Neoclouds - 분석맨의 '두 번째 뇌'

# My Thoughts - Neoclouds are a newer class of cloud providers that are purpose-built for AI workloads (especially GPU-heavy training/inference), rather than being broad “everything clouds.” - Neoclouds (sometimes stylized as "neo clouds" or "Neocloud") are a new category of specialized cloud providers that emerged primarily in 2024–2025, driven by the massive demand for AI training and inference workloads. - They are **AI-first** (or GPU-first) cloud platforms that focus almost exclusively on providing high-performance computing resources — especially **GPU-as-a-Service (GPUaaS)** — using the latest Nvidia GPUs (like Hopper H100/H200 or Blackwell series) and optimized infrastructure for demanding AI, machine learning, and high-performance computing (HPC) tasks. - Neoclouds are **specialized AI‑focused cloud providers built around dense GPU clusters**, while hyperscalers are **general‑purpose global cloud platforms** (AWS, Azure, Google Cloud) that offer a broad range of services beyond AI. The distinction is becoming strategically important as AI workloads reshape the economics of compute. - Neoclouds (often referred to as Specialized or Alternative Cloud Providers) are a generation of cloud platforms designed to provide high-performance infrastructure specifically optimized for AI, machine learning (ML), and large-scale data processing. ## Why? (목적 - 왜 기록하는가?) ## How? (방법 - 어떻게 활용할까?) ## What? (결과 - 무엇을 창조할까?) # Summary ## ChatGPT [ChatGPT](https://chatgpt.com/c/69a5ddf3-ab98-8329-af20-aa6de63153d1) ==Neoclouds are a newer class of cloud providers that are purpose-built for AI workloads (especially GPU-heavy training/inference), rather than being broad “everything clouds.”== The idea is: fewer services, but very fast access to large GPU clusters, simpler buying/ops for AI teams, and often more flexible capacity sourcing. ([McKinsey & Company](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-evolution-of-neoclouds-and-their-next-moves?utm_source=chatgpt.com "The evolution of neoclouds and their next moves")) Common examples that get labeled neoclouds include CoreWeave, Crusoe, Lambda, and Nebius. ([ABI Research](https://www.abiresearch.com/blog/leading-neocloud-companies?utm_source=chatgpt.com "Profiling Six Leading Neocloud Companies")) How neoclouds differ from hyperscalers - Scope: Neoclouds concentrate on GPU compute + adjacent essentials (storage, networking) for AI. Hyperscalers offer huge catalogs (databases, analytics, serverless, IAM, security, PaaS, SaaS integrations, etc.). ([McKinsey & Company](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-evolution-of-neoclouds-and-their-next-moves?utm_source=chatgpt.com "The evolution of neoclouds and their next moves")) - Capacity model: Neoclouds often build/lease GPU capacity aggressively and may sign large “capacity” deals with hyperscalers or enterprises. Hyperscalers also buy massive GPU fleets, but balance that with broader demand across many services and regions. ([McKinsey & Company](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-evolution-of-neoclouds-and-their-next-moves?utm_source=chatgpt.com "The evolution of neoclouds and their next moves")) - Buying/operating: Neoclouds tend to optimize for “I need a lot of GPUs now” with simpler SKUs and faster provisioning; hyperscalers optimize for enterprise breadth, governance, and long-term platform lock-in. ([Interconnections - The Equinix Blog](https://blog.equinix.com/blog/2025/10/14/what-is-a-neocloud/?utm_source=chatgpt.com "What Is a Neocloud? - Interconnections")) What “hyperscalers” are ==A hyperscaler is a cloud provider operating at massive global scale (data centers, backbone networks, elastic capacity) and delivering a broad portfolio of cloud services. AWS, Microsoft Azure, and Google Cloud are the canonical “big 3,” with other large players often included depending on the definition (e.g., Alibaba, IBM, Oracle).