Moving Your Gen AI Workloads to NeoClouds

If you’re running gen AI workloads, you’re likely familiar with the constraints of traditional hyperscalers: limited GPU availability, long quota approval cycles, long-term contracts required to access top-tier GPUs like H100s, and infrastructure designed for general compute rather than AI-specific needs. Neo clouds, GPU-specialized providers like Nebius, Crusoe, and CoreWeave, offer an alternative: direct access to newer GPU hardware, different pricing structures, and infrastructure built around high-performance compute workloads. The tradeoff is that you’re working with smaller providers that have different APIs, tooling, and operational models than AWS, GCP, or Azure. This post covers what you need to learn to evaluate and use neo clouds effectively. Most of your existing knowledge around containers, orchestration, and MLOps transfers directly. The differences are primarily in infrastructure provisioning, provider-specific APIs, and what managed services are (or aren’t) available. We’ll walk through the key concepts, a practical learning path, and the operational differences that matter.

What Are NeoClouds?

Neo clouds are infrastructure providers that specialize in GPU compute for AI and machine learning workloads. Unlike AWS, GCP, and Azure, which offer broad cloud services across compute, storage, networking, databases, and hundreds of other products, neo clouds focus primarily on providing high-performance GPU instances optimized for training and inference. The major players include CoreWeave, Crusoe, Lambda Labs, and Nebius, though the category continues to grow.

The key difference from hyperscalers is architectural focus. Neo clouds build their infrastructure around GPU availability and performance rather than offering GPUs as one option among many compute types. This means direct access to the latest NVIDIA hardware (H100s, H200s, GB200s) without multi-month waitlists or reserved instance contracts that hyperscalers typically require for top-tier GPUs. Many neo clouds also invest heavily in high-speed interconnects like NVLink and InfiniBand, which matter significantly for distributed training workloads.

From a practical standpoint, neo clouds offer three main advantages. First, GPU availability is substantially better. You can typically provision H100 instances on-demand rather than waiting in queue or committing to long-term contracts. Second, pricing is often more competitive, particularly for raw GPU compute hours. Hyperscalers charge premiums for their broader service ecosystems and global footprints, but if you primarily need GPU compute and can handle more infrastructure yourself, neo clouds can deliver better unit economics. Third, these providers optimize specifically for AI workloads, which means better default configurations for things like storage throughput, network bandwidth between nodes, and driver/CUDA stack management.

The tradeoffs are straightforward. You get less managed infrastructure, fewer integrated services (no equivalents to AWS Lambda, RDS, or dozens of other managed services), smaller global footprints, and less mature tooling ecosystems. You’re also working with younger companies that have less operational history, though many of the major neo clouds have achieved SOC2 Type II certifications and other compliance standards that matter for enterprise adoption.

The Cost Savings: Hyperscaler vs. NeoCloud H100 Pricing

The pricing difference between hyperscalers and neo clouds for H100 GPUs is substantial, particularly when you factor in the commitment requirements that AWS, GCP, and Azure typically impose for accessing top-tier GPUs.

Hyperscaler H100 Pricing (2025)

Here’s what you’ll pay for H100 instances on the major hyperscalers:

AWS P5 instances:

• On-demand 8-GPU instance: $55.04/hour (~$6.88 per GPU/hour)
• Requires GPU Capacity Reservations or UltraClusters for guaranteed access
• Savings plans available with long-term commitments

Google Cloud A3 instances:

• On-demand 8-GPU instance: $88.49/hour (~$11.06 per GPU/hour)
• Single GPU configurations: ~$3.00 per GPU/hour
• Preemptible pricing available at $2.25 per GPU/hour
• Newer H200 instances require capacity reservations

Azure ND H100 v5:

• On-demand 8-GPU instance: $98.32/hour (~$12.29 per GPU/hour)
• Single H100 configurations: $6.98 per GPU/hour (East US)
• May require pre-approval for H100 access
• Reserved instances offer up to 60% savings with 1-3 year commitments

NeoCloud H100 Pricing

Compare these hyperscaler prices to neo cloud on-demand rates:

