InfoWorld

There is a quiet war happening underneath the AI boom, and most people are not paying attention to the right front. While headlines focus on which model is winning, which startup is raising money, or which chatbot is gaining users, a parallel race is playing out in data centers and corporate boardrooms across the world. The race is for AI infrastructure: the GPU clusters, power contracts, cooling systems, networking fabric, orchestration tools, and inference services that make modern AI actually work. The companies fighting in that race are called neoclouds. Understanding the importance of neoclouds matters for the same reason understanding cloud computing mattered in 2008. The companies that first saw the shift to cloud and positioned themselves around it correctly ended up controlling a massive layer of the technology industry. The same dynamics are playing out in AI infrastructure right now. But the neocloud market is more complicated than it looks. Not all of these companies do the same thing. Grouping them together as if they are all just GPU rental businesses misses the point entirely. The more useful way to understand the space is to recognize that neoclouds are evolving into four distinct categories, each with a different focus, a different customer, and a different role in the AI stack. ## What a neocloud actually is Before getting into categories, it helps to establish what the term means. A neocloud is a cloud infrastructure provider designed specifically for AI workloads. That sounds simple, but it covers a lot of ground. General-purpose hyperscalers like AWS, Azure, and Google Cloud were engineered to support a wide variety of workloads across many industries. They built architecture around flexibility, breadth of services, and broad enterprise appeal. Neoclouds took a different approach. They built around one mandate: make AI compute as fast, dense, and scalable as possible. That means designing facilities around high power density, integrating fast interconnects between GPUs, investing in liquid cooling and energy-aware cooling, and building orchestration around tools like Kubernetes and Slurm that are purpose-built for distributed AI training. It also means competing aggressively for access to the latest Nvidia accelerators, because in AI infrastructure, GPU access is often the single most important competitive advantage a company can have. The result is a fragmented but deeply competitive market. Some neoclouds are focused on physical capacity. Some are focused on making GPU compute accessible to developers. Some are building enterprise-grade infrastructure that can compete directly with hyperscalers in specific AI domains. And some are focused on one of the most important long-term bets in AI: inference. Let’s take a closer look at the four categories of neoclouds. ## Full-stack AI infrastructure The first and most prominent category is full-stack AI infrastructure providers. These companies are trying to become the AWS of the AI era, yet they are built from the ground up around GPUs rather than general-purpose compute. Their goal is to own as much of the AI infrastructure experience as possible: raw GPU clusters, networking, storage, orchestration, managed Kubernetes, Slurm scheduling, private clusters, enterprise support, training workloads, and inference services. The companies in this space include CoreWeave, Nebius, Lambda, Crusoe, Nscale, and Fluidstack. CoreWeave has emerged as the clearest example of this category. It built its identity around massive GPU clusters optimized for large-scale AI training, paired with Kubernetes-native orchestration and enterprise contracts worth billions. Nebius took a different path, combining deep engineering expertise with a sovereign and European-oriented strategy while expanding heavily into US data centers. Lambda started as a developer and researcher-friendly GPU cloud for machine learning but has moved deliberately upmarket, adding superclusters and private cluster services. ## Developer and self-serve GPU clouds The second category is developer and self-serve GPU clouds. These platforms are not competing for billion-dollar enterprise contracts. They are competing for developers, startups, researchers, and smaller teams who need GPU access quickly without negotiating long-term deals. The product experience here is self-serve: pick a GPU, spin up an instance, deploy a model, run a notebook, test a workload, or launch a small cluster. The brands that fit here include Vultr, Runpod, Civo, Lambda, and Together AI. Vultr brings GPU compute into a broader cloud platform with a global footprint and self-service deployment. Runpod has built a loyal developer following by keeping the experience simple and fast. Civo targets developers who prefer a Kubernetes-first environment. Lambda still offers strong self-serve GPU instances and 1-Click Clusters alongside its enterprise offerings. Together AI goes beyond raw GPU rental to combine inference, fine-tuning, model hosting, and dedicated clusters in one developer-accessible platform. This category matters