effectivelySecurity and sovereignty for AIAI workloads increasingly involve sensitive enterprise and customer data. For many enterprises and government agencies, controlling where sensitive data is processed and where it travels is becoming a critical requirement. AI workloads supporting regulated, classified, or mission-critical operations often require compute infrastructure that can be deployed within approved jurisdictions, while ensuring enterprise data never leaves authorized sovereign boundaries. This requires a solution that combines sovereign AI compute with policy-driven control over data movement, enabling organizations to meet regulatory and national security requirements without compromising performance or operational simplicity.Graphiant's networking platform provides:End-to-end encryptionZero Trust architectureMicro-segmentationPolicy-based connectivitySecure edge-to-cloud transportData sovereignty controls for data in motionGcore complements this with:Air-gapped deployment supportPrivate inference environmentsRegional deployment optionsMulti-tenancy controlsSecure AI cloud operationsTogether, the combined architecture enables sovereign AI infrastructure that balances performance, compliance, and operational simplicity.Key business outcomesOrganizations leveraging Graphiant and Gcore together can achieve:Faster AI deployment. Rapid onboarding into GPU infrastructure and AI environments without lengthy network provisioning.Lower infrastructure complexity. Unified connectivity and AI operations reduce operational overhead across environments.Improved AI performance. Low-latency private connectivity supports real-time inference and large-scale data movement.Enhanced security. Consistent segmentation and Zero Trust architecture protect sensitive AI workloads.Global scalability. Expand AI infrastructure across regions without redesigning networking architectures.Better cost efficiency. Optimize GPU utilization and reduce expensive cloud networking overhead.Data sovereignty and compliance. Maintain control over where AI workloads are processed and where enterprise data travels to satisfy regulatory, contractual, and sovereign data requirements.Target industriesThe combined Graphiant + Gcore solution is especially valuable for industries with:AI-intensive workloadsRegulatory requirementsLatency-sensitive applicationsDistributed infrastructureIncluding:Financial servicesHealthcarePublic sector/government agenciesTelecommunicationsRetailAI-native SaaS platformsConclusionAI infrastructure is evolving beyond traditional cloud architectures.Enterprises now require:Flexible GPU accessSecure multi-environment connectivitySovereign AI operationsReal-time performanceSimplified scalabilityGcore and Graphiant together help organizations bridge the gap between AI compute infrastructure and enterprise networking.By combining Gcore's AI cloud and GPU platform capabilities with Graphiant's secure network fabric, enterprises can accelerate AI adoption while maintaining control, security, and operational efficiency.The future of AI infrastructure will depend not only on compute power but on the ability to securely move and operationalize data across distributed environments at global scale.That future requires both AI infrastructure and intelligent connectivity working together.

Panels about AI sovereignty tend to follow a predictable arc. Someone invokes GDPR. Someone else mentions hyperscalers. A politician says something optimistic. Everyone applauds and goes home.Last week's Gcore AI panel in Luxembourg didn't go that way.We had Christos Floros — founder of Monnett, former political candidate, and one of the more honest voices I've heard on the intersection of technology and governance — alongside Clara Ulken from our own team. The audience was full of founders, investors, and operators who are actually building things. And the conversation got uncomfortable in all the right ways.Here's what I actually took away — not the polished version, but the substance.First, who controls AI today? (Nobody in this room)We opened with a live audience poll: who controls AI today — US hyperscalers, governments, or private enterprises?The result was essentially: US hyperscalers, and also private enterprises, and those two categories overlap almost completely. Nobody selected governments. The response to that option landed somewhere between a polite laugh and a collective wince.It's worth sitting with that for a moment. We're building policy frameworks, funding initiatives, running regulatory consultations — and the people in the room, the practitioners, don't believe governments are meaningfully in control of the technology those policies are meant to govern. That's not cynicism. That's an accurate read of the current state.The follow-up question — is China a factor? — got a more nuanced answer. The US dominates, clearly. But writing off China as irrelevant would be a mistake. Christos noted that Mistral remains one of Europe's few genuine bright spots in foundation models, and the audience felt the fragility of that position without anyone needing to say it directly.What is sovereignty, actually?Before you can have a serious conversation about sovereign AI, you need a working definition of sovereignty. It sounds obvious. It isn't.Christos gave what I thought was the most honest framing of the evening. He invoked the Greek revolutionary motto — Eleftheria i Thanatos, freedom or death — not as theatre, but as a genuine philosophical anchor. Sovereignty is the freedom to self-govern. The freedom to decide what kind of society you want to be. The freedom to shape, rather than merely consume, the tools that increasingly make decisions on your behalf."If we're not able to shape the very tools that are shaping our decision-making," he said, "then we're not free."That's