• CoreWeave: $2.49 per GPU/hour on-demand
• Nebius: $2.95 per GPU/hour on-demand (up to 35% discounts for commitments)
• Lambda Labs: $1.85 per GPU/hour for single instances, $2.99 per GPU/hour for 8-GPU nodes

The Real Cost: Commitment Requirements

Beyond the sticker price difference, hyperscalers typically require significant commitments to access H100 capacity at all:

• AWS: Requires GPU Capacity Reservations or joining UltraClusters for guaranteed H100 access, meaning you often can’t get on-demand capacity without prior arrangements
• GCP: Capacity reservations required for newer GPU types, with on-demand access subject to availability
• Azure: Pre-approval often required for H100 access, with reserved instances (1-3 year contracts) being the primary path to consistent availability

Neo clouds, by contrast, typically provide immediate on-demand access to H100s without requiring sales calls, capacity reservations, or multi-year commitments. For a concrete example: an 8-GPU H100 instance costs $98.32/hour on Azure versus $19.92/hour on CoreWeave ($2.49 × 8 GPUs)—nearly 5x more expensive. Over a month of continuous usage (730 hours), that’s $71,774 on Azure versus $14,540 on CoreWeave, a difference of $57,234 per month per 8-GPU instance.

The combination of lower per-hour pricing and flexible on-demand access makes neo clouds compelling for teams that need H100s without locking into multi-year commitments or navigating quota approval processes. For production workloads requiring reserved capacity, neo clouds also offer commitment-based discounts, but you’re starting from a lower baseline price and maintaining flexibility in commitment terms.

What You Already Know (That Transfers Over)

The good news is that most of your existing infrastructure and ML knowledge applies directly to neo clouds. If you’re already running AI workloads on traditional cloud providers, the core operational concepts remain the same.

Container orchestration works identically. Neo clouds typically offer managed Kubernetes services or support for running your own clusters. Your existing k8s manifests, Helm charts, and deployment patterns transfer directly. If you’re using tools like ArgoCD, Flux, or other GitOps workflows, those continue to work without modification. The main difference is that you’ll provision GPU node pools through the neo cloud’s API rather than EKS, GKE, or AKS, but the actual workload orchestration remains unchanged.

GPU fundamentals stay the same. CUDA, cuDNN, NVIDIA drivers, and the entire GPU software stack work identically across providers. Your PyTorch or TensorFlow training scripts don’t need changes. Model optimization techniques like quantization, mixed precision training, and distributed data parallel patterns are all provider-agnostic. If you’ve debugged CUDA out-of-memory errors or optimized batch sizes for GPU utilization on AWS, that knowledge applies directly.

Model serving patterns transfer completely. Whether you’re using TorchServe, TensorFlow Serving, Triton Inference Server, or custom FastAPI services, these run the same way on neo clouds as they do on hyperscalers. Load balancing, autoscaling based on inference latency, and monitoring GPU utilization for serving workloads all use the same approaches. The serving infrastructure is just containers and GPUs, which are provider-independent.

Data management basics remain consistent. Most neo clouds provide both S3-compatible object storage and managed NFS services. The NFS offerings are often higher performance than hyperscaler equivalents like EFS or Azure Files, which matters for workloads that need high-throughput shared storage. If a specific neo cloud doesn’t have native storage services, you can continue using your existing hyperscaler object storage (cross-cloud egress costs permitting) or migrate to third-party providers like Cloudflare R2 or Backblaze B2. The patterns for loading training data, checkpointing models, and storing artifacts remain unchanged regardless of where storage is hosted. MLflow, Weights & Biases, and other experiment tracking tools integrate the same way they do with any cloud provider.

The core ML workflow (data prep, training, evaluation, deployment) doesn’t change. The Python libraries, training frameworks, and tooling you already use continue to work. You’re not rewriting code or learning new ML concepts. The learning curve is entirely on the infrastructure side, specifically how you provision resources and interact with the provider’s platform.