because not every AI team needs thousands of GPUs and a dedicated account manager. A lot of important work happens at the research bench, the startup prototype stage, and the individual developer level. These clouds serve that market, and they often grow into larger accounts as teams scale. ## Inference-first AI clouds The third and increasingly important category is inference-first AI clouds. Training gets the dramatic headlines, but inference is where AI products actually live. Every chatbot response, coding assistant completion, voice agent interaction, and image generation request is inference. And unlike training, which happens in bursts, inference runs all day, every day, for every user. The companies to watch include Together AI, Groq, CoreWeave Inference, Nebius Token Factory, Crusoe Managed Inference, and Nscale Serverless Inference. Together AI has built one of the clearest inference-first platforms in the market, especially for open source and open-weight models, while also supporting fine-tuning and dedicated GPU clusters. Groq stands apart because its identity is tied to ultra-fast inference running on its own language processing unit (LPU) architecture, which inverts the traditional GPU paradigm. CoreWeave Inference extends CoreWeave’s infrastructure story into serverless and dedicated model-serving. Nebius Token Factory and Crusoe Managed Inference represent how full-stack players are adding inference layers to monetize their GPU capacity differently. Nscale Serverless Inference is Nscale’s move into on-demand model serving without infrastructure management. What makes this category so compelling is the economic direction. As AI moves from a handful of large frontier labs into millions of production applications, the demand for inference infrastructure will likely dwarf training demand. Companies that control inference capacity may end up controlling the more durable long-term revenue stream. ## Data-center and capacity-heavy players The fourth category is the most physically grounded. These are the companies that are building and controlling the real estate of AI. This goes beyond GPU clouds in the traditional sense. These companies are acquiring land, securing power contracts, designing data centers, developing campuses, deploying high-density racks, and racing to control capacity that genuinely cannot be replicated quickly. The brands that fit here include Fluidstack, Applied Digital, Core Scientific, Voltage Park, Crusoe, and Nscale. Some of these companies also offer cloud services, but their strategic advantage is fundamentally about commanding physical infrastructure. Fluidstack specializes in delivering large-scale custom GPU cluster capacity and custom data center solutions for enterprises and AI labs. Applied Digital provides hosting infrastructure for AI and high-performance computing at scale. Core Scientific transitioned from crypto-mining data centers into AI infrastructure. Voltage Park is focused on large-scale GPU capacity for AI and research organizations. Crusoe and Nscale appear in this category as well as in others, which gets to the heart of one of the most important points about the neocloud market overall. These categories are not clean boxes. Companies routinely sit across multiple categories, and competitive pressure is pushing most of them to expand their scope. Crusoe is both a cloud provider and a data-center developer. Nscale is both a cloud platform and a major infrastructure builder. The companies that ultimately win may be those that can span the four categories most effectively. ## Why the category framing matters Understanding these categories is not just an academic exercise. It has real consequences for how companies make buying decisions, how investors evaluate the space, and how technology leaders think about AI infrastructure strategy. A company focused on physical capacity is playing a fundamentally different game than one selling managed inference APIs. They may appear in the same technology articles and compete for some of the same headlines, but their economics, customer relationships, and long-term differentiation look very different. From an enterprise buyer perspective, understanding which layer you are actually buying from matters before you sign a contract. Some neoclouds are building long-term platform relationships. Others are offering capacity to fill an immediate gap. The category determines which is which. The neocloud market is young, fast-moving, and genuinely complicated. New entrants, mergers and acquisitions, and platform expansion are all reshaping the landscape in real time. But the underlying logic remains constant. AI infrastructure is becoming a critical layer of the technology stack, and whoever controls that layer will have enormous influence over how AI gets built, deployed, and monetized. The companies in this market are not all the same. Paying attention to what they actually do, rather than just what they call themselves, is how you separate the ones that will shape AI’s future from the ones that were simply first to send a press release.