a harder standard than most AI sovereignty discussions apply. Most conversations focus on data residency and regulatory jurisdiction. Those matter — but they're the floor, not the ceiling. The deeper question is whether Europe is building the capacity to shape the direction of AI development, not just the conditions under which foreign-built AI gets deployed here.Clara brought it back to the operational: sovereignty for AI means controlling where your data lives, how it's accessed, and who has legal authority to compel its disclosure. That last point is the one that actually changes business decisions. Under the US CLOUD Act, American cloud providers are required to produce customer data when served with a valid US legal order — regardless of where the data is physically stored. A German company's files on AWS servers in Frankfurt are not protected from that obligation by their European location. European providers operating under EU law face no equivalent compulsion from US authorities.That distinction is increasingly driving real infrastructure decisions. It's not abstract sovereignty. It's a concrete legal risk that procurement teams are starting to model.Sovereignty is not isolation — and the distinction mattersOne of the more productive moments came when Leo, our moderator, floated an analogy: is sovereignty like an island, where everything made inside stays inside?Both panelists pushed back, and I think they were right to.Christos: "Being interdependent just means being self-sufficient enough to act as a global player — to trade with others, to exchange goods and services — without being absolutely dependent. The moment you become dependent, you're no longer sovereign."Clara: "When we talk about European sovereign AI, we're not talking about an isolated European sovereign AI. It's about Europe being sovereign — having the ability to control its own narrative — while also supplying those services to the world. There are plenty of US companies that don't want to be beholden to the Cloud Act and would therefore like to store their data in Europe."This is an important distinction for how Gcore thinks about our role. We're not building infrastructure for Europe to retreat behind. We're building infrastructure that allows European companies — and global companies that value European standards — to operate without being structurally dependent on providers subject to foreign legal authority. The goal is competitive participation in the global market, not withdrawal from it.Europe is already in the game — further than the narrative suggestsA recurring frustration I have with public discourse on this topic is the framing of European AI infrastructure as a future ambition rather than a present reality.It isn't.Gcore operates tens of thousands of GPUs across data centers in Europe and globally. We were among the European companies highlighted at Nvidia's GTC conference in San Jose this year — alongside Nebius, which recently announced a 310 megawatt AI factory project in Finland. These are not pilot programs or announcements about future capacity. This is infrastructure that exists and is serving real demand right now.The global AI infrastructure shortage is real — but it's global. US companies are struggling to find compute just as much as European ones. The narrative that Europe is uniquely behind ignores the fact that demand has simply outpaced supply everywhere. What's different about Europe is that our regulatory environment, so often framed as the obstacle, is increasingly the reason companies want to be here.GDPR compliance used to be a compliance cost. It's increasingly a sales feature.The 75 million euro reality checkDuring the panel, I referenced the Euro 3C project — a pan-European AI infrastructure initiative led by Telefónica, involving 70 organizations across Europe, currently seeking €75 million in public funding to connect AI infrastructure across the continent.The audience reaction was immediate. Christos: "75 million doesn't sound like enough." Clara: "75 million is an astronomically small number when it comes to deploying AI infrastructure at this scale."To put it in context: a single hyperscale data center with meaningful GPU capacity costs hundreds of millions to build and equip. A major AI training cluster can run into the billions. Microsoft alone has committed $80 billion to AI infrastructure investment in a single year. €75 million, spread across 70 organizations and multiple countries, is not a rounding error — it is structurally insufficient to change the competitive landscape.But here's where I think the more useful conversation is: the question isn't whether €75 million is enough to build one European AI supercomputer. It's whether that's even the right model.Clara made the point clearly: the future of European AI infrastructure is not one centrally governed supercomputer. It's a distributed architecture — national and regional projects across Spain, Luxembourg, Germany, Finland, and elsewhere — operating under a common regulatory framework with consistent data standards and governance requirements. The European Commission's AI Factories initiative points in this direction: fund distributed projects, require regulatory compliance, let the private sector execute.That's a model that can actually work. Not because €75 million is enough, but because the right architecture doesn't require a single massive bet — it requires consistent standards applied across many funded projects.The regulation speed problem — an honest assessmentI want to be straight about something that tends to get softened in official Gcore messaging.Europe's regulatory process is slow. Not slow in a bureaucratic-caricature way, but slow in the specific sense that matters for technology: the world that a regulation is designed to govern often looks