One important caveat: if you’re heavily invested in managed ML platforms like AWS SageMaker, GCP Vertex AI, or Azure ML Studio, migration will be more complex. These platforms bundle infrastructure, training orchestration, feature stores, model registries, and deployment automation into opinionated workflows. Neo clouds don’t have equivalents to these managed services. You’ll need to replicate that functionality using open-source tools (Kubeflow, MLflow, etc.), SaaS alternatives, or platforms like Saturn Cloud that can run on top of neo cloud infrastructure and provide similar managed ML capabilities.

What’s Different: Key Concepts to Learn

While the core ML and infrastructure concepts transfer over, there are specific areas where you’ll need to learn new tools, APIs, and operational patterns. These differences are concentrated in how you provision and manage infrastructure rather than how you run workloads.

Infrastructure & Provisioning

The most immediate difference is that you’re not using AWS CLI, gcloud, or Azure CLI anymore. Each neo cloud has its own API, CLI tool, and web console. CoreWeave has the cw CLI, Lambda Labs has its own API and dashboard, and so on. The concepts are familiar (create instance, attach storage, configure networking), but the specific commands and flags are different. You’ll spend time reading provider-specific documentation and translating your existing automation scripts to new APIs.

Terraform and infrastructure-as-code support varies significantly by provider. Some neo clouds have official Terraform providers with good coverage of their resources. Others have community-maintained providers or no Terraform support at all, which means you might need to use their native APIs directly or wrap them in your own tooling. Check Terraform provider maturity before committing to a provider if IaC is central to your workflows.

GPU instance types use different naming conventions. Instead of AWS’s p4d.24xlarge or GCP’s a2-highgpu-8g, you’ll see provider-specific names that might be more or less descriptive. Some providers name instances by GPU count and type (like 8xH100-80GB), others use their own SKU naming. You’ll need to learn what’s available and how instances map to your workload requirements. Most providers have fewer instance type options than hyperscalers, which simplifies choices but may require adjusting your resource requests.

Network architecture differs across providers. Some neo clouds give you full VPC equivalents with subnets, security groups, and private networking that feels similar to AWS. Others have simpler networking models where instances get public IPs by default and you configure firewall rules at the instance level. Understanding how to set up secure private networks, expose services externally, and connect multiple instances or clusters requires learning each provider’s specific networking model.

GPU Hardware Specifics

Neo clouds typically provide access to the latest GPU hardware significantly faster than hyperscalers. When NVIDIA releases new architectures like H200 or GB200, neo clouds often have them available within months, while AWS, GCP, and Azure may take a year or longer to reach general availability. For workloads that benefit from increased memory capacity, higher bandwidth, or architectural improvements, this gap matters. You also tend to get more consistent hardware within clusters rather than mixed GPU generations that complicate performance tuning.

High-speed interconnects become a more prominent consideration. NVLink handles GPU-to-GPU communication within a single node, while InfiniBand provides low-latency node-to-node communication for multi-node training. Hyperscalers offer these on specific instance types, but neo clouds often make them standard for GPU instances. If you’re running distributed training across multiple nodes, understanding whether your workload is communication-bound or compute-bound helps you evaluate whether you need InfiniBand. For large model training where gradients need frequent synchronization across nodes, InfiniBand’s lower latency compared to standard Ethernet can significantly reduce training time.

You’ll also need to map hardware specifications to your workload requirements more directly. When choosing between instance types, you’re evaluating specs like FLOPS (floating point operations per second), memory bandwidth, and GPU memory capacity. For example, if you’re training large language models, you need to know whether 80GB per GPU is sufficient for your model size and batch size, or whether you need to use model parallelism techniques. Memory bandwidth (measured in TB/s) determines how fast data moves between GPU memory and compute cores, which affects training throughput for memory-bound operations. FLOPS indicates raw compute capacity, which matters for compute-bound workloads like dense matrix multiplications in transformer models. Understanding these specs helps you choose cost-effective hardware rather than over-provisioning.

Storage & Data Transfer

As mentioned earlier, most neo clouds provide S3-compatible object storage and managed NFS. The operational difference is in the details. S3-compatible doesn’t always mean perfectly compatible. Most common operations work fine (GET, PUT, LIST), but edge cases in multipart uploads, versioning, or lifecycle policies may behave differently. Test your specific storage workflows during evaluation rather than assuming full compatibility.