Yet another security vulnerability has been discovered in GitLab infrastructure, this one a perfect 10 in severity. CVE-2026-85706, the second flaw GitLab has disclosed in just a month, is a maximum-severity vulnerability that allows attackers to read arbitrary files in a single HTTP request. The path traversal flaw results from improper confinement and lack of authentication enforcement in GitLab’s repository commits API, the company reported. Threat actors could exploit it “under certain conditions” and read arbitrary files (credentials, secrets, and other sensitive data) on vulnerable GitLab servers. The company has fixed the vulnerability, which impacts GitLab Community Edition (CE) and Enterprise Edition (EE), and has advised customers with public-facing self-hosted GitLab instances to patch their servers immediately, or remove public access. Experts say the flaw is alarming because GitLab’s DevSecOps platform is used by roughly 50% of the Fortune 100 and has more than 50 million estimated registered users. “GitLab is not simply a source-code repository,” noted Safayat Moahamad, advisory director at Info-Tech Research Group. In many enterprises, it is connected to build pipelines, deployment processes, application security workflows, and other trusted systems. As a result, unauthorized access to configuration files, secrets, or credentials on the GitLab server could “create consequences well beyond the affected instance,” he said. ## Don’t wait for the normal patch cycle GitLab has been a favorite hacker target of late: In January, it patched a high-severity flaw that allowed attackers possessing a target’s account ID to bypass two-factor authentication, and in August, it fixed a critical vulnerability that could give unauthenticated users the ability to make modifications inside code repositories or even completely delete them with a single HTTP request. The current maximum severity bug, CVE-2026-85706, was reported through GitLab’s HackerOne bug bounty program. It affected CE and EE versions 18.7 before 19.1.8, 19.2 before 19.2.6, and 19.3 before 19.3.2. The US Cybersecurity and Infrastructure Security Agency (CISA) has added CVE-2026-85706 to its Known Exploited Vulnerabilities catalog, noting that this type of vulnerability is a frequent attack vector for malicious cyber actors, and particularly poses significant risks to the federal enterprise; watchTowr Intel has reported that it is already observing “in-the-wild probes.” “Based on recent GitLab vulnerabilities, we know the time until indiscriminate exploitation is likely not far away,” it warned. This is not a vulnerability that organizations should leave for the normal patch cycle, Moahamad emphasized. “It offers an unauthenticated path to arbitrary files on a platform that frequently sits at the center of source code, build, and deployment workflows,” he said. Enterprises should patch immediately, hunt for suspicious repository-commits API activity, and investigate whether exposed files contained credentials or secrets that may now require rotation, he advised. Beyond patching, the watchTowr Intel team said defenders should try to identify exploitation attempts by hunting through log files for HTTP POST requests to “/api/v4/projects/{id}/repository/commits/” URIs containing “file.path” parameters. ## CI/CD platforms are critical trust infrastructure Organizations running affected self-managed GitLab CE or EE instances should be most concerned, Moahamad noted. Risk increases where GitLab is connected to sensitive repositories, CI/CD pipelines, cloud environments, or production-deployment processes. The information and/or access that attackers could obtain depends on what the GitLab service can read and what organizations store on the server, he explained. It could include configuration files, secrets, credentials, and other sensitive server-side data. If those files happen to contain usable tokens, keys, or credentials, an attacker could attempt to access connected infrastructure “The flaw directly creates an unauthorized file access risk,” he said. Credential theft, lateral movement, source code exposure, and supply chain compromise are possible follow-on scenarios. Source code and CI/CD platforms must be governed as “critical trust infrastructure,” he said. While patching remains essential, resilience will depend on knowing where platforms can be exposed, limiting what they can access, detecting abnormal API behavior, and having a tested process for investigating and rotating credentials. David Shipley of Beauceron Security described two factors colliding to generate “maximum pain” for GitLab users. First is the vulnerability. “It’s a 10 for a reason: Unauthenticated read access to GitLab client source code,” he said. Second, this especially hurts because developers still have bad habits: way too much code continues to ship or is in production with embedded SSH keys, cloud secrets, tokens, and other valuable data that attackers can use to get into infrastructure, Shipley said. “So for attackers, you’ve got the classics,” he noted: Data exfiltration and extortion, ransomware, cloud infrastructure hijacking for crypto mining, and the stealing of AI tokens and infrastructure to enable other criminal activity. “This level of vulnerability is the code and cloud equivalent of being able to get the credit card number, expiry and security number,” Shipley said. “It opens up a world of criminal data shopping.” The bottom line: “Get those secrets out of production code and adopt modern coding authentication best practices,” he advised. This article originally appeared on CSOonline.

Anthropic has owned up to a fourth security incident involving its AI model, Claude, escaping onto the open internet and attacking other organizations during a test of cybersecurity abilities on what was believed to be a closed system. The company revealed three such incidents in July after a preliminary investigation. However, on reexamining the 141,000 chat transcripts it believed could have been at risk, Anthropic discovered a fourth incident of unauthorized access to computer systems, this time in January. After this discovery, the company instigated a wider search of 481 million transcripts, covering all those from its Frontier Red Team, some non-cyber evaluations, reinforcement learning environments, and more, to see if any other incidents had occurred. So far, this search has only identified the four already-known incidents, it said. It has also reported details of all the previous incidents to the non-profit lab Model Evaluation and Threat Research (METR), which has agreed to conduct an independent investigation. Anthropic is not revealing too many details of its latest discovery. It has contented itself with saying that it was due to a misconfiguration which mistakenly connected to the open internet, when the simulation was meant to be without such access. It also said that it all four faults were with the same evaluation partner. It has asked METR to investigate all the incidents. The company said that this latest revelation was not connected to the Mythos incident reported by the UK’s AI Security Institute last month. News of the latest discovery broke at the same time as a young researcher, Jacob Coxon, dramatically quit Anthropic accusing it and his previous employer, OpenAI, of “acting irresponsibly” and “gambling with our lives” — a warning that has excited many sections of the press. This article first appeared on CSO.