different by the time the regulation takes effect.Leo used the European Green Deal as his example — a framework designed in 2019 to address energy problems from 2015-2017, that arrived just as COVID, the Russian invasion of Ukraine, and Strait of Hormuz disruptions fundamentally changed the energy calculus. By the time the policy was operational, it was partially solving a problem that no longer existed while missing new problems it hadn't been designed for.The question for AI regulation is whether the same dynamic plays out — and the honest answer is: probably yes, to some degree. AI is moving faster than energy infrastructure ever did.Christos was blunter than I might be in an official capacity: "We don't have the knowledge. The best, most knowledgeable people don't take the time to get invested in politics. We don't know who's voting on our regulation."That's uncomfortable. It's also accurate. The people with the deepest understanding of large language models, GPU infrastructure, inference economics, and adversarial AI capabilities are, almost without exception, not in the rooms where EU AI regulation gets written. They're at Gcore, at Mistral, at the startups in this room, at the research labs. Getting that expertise into the regulatory process — not as lobbyists, but as genuine technical advisors — is one of the most important things that could happen for European AI governance.That said: the instincts behind European regulation are right. The Smart Spires project in Esch that Clara described — deploying AI compute infrastructure with cameras throughout a city, where European regulation prevents any facial recognition data from ever reaching the servers — illustrates what good regulation actually looks like. It doesn't design the product. It defines what the product cannot do. Private companies build and operate the infrastructure, understanding the market. Regulation sets the hard limits. That division of labor is the model that works.What Mistral and LetzAI tell us about sovereign identityOne of the sharper exchanges came around a question I've been turning over myself: what is the sovereign AI of Luxembourg?Luxembourg has a strategic partnership with Mistral AI — a French company whose largest shareholder is ASML, the Dutch semiconductor equipment manufacturer. Is Mistral "Luxembourg's sovereign AI"? Or is it LetzAI — founded in Luxembourg, built on Gcore infrastructure from the start, Luxembourgish team and founder — that earns that designation?Clara's answer: both are sovereign, and the question of which is "more" sovereign gets into semantics that may not be productive. The operative standard is whether a company operates under EU law, whether its data infrastructure is subject to EU jurisdiction, whether it complies with GDPR. Mistral meets that standard. LetzAI meets it more directly. Both are legitimate expressions of European AI capability.Christos took the federalist view: as long as European companies are operating under EU law and collaborating across borders, he's satisfied. He'd actively prefer cross-border collaboration — companies operating across multiple EU member states — because it reinforces the European project itself.I find myself somewhere in between. There's genuine value in companies that are born sovereign — built from the ground up on European infrastructure, under European legal jurisdiction, by European teams. That's a different thing from companies that are European by compliance rather than by origin. Both matter. They serve different purposes in the ecosystem.The capital gap is where this actually breaks downHere's where I spend a disproportionate amount of my time as Head of EU Sovereignty, because it's where the structural gap is most concrete.Everything about European AI infrastructure — the regulatory framework, the distributed architecture model, the genuine technical capability that exists in companies like Gcore and Nebius, and Mistral — can be undermined by one persistent problem: the availability of patient, risk-tolerant capital for European deep-tech companies.A US startup raising a pre-seed round operates in a market where investors are familiar with deep-tech risk, comfortable with long timescales to liquidity, and operating in a single legal and regulatory jurisdiction. A European equivalent raises in a fragmented landscape, navigating different legal environments across member states, with a smaller pool of investors who have direct experience with infrastructure-scale technology bets.Clara put it directly: "It is much easier for a US startup to get their pre-seed and first series funding than it is for a European company to do so. That's really where I hope to see change — allowing more free market, easier access to funding in Europe."The infrastructure isn't just data centers and GPUs. It's the financial infrastructure that allows companies to build data centers and GPUs. Until the capital environment in Europe moves closer to what exists in the US, we're solving the second-order problem while the first-order problem persists.This is where I'd push back on purely regulatory solutions. Gcore can be sovereign. Mistral can be sovereign. But if the companies that should be building the next layer of the European AI stack can't raise their Series A because there isn't enough patient capital in the ecosystem, the sovereign infrastructure they would have built doesn't exist. The talent stays, but it leaves to build in the US. That's not a regulatory failure — it's a capital markets failure, and it needs different solutions.Choosing European: a responsibility on both sidesThe panel ended with an audience member making what I thought was the most direct point of the evening: "Don't you think it's up to us? We talk about sovereignty, and then we spin up AWS instances because it's easy. Isn't