Data transfer costs are typically more favorable than hyperscalers. Egress from AWS, GCP, or Azure can be expensive (often $0.08-0.12 per GB), while neo clouds often have lower or zero egress fees. This matters significantly if you’re moving large datasets or serving models that return substantial response payloads. However, ingress to neo clouds from your current infrastructure may incur costs on the hyperscaler side, so factor that into migration planning.

For initial data migration, most neo clouds support standard transfer methods: direct upload to object storage, data transfer appliances for large datasets, or network-based transfers. If you’re moving hundreds of terabytes, check whether the provider offers bulk transfer services or partnerships with data migration providers. Some teams keep training data on hyperscaler storage initially and incrementally migrate while running compute on neo clouds, accepting cross-cloud transfer costs temporarily to derisk migration.

Networking & Security

Network security models vary considerably. Some providers give you full software-defined networking with security groups, network ACLs, and private subnets similar to AWS VPC. Others use simpler firewall rules at the instance or account level. Understanding the security model is critical for compliance and risk management. You’ll need to map your existing security requirements (like “no GPU instances should be directly exposed to the internet” or “all inter-service communication must be on private networks”) to the provider’s capabilities.

VPN and private connectivity options differ across providers. If you need to connect neo cloud resources to your existing infrastructure (on-prem data centers, other clouds), check what’s available. Some providers support site-to-site VPN, some offer dedicated interconnects, and some require you to build connectivity through standard VPN instances. This affects network latency, bandwidth, and operational complexity for hybrid deployments.

Identity and access management is another area where you’ll encounter provider-specific implementations. Instead of AWS IAM, you’re working with the neo cloud’s user management, API keys, and role-based access control. The concepts are familiar (users, groups, permissions), but the specific mechanisms and integration with your existing identity providers (Okta, Azure AD, etc.) vary. Some providers have good SSO and SAML support, others require managing users manually.

Managed Services (Or Lack Thereof)

The most significant operational difference is reduced managed service availability. Hyperscalers offer dozens of managed services (databases, message queues, caching, serverless compute, etc.) that reduce operational burden. Neo clouds focus on compute and storage, leaving you to run these services yourself on instances or use external SaaS providers.

For databases, you’ll typically run your own PostgreSQL, MySQL, or Redis instances on VMs, use managed database services from the hyperscalers (connecting across networks), or adopt third-party managed database providers. This adds operational overhead but also gives you more control over configuration and performance tuning. The same applies to message queues, caching layers, and other infrastructure components you might have used as managed services previously.

Monitoring and logging are less integrated. Instead of CloudWatch or Cloud Logging being automatically available, you’ll typically deploy your own observability stack (Prometheus, Grafana, Loki, or commercial alternatives like Datadog or New Relic). This requires more upfront setup but often provides better visibility and control once configured. Most neo clouds provide basic instance metrics and logs, but production-grade observability needs additional tooling.

Load balancing and ingress management work differently. Some providers offer managed load balancers, others expect you to run your own (NGINX, HAProxy, or cloud-native options like Envoy). For Kubernetes workloads, you’ll typically use standard ingress controllers, but understanding how they integrate with the provider’s networking model requires provider-specific knowledge.

Common Gotchas & How to Avoid Them

GPU availability isn’t unlimited. While neo clouds generally have better GPU availability than hyperscalers, they can still run out of capacity, especially for the latest hardware. Reserved capacity or committed use contracts may be necessary for production workloads that need guaranteed availability. Have conversations with sales or support early about capacity planning for your needs.

Spot/preemptible instances may not be available. Many neo clouds don’t offer spot or preemptible instances at all because GPU demand is consistently high and capacity is better utilized at standard pricing. If you’re currently relying on spot instances for cost savings on hyperscalers, that strategy may not translate to neo clouds. For providers that do offer discounted or interruptible capacity, understand their specific policies on interruption notice periods, pricing volatility, and availability.