OpenAI on Wednesday introduced a new Agents API that brings the agent harness and infrastructure behind Codex to developers, potentially giving enterprises a way to build custom AI agents while removing much of the orchestration and infrastructure management traditionally required to make such agents work. That reduction in engineering complexity is possible because the Agents API is a managed service, with OpenAI hosting and maintaining the underlying harness and infrastructure. Previously, developers building a custom agent typically had to assemble the components needed to support its work, including an agent runtime, context and session management, tools and external data connections, execution environments, and associated infrastructure. OpenAI itself already offers several of those building blocks through products such as its Responses API, which developers could use to combine models with built-in capabilities including web search, file search and computer use, and its Agents SDK for defining and orchestrating agent workflows. Agents API, which is currently in public beta, in contrast, can help developers design a custom agent in a single API call after they specify the task, model, tools, and the environment, the model provider wrote in a blog post. For that execution environment, developers can choose to run agents in an OpenAI-managed sandbox, on their own infrastructure, or through supported sandbox providers, including Blaxel, Cloudflare, Daytona, DigitalOcean, E2B, Modal, Oracle, Runloop, and Vercel, it added. These options give enterprises flexibility to choose between fully managed environments and deployments within their own VPCs, as well as different approaches to file and secret storage and compute configurations based on their workloads, it further explained. ## The API could reduce time to production The Agents API “significantly reduces” engineering work, helping developers spend more time building the actual business application instead of the agent infrastructure, said Pareekh Jain, principal analyst at Pareekh Consulting. “The main advantage with the Agents API is fewer moving parts. A long-running agent built by hand needs a job queue, a state database, a sandbox fleet, a compaction routine and a retry policy, and someone has to own and page for each of them,” echoed Amit Kumar Jena, AI development head at IT consulting firm Kanerika. That reduction in engineering work, in turn, could help CIOs reduce the time required to roll out an agent in production and subsequently scale it. “Enterprises may need fewer engineers to build the infrastructure around each agent, which should reduce development time,” said Phil Fersht, CEO of HFS Research. That time advantage, Jena said, could be critical for most enterprises as agent development usually stalls between a working demo and a system capable of running unattended for hours, with infrastructure-related issues often becoming the key bottleneck to moving an agent into production. ## Convenience at the cost of lock-in? The reduction in engineering effort and potentially faster path to production, however, does not eliminate the tradeoffs CIOs must consider, including greater reliance on OpenAI for both the underlying agent infrastructure and the models powering it. “Lock-in is the biggest concern. If OpenAI provides the model, context management, tools, orchestration, and execution environment, moving to another platform becomes harder,” Jain pointed out. That dependency could also weaken an enterprise’s negotiating position on pricing and terms, Jena said. There are also issues around data privacy and governance as the new API “doesn’t support Zero Data Retention” even when enterprises use their own sandbox, Jena pointed out. That, in turn, could limit its adoption, especially in industries in the regulated sectors, such as healthcare and BFSI, Fersht said. Rather, Startups, SaaS companies, and enterprises already using OpenAI are more likely to adopt the new API more readily as it reduces development effort, Jain said. “Enterprises pursuing multi-model strategies may also prefer their own independent harness or are likely to take a hybrid approach,” Jain added. Agents API’s adoption could be further limited due to the competition it faces from hyperscalers and rivals, analysts pointed out. “This is a crowded category. Anthropic’s Claude Managed Agents has been in public beta since April. AWS also offers Amazon Bedrock AgentCore, whose managed harness became generally available in June and does the same category of work, meaning the loop, tool execution, context, state, and recovery, while letting you use any model and switch providers mid-session without losing context,” Jena pointed out. Other competing offerings include Microsoft’s Foundry Agent Service and LangGraph, Jena added.