== ([Red Hat](https://www.redhat.com/en/topics/cloud-computing/what-is-a-hyperscaler?utm_source=chatgpt.com "What is a hyperscaler?")) Differences between hyperscalers (practical, decision-oriented) Instead of marketing claims, these are the differences that usually matter in architecture and procurement: 1. Enterprise gravity and “default” integration - AWS: broadest service portfolio and ecosystem depth; commonly the default for cloud-native startups and many enterprises. - Azure: strongest pull where Microsoft stack dominates (Entra ID/Azure AD heritage, Windows/AD, M365, .NET). - Google Cloud: strongest pull where data/ML + Kubernetes-first patterns dominate (GKE reputation, data analytics stack). (High-level comparisons like these are broadly consistent across industry summaries.) ([Coursera](https://www.coursera.org/articles/aws-vs-azure-vs-google-cloud?utm_source=chatgpt.com "What's the Difference Between AWS vs. Azure vs. Google ...")) 2. Data + AI platform shape All three have mature AI offerings, but they package them differently (model hosting, MLOps, managed training, enterprise agent tooling). In 2025–2026 reporting, the “platform AI” layer is a major competitive focus (e.g., AWS Bedrock vs Azure AI offerings vs Google Vertex AI). ([IT Pro](https://www.itpro.com/cloud/cloud-computing/cloud-infrastructure-spending-hit-usd102-6-billion-in-q3-2025-and-aws-marked-its-strongest-performance-in-three-years?utm_source=chatgpt.com "Cloud infrastructure spending hit $102.6 billion in Q3 2025 - and AWS marked its strongest performance in three years")) 3. Networking footprint and multicloud posture Hyperscalers compete on private backbone reach, interconnect, and multicloud connectivity. Example: AWS and Google launched a joint multicloud connectivity service to speed up private links between the two clouds. ([Reuters](https://www.reuters.com/business/retail-consumer/amazon-google-launch-multicloud-service-faster-connectivity-2025-12-01/?utm_source=chatgpt.com "Amazon and Google launch multicloud service for faster connectivity")) 4. Commercials and procurement reality Pricing is complex and highly workload/commit dependent (reserved/committed use, spot/preemptible, egress, managed service premiums). In practice, negotiated enterprise agreements and committed-spend discounts often matter more than list price. When to pick a neocloud vs hyperscaler - Pick a neocloud when: your bottleneck is GPU availability/cost-to-train/inference throughput and you can tolerate a narrower platform (you’re comfortable running more of the stack yourself or via partners). - Pick a hyperscaler when: you need enterprise controls, global regions, deep managed services, integrated security/compliance, and a long runway of platform capabilities beyond GPUs. If you tell me what you’re building (training vs inference, model sizes, regions, compliance constraints, and whether you’re already deep in Microsoft/AWS/GCP), I’ll map this into a concrete shortlist and an evaluation checklist (latency, egress, reserved capacity, failure domains, IAM, and cost model). ## Perplexity [Perplexity](https://www.perplexity.ai/search/what-s-neoclouds-and-what-are-ddP6xcKzTQ2KREVrj8wV2g) Neoclouds are specialized cloud providers designed primarily for AI and high-performance computing workloads, focusing on GPU-centric infrastructure offered as a service. They emerged to meet surging demand for AI compute that traditional providers struggled to supply quickly.rcrtech+2 ### Neoclouds Overview Neoclouds prioritize raw GPU power, high-speed interconnects, and AI-specific optimizations like faster model training and transparent per-hour pricing. Unlike general clouds, they use open-source software, regional data centers (often for sovereign needs), and simpler models without proprietary lock-in.[[youtube](https://www.youtube.com/watch?v=b6o12sOIDI4)]nextdc+1 ### Hyperscalers Defined Hyperscalers like **AWS**, **Microsoft Azure**, and **Google Cloud** are massive, global platforms offering broad general-purpose services rooted in CPU-based compute.nextdc+1 ### Neoclouds vs. Hyperscalers Neoclouds target AI specialists with agility and cost efficiency, while hyperscalers serve diverse enterprise needs with vast ecosystems. |Aspect|Neoclouds|Hyperscalers (AWS, Azure, GCP)| |---|---|---| |Focus|AI/HPC, GPU-first|General-purpose, CPU-dominant| |Scale|Regional, smaller|Global, massive| |Pricing|Transparent GPU-hour|Complex, usage-based tiers| |Software|Open-source heavy|Proprietary managed services| |Deployment|Fast for AI (months)|Broad but GPU wait times| ### Hyperscalers Differences Major hyperscalers vary in strengths: AWS leads in service breadth and reliability, Azure excels in Microsoft integration and hybrid setups, and GCP shines in AI-native tools and data analytics.coursera+2 |Provider|Key