Cross-region data transfer may not exist. Many neo clouds operate in fewer regions than hyperscalers, and cross-region networking may be limited or require different approaches than AWS cross-region VPC peering. If you need geographic distribution, verify the provider supports your required regions and connectivity patterns.

Support models vary significantly. Hyperscalers offer tiered support with SLAs and 24/7 availability (at a cost). Neo clouds may have less formal support structures, community-based support, or require higher spending levels to access dedicated support teams. Understand the support model before depending on it for production incidents.

Billing and cost management tools are less mature. Instead of detailed cost allocation tags, budget alerts, and cost explorer tools, you may get simpler billing dashboards. Build your own cost tracking and alerting if detailed cost management is important. Some providers have APIs that let you build custom cost monitoring.

Comparing Major NeoCloud Providers

To help you evaluate specific providers, here’s how some of the major neo clouds compare across the key dimensions discussed above.

Nebius

Nebius illustrates how neo clouds can bridge the gap between hyperscaler functionality and GPU-focused infrastructure. On pricing, Nebius offers H100 instances at $2.95/hr on-demand with discounts up to 35% for longer commitments, competitive with other neo clouds. For on-demand capacity, they provide immediate access to 16-32 H100 GPUs without sales calls or quota approvals, addressing one of the key hyperscaler pain points. On managed services, Nebius goes further than most neo clouds with managed Kubernetes (including GPU node pools), PostgreSQL, MLflow, and Slurm workload managers, which reduces the operational burden compared to providers that only offer raw compute. Their Terraform provider is official and well-documented, making infrastructure-as-code straightforward. API access is flexible with support for CLI, REST APIs, SDKs, and Terraform. For networking, Nebius provides full VPC functionality including security groups, network isolation, and site-to-site VPN support, similar to AWS VPC. They also offer both S3-compatible object storage and NFS file storage, with InfiniBand networking standard for GPU instances. This combination makes Nebius a useful reference point when evaluating neo clouds, as they demonstrate that it’s possible to maintain strong GPU availability and pricing while offering more managed services than the typical neo cloud provider.

CoreWeave

CoreWeave positions itself as a Kubernetes-native GPU cloud with strong infrastructure automation. Pricing for H100 instances runs around $2.49/hr on-demand, with spot pricing available for interruptible workloads and reserved pricing for longer commitments (1 month to 3 years). On-demand GPU availability is generally good, though like all neo clouds, capacity for the latest hardware can fluctuate. CoreWeave’s Kubernetes Service (CKS) is their primary managed offering, providing GPU-enabled Kubernetes clusters as the core orchestration layer. This Kubernetes-first approach means if you’re already running workloads on k8s, CoreWeave integration is relatively straightforward. However, they offer fewer managed services beyond Kubernetes compared to traditional clouds. Terraform support exists with official providers, and they offer REST API, Terraform, and Ansible for automation. VPC networking includes private networking, security groups, and NAT gateways, with public IPs billed separately at $4/month. For storage, CoreWeave recently launched AI Object Storage (CAIOS), an S3-compatible service delivering up to 7 GB/s per GPU with zero egress fees, which is significantly faster than traditional object storage. They also provide NFS file storage with petabyte-scale capacity and up to 1 GB/s per GPU. The combination of Kubernetes-native infrastructure, high-performance storage, and zero egress costs makes CoreWeave appealing for teams already invested in Kubernetes and moving large amounts of data.

Crusoe

Crusoe differentiates itself with a focus on energy efficiency (using stranded energy sources) while providing standard neo cloud capabilities. H100 pricing is competitive with other neo clouds on both on-demand and reserved instances, with pricing tiers for short and long-term commitments. GPU availability has been strong, with Crusoe reporting 99.98% cluster uptime for H100 instances. For managed services, Crusoe offers managed Kubernetes and recently introduced “Auto Clusters,” a fully managed Slurm offering unveiled at GTC, alongside a managed AI inference service for API-driven model access. This puts them in the middle ground between minimal and comprehensive managed services. Crusoe provides official Terraform support, allowing infrastructure-as-code workflows for provisioning GPU clusters. API access is available via API, CLI, UI, or Terraform. VPC networking includes software-defined networking with VPC networks, subnets, and NAT gateways, and they support customer-defined IP ranges in RFC 1918 space. They’ve also introduced a Capacity API that lets users check current on-demand availability before provisioning. Storage includes persistent block storage (backed by Lightbits) and storage-optimized instances for running your own file systems like Lustre or object stores like MinIO. The focus on energy efficiency, strong uptime metrics, and managed Slurm make Crusoe particularly relevant for HPC-style workloads transitioning to cloud GPU infrastructure.