For 15 years, public cloud providers have defined the industry for most enterprises: on-demand, automatically metered services delivered over the open internet that give developers immediate access to storage, databases, compute, application development platforms, and AI capabilities. That model remains dominant because it is simple, well-instrumented, and universally supported. Yet an adjacent reality has always existed. Large-scale, off-market capacity deals where technology companies with surplus GPUs, storage, and compute sold blocks of capacity in bulk to other firms under nondisclosure agreements were once common. Those were cloud transactions in substance, since you were using somebody else’s servers. However, they lacked the automation, metering, and governance that define public cloud services, and they operated entirely outside the frameworks enterprises rely on for budgeting, compliance, and accountability. ## A shadow market becomes visible These arrangements are now becoming more visible and more formalized. Some are even openly auctioned. Meta’s entrance into the cloud capacity space, essentially offering its excess compute infrastructure to outside buyers, represents the maturation of a market that has existed in back rooms for years. Large, multiyear capacity commitments are no longer whispered about during boardroom lunches. They are being announced, financed, and tracked by analysts. This shift is creating a distinct layer in the cloud landscape, one that sits between traditional hyperscalers and true private clouds, and one that enterprises can no longer afford to ignore. The practical implication is that the cloud market you thought you understood now has a parallel track. When a large enterprise needs maximum GPU capacity for model training or inference, it has more options than a year ago, and those options come with genuinely different trade-offs. You can go to a hyperscaler and pay the published rate for metered GPU as a service, receiving a fully managed platform with all the tools, governance, and integration you need. Or you can reach out to a technology provider that happens to have idle or excess GPU capacity and negotiate a bulk deal at a fraction of the published rate. The first option provides predictability and depth of service. The second offers raw cost savings if you have the operational capability to handle less-managed infrastructure. This bifurcation is muddying procurement decisions. The gap in pricing between bulk off-market capacity and public cloud offerings can be substantial. Some analysts cite differentials ranging from 10 to 100 times. But headline GPU rates are only part of the evaluation. Bulk deals tend to come with minimal metering, limited monitoring, and a do-it-yourself culture around incident response and optimization. You are, in effect, purchasing wholesale energy. It is robust and capable, but it requires your own wiring and maintenance. Organizations accustomed to the instrumentation and service-level agreements of hyperscalers may find such a different operational reality a bit jarring. Those with mature cloudops teams may find the economics compelling. ## Who benefits and who adjusts The hyperscale incumbents will not be displaced overnight. They offer the complete ecosystem, not just GPU frameworks but development tools, hundreds of managed services, identity, compliance, and integration layers that are genuinely difficult to replicate. However, they are now facing a market where customers with intensive computational needs can source raw GPU capacity from alternative suppliers, particularly for bursty or cost-sensitive workloads. This is not necessarily a threat to hyperscaler dominance, but it is a complication. The hyperscalers will likely respond through partnerships, capacity-coverage agreements, and possibly acquisitions of promising capacity players. Effectively, they will become customers of the same off-market ecosystem they once ignored. The cloud market is adding a new axis of choice: managed breadth versus specialized efficiency. Enterprises will increasingly construct hybrid operating models, keeping steady-state, security-sensitive, and toolchain-dependent workloads on hyperscaler platforms while offloading cost-intensive training runs and elastic inference to bulk-capacity providers. This is not a binary choice. It is an expanding continuum of sourcing options that demands more sophisticated procurement and architecture planning. ## How to take advantage Know your actual workload cost before you negotiate. If your AI workloads are compute-intensive, model training-heavy, or require burst capacity at unpredictable intervals, the economics of bulk capacity deals can be compelling. Run a pilot that captures total cost of ownership, including data movement, integration effort, operational overhead, and potential downtime during self-managed troubleshooting before you assume bulk is simply cheaper. Compare fully loaded costs, not GPU-hour sticker prices. Build portability into your AI stack from the start. Bulk-capacity providers often lack the governance, compliance tools, and model life-cycle management enterprises need. Mitigate this risk by standardizing on containerized inference runtimes, open model formats, and CI/CD pipelines capable of deploying across multiple targets. This type of discipline turns vendor diversity into negotiating leverage rather than operational liability. It also ensures you can shift workloads as capacity markets fluctuate. Treat capacity sourcing as a strategic procurement function. Bulk cloud capacity is not a niche quirk. It is becoming a recognized market that rewards supply-chain thinking. Diversify your supplier base, negotiate multi-vendor commitments where possible, and develop a reserve strategy for critical workloads. Keep your hyperscaler relationship for reliability and breadth, but maintain at least one secondary capacity arrangement for cost-sensitive or elastic workloads. The enterprises that treat capacity as a variable asset, rather than a fixed infrastructure decision will be better positioned as this market continues to expand and formalize. The cloud market is no longer a simple fork between public and private. A defined middle ground of negotiated bulk capacity is now attracting billions in investment and reshaping how AI infrastructure costs are allocated. Enterprises that understand this landscape and build flexible, portable architectures that can span it will capture more value from AI investments than those who default to the familiar hyperscaler menu alone.