it our job to choose European providers?"Yes. With a condition that Christos stated clearly: "We need to develop better products. Gcore needs to be better than their competitors. At the end of the day, the market decides."That's the only honest version of this argument. Sovereign infrastructure that underperforms isn't sovereign infrastructure that gets used. If European providers — including Gcore — ask companies to make a values-based choice between equivalent options, that's a reasonable ask. If we're asking companies to accept a material performance or cost disadvantage in the name of sovereignty, that's not a sustainable model and it's not one I'd endorse.The obligation runs in both directions. The ecosystem should support European providers. European providers should earn that support by building things that are genuinely better — not just jurisdictionally preferable, but technically excellent, commercially viable, and increasingly indispensable.That's the standard we're working toward.Where I land after this conversationThe European AI infrastructure conversation has matured significantly in the last two years. We've moved past the stage where the main debate was whether Europe should build sovereign infrastructure. The debate now is about how — what model, what funding mechanisms, what governance structures, what timelines.Here's my current read:The distributed model is right. Not one supercomputer, but many sovereign nodes operating under consistent standards. That's both the realistic path and the correct one.The regulatory instincts are right, but the execution needs to catch up. Getting technical expertise into the regulatory process is urgent. The people who understand the technology need to be in the rooms where decisions get made.The capital environment is the most underaddressed constraint. Regulatory reform that makes it easier for European investors to back European deep-tech companies would do more for AI sovereignty than almost any infrastructure initiative.And Europe's current position is stronger than the mainstream narrative suggests. We're not preparing to compete. We're competing. The question is whether we can sustain and accelerate that trajectory.At Gcore, that's the problem we wake up to every day. If you're working on pieces of this — infrastructure, policy, capital formation, applications — I'd genuinely like to talk.Dima Maslennikov is Head of EU Sovereignty at Gcore. Gcore operates global cloud, edge, and AI infrastructure with a focus on sovereign, GDPR-compliant deployment across Europe and worldwide. The views in this post are the author's own.Watch the full video of the event here:

We're excited to announce the alpha release of Gcore Terraform Provider v2 — a complete rewrite of our Terraform provider, built for the way we build today.This isn't an incremental update. It's a new foundation.Why we rewrote itAs Gcore's platform has grown — across Cloud, AI infrastructure, CDN, DNS, and more — we wanted our Terraform provider to keep pace automatically rather than through manual updates. We set out to build something where the provider accurately reflects the API by construction, and where shipping updates is fast. v2 is the result.What's new in v2Auto-generated from our OpenAPI spec — and that changes everything. The v2 provider's schemas, documentation, and validation are generated directly from our API spec using industry-proven code generation tooling. This means the provider stays accurate and in sync with the API, and when we ship new API features, a matching Terraform update can follow quickly.Built on the modern Terraform Plugin Framework. v2 is built on the current Terraform Plugin Framework, aligning us with where the ecosystem is heading and unlocking better performance and long-term maintainability.One unified API client. v2 uses a single, unified Go client — gcore-go — keeping things consistent and easy to maintain across all resources.Plural data sources. v2 adds plural data sources for most resources, making it much easier to query and manage groups of resources declaratively.Import support. Almost all resources now support terraform import, enabling teams to bring existing Gcore infrastructure under Terraform management without starting from scratch.What to know right nowv2 is in alpha. That means:Do not use it in production yet. Breaking changes between releases should be expected while the provider is in alpha.The existing provider (v0.*) remains fully supported. Documentation is still available and nothing is going away. We'll announce beta and General Availability milestones separately as v2 matures.Both versions live in the same GitHub repository: the master branch holds the v0 provider, and the v2 branch holds the new one.Migrating from v0 to v2We know many of you have been using the v0 provider extensively — particularly for Cloud and AI workloads. We're committed to making the migration as smooth as possible.Migration guidelines will be published as part of the v2 provider documentation. That said, we don't expect a fully automated migration path — Terraform configurations tend to be specific to how each team structures their infrastructure. For customers who want hands-on help, our Developer Experience team is happy to work through it with you directly, based on your specific setup. Don't hesitate to contact our support team via the customer portal.Part of a bigger pictureThis release is the next step in our broader push to improve developer experience across the Gcore platform. Earlier this year, we launched auto-generated SDKs in both Python and Go, covering all Gcore products. The new Terraform provider builds on that same foundation — one source of truth, the OpenAPI spec, powering everything.What's next? A Gcore CLI. Watch this space.Explore the Gcore Terraform Provider v2 →