Lambda Labs

Lambda Labs targets simplicity and ease of use, particularly for researchers and smaller teams getting started with GPU workloads. H100 pricing starts at $1.85/hr per GPU for single instances or $2.99/hr for 8-GPU nodes, among the more competitive options in the neo cloud space. On-demand availability can be inconsistent due to high demand and competitive pricing, with GPU availability varying significantly by region and instance type. Lambda offers production clusters from 16 to 2,000+ H100 or B200 GPUs via their “1-Click Clusters” feature, though these typically require contacting sales for larger deployments. Managed services are minimal. Lambda focuses on providing GPU instances with persistent storage rather than managed Kubernetes, databases, or other platform services. They provide a Cloud API for automation but Terraform support is not officially documented or widely available, meaning infrastructure-as-code workflows require using their API directly. Networking is basic with firewall rules for TCP and UDP (port 22 open by default), but no full VPC functionality or advanced networking features. Storage recently expanded to include persistent network storage at $0.20/GB/month with no ingress or egress charges, though filesystems are regional and cannot be attached across regions. Lambda’s approach trades advanced features for simplicity. If you need to quickly spin up GPU instances for training without complex infrastructure requirements, Lambda’s straightforward interface and competitive pricing work well. For production deployments requiring VPC isolation, Terraform automation, or managed services, other providers offer more complete platforms.

Vultr

Vultr represents a more traditional cloud provider expanding into GPU offerings, bringing broader infrastructure services to the neo cloud category. H100 pricing starts around $2.30/hr per GPU for on-demand usage, with lower rates available on 36-month prepaid commitments. GPU availability includes H100, H200, A100, A40, L40S, and AMD MI300X options, available as VMs or bare metal with fractional, full, or multiple GPU configurations. Vultr differentiates itself with a broader set of managed services than typical neo clouds: managed Kubernetes (with free control plane), managed databases (MySQL, PostgreSQL, Valkey, Kafka) with automatic backups and 99.99% SLA, and serverless inference services for GPU workloads. This makes Vultr feel more like a traditional cloud provider with GPU capabilities than a GPU-specialized neo cloud. Terraform support is official and well-documented, with API v2 and CLI also available for automation. VPC networking includes private networking capabilities and the ability to connect to other cloud providers or on-premises infrastructure. Storage options are comprehensive: S3-compatible object storage with 99.999999999% durability, block storage using NVMe SSDs, and a file system supporting simultaneous access from multiple instances or Kubernetes containers. Vultr operates across 32 cities in 19 countries, providing broader geographic distribution than most neo clouds. The tradeoff is that Vultr’s GPU-specific optimizations (like InfiniBand networking) may not be as mature as pure-play GPU providers, but for teams wanting GPU compute within a more traditional cloud environment with managed services and global presence, Vultr offers a different value proposition than specialized neo clouds.

Conclusion

Moving gen AI workloads to neo clouds requires learning new infrastructure APIs and operational patterns, but the core ML work remains unchanged. Most of your existing knowledge around containers, orchestration, GPU optimization, and model serving transfers directly. The learning curve focuses on provider-specific infrastructure provisioning, understanding which managed services you’ll need to replace or run yourself, and adapting to different networking and security models.

The value proposition is straightforward: better GPU availability, access to newer hardware, and often better economics for GPU-heavy workloads. The tradeoff is taking on more infrastructure responsibility and working with younger platforms. For teams running significant AI workloads where GPU access is a constraint, neo clouds offer a practical alternative worth evaluating. Start with a small pilot project, understand the provider’s specific capabilities and limitations, and scale adoption based on what works for your team’s needs and operational capabilities.

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