Strengths|Pricing Example (2 vCPU/8GB instance/month)| |---|---|---| |AWS|Maturity, regions (34+), recovery|~$69 [[coursera](https://www.coursera.org/articles/aws-vs-azure-vs-google-cloud)]| |Azure|Enterprise hybrid, OpenAI ties|~$70 [[coursera](https://www.coursera.org/articles/aws-vs-azure-vs-google-cloud)]| |GCP|Low latency, BigQuery/ML|~$52 [[coursera](https://www.coursera.org/articles/aws-vs-azure-vs-google-cloud)]| ## Claude [Claude](https://claude.ai/chat/74ebb6a2-bb05-4d16-b221-af4dad44f980) ### Neoclouds vs. Hyperscalers #### What is a Neocloud? Neoclouds are a new class of cloud solution, purpose-built for the AI era. Unlike traditional hyperscalers that offer a wide range of general-purpose services, Neoclouds focus on just one thing: delivering high-performance infrastructure for AI workloads. Many of these companies — like CoreWeave and Crusoe — got their start in crypto mining, where GPU power was essential. As demand for AI exploded, they pivoted, repurposing those GPU fleets to support AI training, inference, and real-time applications. More than 100 neoclouds exist in the world today, with between 10 and 15 operating at meaningful scale in the United States. Their footprint is growing across Europe, the Middle East, and Asia, often backed by venture capital, private equity, or sovereign-wealth capital. Major players include **CoreWeave, Lambda Labs, Crusoe, and Nebius**. --- #### Key Differences: Neoclouds vs. Hyperscalers ||**Neoclouds**|**Hyperscalers (AWS, Azure, GCP)**| |---|---|---| |**Focus**|GPU-first, AI-only|General-purpose compute| |**Pricing**|Lower, transparent|Higher, complex| |**Speed**|Deploy in days/weeks|Can take months| |**Scale**|Regional|Global| |**Software**|Open-source|Proprietary ecosystems| **1. Cost** A 2024 analysis by Uptime Institute found that renting an NVIDIA H100 GPU via a hyperscaler could cost around $98 per hour, while neoclouds offered the same at just $34 — a 66% saving for equivalent performance. **2. Architecture & Focus** Hyperscalers remain broad service platforms rooted in CPU-based general-purpose compute. Many in the industry describe hyperscalers as "CPU clouds," whereas Neoclouds design infrastructure around GPUs from the ground up. **3. Pricing Simplicity** Hyperscaler clouds typically offer dozens of products, with millions of individual line items for sale. In contrast, neoclouds provide only a handful of product lines, with variations in the tens. **4. Speed of Access** Hyperscaler lead times can be long — in some regions, it can take months to secure high-density AI compute from traditional providers. Neoclouds can move faster, often deploying in days or weeks. **5. Ecosystem Lock-in** Neoclouds rely heavily on open-source software, while hyperscalers often deploy proprietary managed services that make it harder for enterprises to switch. Enterprises are constrained within hyperscalers not only because of the ecosystem, but also because of egress fees, ingress fees, and other pricing factors. --- #### The Catch: Neocloud Limitations The bare-metal-as-a-service (BMaaS) model that many neoclouds have adopted is inherently commoditized — it has limited differentiation, high spending intensity, and price-driven competition. Additionally, most neoclouds have been operating for only three or four years, and their customer base is concentrated in AI R&D labs and model developers — not large enterprises, who remain dominated by hyperscalers due to full-stack ecosystems. --- #### Bottom Line Neoclouds aren't replacing hyperscalers — they're carving out a specialized lane. For enterprises, the priority should not be to choose one over the other, but to develop intelligent, use-case-driven multi-cloud strategies — using hyperscalers for general IT needs and neoclouds for specialized, compute-heavy AI workloads. ## Copilot [Microsoft Copilot: Your AI companion](https://copilot.microsoft.com/chats/VjCSJJEYTbPWaqswd4CZq) Neoclouds are **specialized AI‑focused cloud providers built around dense GPU clusters**, while hyperscalers are **general‑purpose global cloud platforms** (AWS, Azure, Google Cloud) offering broad services at massive scale. The distinction matters because AI workloads—especially training and large‑scale inference—stress compute, networking, and power in ways traditional clouds weren’t originally optimized for. --- ### What Neoclouds Are Neoclouds are **purpose‑built GPU cloud providers** optimized for AI/ML workloads. They emerged from crypto‑mining and HPC backgrounds and now focus on: - **Bare‑metal GPU clusters** with high‑bandwidth memory - **NVLink/NVSwitch** intranode fabrics - **InfiniBand or RDMA Ethernet** for cluster‑scale networking - **Dense, power‑optimized datacenters** designed for accelerators - **Lower pricing** by consolidating infrastructure and reducing overhead Examples include **CoreWeave, Crusoe, Lambda Labs, Nebius**. Neoclouds often claim **1/3 the cost of hyperscalers for GPU instances**, driven by specialization and efficiency [Data Center Knowledge](https://www.datacenterknowledge.com/ai-data-centers/neoclouds-vs-hyperscalers-will-ai-s-specialized-clouds-prevail-). They grew rapidly because AI demand exposed weaknesses in general‑purpose clouds, and GPU scarcity created an opening for providers who could deploy accelerators faster and cheaper [Data Center Knowledge](https://www.datacenterknowledge.com/ai-data-centers/neoclouds-vs-hyperscalers-will-ai-s-specialized-clouds-prevail-). --- ### What Hyperscalers Are Hyperscalers—AWS, Microsoft Azure, Google Cloud—are **broad, global cloud platforms** offering: - Compute, storage, networking, databases, analytics - Enterprise‑grade security, compliance, global regions - Managed AI services (SageMaker, Vertex AI, Azure AI) - Custom silicon (AWS Trainium/Inferentia, Google TPU) They dominate global cloud infrastructure but face pressure as AI workloads demand more specialized hardware and interconnects. --- ### Key Differences: Neoclouds vs. Hyperscalers #### 1. **Architecture & Performance** - **Neoclouds:** Purpose‑built for AI; dense GPU racks, optimized cooling, high‑performance fabrics. - **Hyperscalers:** General‑purpose; AI clusters exist but share datacenter design with many other workloads. Neoclouds often deliver **better price/performance for large model training** because they minimize abstraction layers and maximize GPU density. #### 2. **Cost Structure** - **Neoclouds:** Lower overhead, fewer services → **significantly cheaper GPU pricing** (as low as one‑third of hyperscaler rates) [Data Center Knowledge](https://www.datacenterknowledge.com/ai-data-centers/neoclouds-vs-hyperscalers-will-ai-s-specialized-clouds-prevail-). - **Hyperscalers:** Higher cost due to global infrastructure, enterprise features, and managed services. #### 3. **Scalability & Availability** - **Neoclouds:** Fast to deploy GPU clusters but constrained by power, supply chain, and specialized talent shortages [Data Center Knowledge](https://www.datacenterknowledge.com/ai-data-centers/neoclouds-vs-hyperscalers-will-ai-s-specialized-clouds-prevail-). - **Hyperscalers:** Massive global scale, but GPU availability can be limited due to multi‑tenant demand. #### 4. **Ecosystem & Services** - **Neoclouds:** Focus on raw compute; minimal managed services. - **Hyperscalers:** Full enterprise ecosystem—databases, IAM, observability, serverless, compliance. #### 5. **Business Models** - **Neoclouds:** - Creative financing (e.g., CoreWeave’s asset‑backed debt + customer prepayments) - Energy‑centric strategies (e.g., Crusoe colocating with stranded or renewable power) - Regional sovereignty plays (e.g., Nebius in Europe) These models reshape economics of GenAI infrastructure [Forbes](https://www.forbes.com/councils/forbestechcouncil/2025/12/11/beyond-hyperscalers-how-neoclouds-are-redefining-the-economics-of-genai/). - **Hyperscalers:** - Traditional cloud consumption model - Heavy investment in custom chips and global datacenters - Long‑term enterprise contracts #### 6. **Target Customers** - **Neoclouds:** AI startups, research labs, model‑training companies needing raw GPU power. - **Hyperscalers:** Enterprises needing integrated cloud services, compliance, and global reach. --- ### Why Neoclouds Exist Now AI has turned compute—especially GPUs—into a scarce commodity. Neoclouds emerged because: - Hyperscalers couldn’t meet GPU demand fast enough - AI workloads require specialized fabrics and power density - Startups needed cheaper, more flexible access to accelerators - Crypto‑era datacenter operators repurposed infrastructure for AI But as GPU supply stabilizes, neoclouds must differentiate beyond price and availability [Futuriom](https://www.futuriom.com/articles/news/could-neoclouds-become-commoditized/2025/04). --- ### When to Choose Each #### Choose **Neoclouds** if you need: - Large GPU clusters for training - Lower cost per GPU hour - Bare‑metal access and high‑performance fabrics - Rapid provisioning without enterprise overhead #### Choose **Hyperscalers** if you need: - Integrated cloud services (databases, storage, IAM) - Enterprise‑grade compliance and global regions - Managed AI platforms - Hybrid cloud or multi‑cloud architectures --- ### A Non‑Obvious Insight The next bottleneck in AI infrastructure may shift from **GPU supply** to **energy availability**. Crusoe’s nuclear‑powered AI datacenter partnerships highlight how **power sourcing** is becoming a competitive advantage as AI clusters scale to hundreds of megawatts [Forbes](https://www.forbes.com/councils/forbestechcouncil/2025/12/11/beyond-hyperscalers-how-neoclouds-are-redefining-the-economics-of-genai/). --- If you want, I can map this into a **decision framework** tailored to your use cases—training vs. inference, cost sensitivity, latency, or enterprise integration. ## Gemini [Google Gemini](https://gemini.google.com/app/b9552dfab6bf5476) ### **Neoclouds Overview** **Neoclouds** (often referred to as Specialized or Alternative Cloud Providers) are a generation of cloud platforms designed to provide high-performance infrastructure specifically optimized for **AI, machine learning (ML), and large-scale data processing**. Unlike traditional providers that offer a broad catalog of hundreds of services, Neoclouds focus on a "thick" layer of specialized hardware—primarily high-end **NVIDIA GPUs** (H100s, A100s, B200s)—and high-speed networking like InfiniBand. ### **Key Characteristics of Neoclouds** - **GPU Availability:** They often secure massive allocations of the latest AI chips, making them a go-to for startups that cannot get capacity from larger providers. - **Performance Optimization:** Their stacks are stripped of the overhead found in general-purpose clouds, focusing on bare-metal or near-bare-metal performance for model training. - **Cost Efficiency for AI:** By focusing on a narrow niche, they often provide better price-to-performance ratios for long-running training clusters. - **Notable Examples:** CoreWeave, Lambda Labs, Together AI, and Crusoe Energy. --- ### **Hyperscalers: The Big Three** **Hyperscalers** (AWS, Microsoft Azure, and Google Cloud) are massive, global cloud providers that offer an expansive ecosystem of integrated services. They focus on **scale, reliability, and "all-in-one" utility.** #### **Comparison Table: Hyperscalers vs. Neoclouds** |**Feature**|**Hyperscalers (AWS, Azure, GCP)**|**Neoclouds (CoreWeave, Lambda, etc.)**| |---|---|---| |**Primary Focus**|General purpose, enterprise IT, SaaS, and PaaS.|High-performance computing (HPC) and AI/ML.| |**Service Breadth**|Hundreds of services (Databases, Serverless, CRM, IoT).|Narrow focus on Compute (GPU/CPU) and Storage.| |**Hardware**|Custom silicon (Trainium, TPU) + NVIDIA.|Latest NVIDIA GPUs and InfiniBand networking.| |**Complexity**|High; complex IAM, VPC, and billing structures.|Low; streamlined for rapid deployment of clusters.| |**Target User**|Global enterprises and web developers.|AI researchers, model builders, and LLM startups.| --- ### **Differences Among the Hyperscalers** While all three hyperscalers offer similar core services (Compute, Storage, Networking), they differentiate based on their unique strengths and ecosystems: #### **1. Amazon Web Services (AWS)** - **Strength:** Market maturity and the largest catalog of services. - **Best For:** Mature DevOps environments and companies requiring highly specific niche services (e.g., satellite ground stations or specialized databases). - **Hardware:** Known for custom ARM-based processors like **Graviton** and AI-specific chips like **Trainium** and **Inferentia**. #### **2. Microsoft Azure** - **Strength:** Seamless integration with the Microsoft ecosystem (Active Directory, Office 365, Windows Server). - **Best For:** Enterprise organizations already deep in the Microsoft stack. - **AI Edge:** Azure has a dominant position due to its massive partnership and exclusive infrastructure support for **OpenAI**. #### **3. Google Cloud Platform (GCP)** - **Strength:** Data analytics, machine learning, and containerization (Google invented Kubernetes). - **Best For:** Data-heavy organizations and cloud-native startups. - **Hardware:** Pioneers of the **TPU (Tensor Processing Unit)**, which is specifically designed to accelerate Google's TensorFlow and other ML frameworks. --- Would you like me to compare the specific GPU pricing between a Neocloud like CoreWeave and a Hyperscaler for a specific AI project? ## Grok [Grok](https://grok.com/c/2d6a2cea-e266-409f-8e3d-1e5e5b8f0151?rid=b317d325-b33c-4cb1-a2bd-306ccad1f1c1) **Neoclouds** (sometimes stylized as "neo clouds" or "Neocloud") are a new category of specialized cloud providers that emerged primarily in 2024–2025, driven by the massive demand for AI training and inference workloads. They are **AI-first** (or GPU-first) cloud platforms that focus almost exclusively on providing high-performance computing resources — especially **GPU-as-a-Service (GPUaaS)** — using the latest Nvidia GPUs (like Hopper H100/H200 or Blackwell series) and optimized infrastructure for demanding AI, machine learning, and high-performance computing (HPC) tasks. Unlike traditional clouds, neoclouds prioritize raw compute power, low-latency interconnects, bare-metal access, flexible/short-term contracts, and faster provisioning to address GPU shortages and the specific needs of AI developers and startups. Popular examples include CoreWeave, Lambda Labs, Crusoe, Voltage Park, Nebius, and others (around 190 operators exist in the ecosystem as of late 2025). ### Key Differences Between Neoclouds and Hyperscalers Hyperscalers refer to the massive, general-purpose cloud giants: **AWS (Amazon Web Services)**, **Microsoft Azure**, and **Google Cloud Platform (GCP)**. They dominate traditional cloud computing but have expanded into AI/GPU offerings. Here's a comparison of the main differences: | Aspect | Hyperscalers (AWS, Azure, GCP) | Neoclouds (CoreWeave, Lambda, etc.) | |-------------------------|----------------------------------------------------------|----------------------------------------------------------| | **Primary Focus** | Broad, general-purpose cloud (storage, databases, VMs, containers, serverless, AI tools, enterprise IT) | Specialized in AI/HPC — mainly GPUaaS and accelerated compute | | **Hardware Priority** | Mix of CPU-first + GPUs/TPUs added later; broad instance types | GPU-first from the ground up; optimized for latest Nvidia accelerators, high-density racks, low-latency networking (e.g., InfiniBand) | | **Pricing for GPUs** | Higher (often 30–85% more expensive for equivalent GPU instances) | Typically much cheaper (up to 85% less), with more predictable/simple pricing | | **Contract Flexibility**| Long-term commitments common; enterprise-oriented | More flexible, shorter-term, pay-as-you-go; faster access & provisioning (days/weeks vs months) | | **Scale & Global Reach**| Massive global footprint, huge economies of scale | Smaller/regional focus; growing fast but not as ubiquitous | | **Target Customers** | Enterprises, legacy IT modernization, full-stack apps | AI-native startups, research labs, ML teams needing raw GPU power quickly | | **Service Breadth** | Thousands of services (full ecosystem: managed Kubernetes, databases, security, etc.) | Narrower — focused on compute, networking, storage for AI; less "full-stack" | | **Deployment Speed** | Slower for cutting-edge GPU capacity due to massive scale | Faster spin-up of new clusters; agile in addressing GPU shortages | | **Long-term Role** | Platform leaders; integrating AI deeply (e.g., Azure OpenAI, Bedrock) | Often complementary (many hyperscalers even rent from neoclouds); some aim to move "up the stack" into managed AI services | In short: Hyperscalers are like giant department stores offering everything for IT/AI, while neoclouds are specialized high-end GPU boutiques built specifically for the AI boom. Many experts see them as complementary — enterprises often use hyperscalers for general workloads and neoclouds for heavy AI training/inference where cost, speed, and raw performance matter most. The neocloud phenomenon reflects the explosive growth of AI infrastructure demand outpacing traditional cloud supply chains in the mid-2020s. If you're evaluating options for AI projects, the choice usually comes down to whether you need a full ecosystem (→ hyperscaler) or optimized, cost-effective GPU firepower (→ neocloud). # References # Links # Tags #network #cloud #hyperscaler #neocloud