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There is no defensible universal winner. Nebius is the clearest fit when you want self-service access, named Kubernetes or Slurm options, and publicly posted dynamic preemptible pricing. CoreWeave is strongest for buyers who need a detailed regional price table covering on-demand, spot, storage, and networking. Lambda documents a broad menu of GPU-backed virtual machines, but the reviewed official page does not publish a comparable current price table.
Choose by the exact GPU and memory configuration, available capacity in your required region, host and network design, interruption tolerance, orchestration needs, and the complete bill—not by the GPU name alone. The prices below are provider-published snapshots accessed October 25, 2026, not an independent benchmark or a promise of account-level availability.
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Nebius, CoreWeave, and Lambda expose different instance shapes, billing units, regions, and service layers. A price attached to one provider’s eight-GPU host is not directly comparable with another provider’s per-GPU-hour number. Likewise, the presence of the same NVIDIA model does not prove equivalent CPU, RAM, local storage, interconnect, software image, or support.
No matched independent benchmark establishes a performance winner among these three clouds. Nebius discusses its own validation and participation in MLPerf, but a vendor’s participation or performance claim is not a three-way workload result. Treat the comparison as a procurement and architecture guide, then validate the finalists with your own model, data, framework, and serving pattern.
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At-a-glance comparison
| Decision axis | Nebius | CoreWeave | Lambda |
|---|---|---|---|
| GPU options documented in the reviewed sources | Up to 32 NVIDIA GPUs per self-service cluster page, including HGX B300, B200, H200, H100, RTX PRO 6000, and L40S. | GB300/GB200, HGX B300/B200, H200/H100, GH200, RTX PRO 6000, L40/L40S, and A100 instance families are listed. | B200, GH200, H100, A100, A10, A6000, V100, and RTX 6000 configurations are listed. |
| Published price evidence | On-demand per-GPU-hour rates and dynamic preemptible rates; some newest configurations require contacting sales. | Regional on-demand and spot instance-hour rates; some configurations require contacting sales. Certain inference prices are limited to customers of its inference platform. | The reviewed on-demand page describes configurations but is not a comparable current price table. |
| Geography | Self-service materials describe data centers in Europe and the United States. A company announcement describes a European footprint in Finland, France, the United Kingdom, Spain, Estonia, and Iceland; verify GPU-specific availability. | Pricing is separated into North America and Europe. Confirm live capacity and account eligibility. | Each instance is tied to a region, but the reviewed page does not enumerate the region list. |
| Scale and orchestration | Single VMs through multi-node clusters, with managed Kubernetes and Slurm options described. | The reviewed pricing page gives detailed instance specifications and cloud cost categories but does not establish a complete orchestration comparison. | Linux GPU-backed VMs and many instance shapes are documented; the reviewed material does not establish a full orchestration comparison. |
| Costs beyond GPU time | Storage tiers, network traffic, and public-IP charges are listed on the pricing page. | Storage and networking charges are listed; the page states internet transfer is free and lists public-IP and Direct Connect pricing. Confirm current terms. | Storage, transfer, and networking charges were not established by the reviewed page; check them separately. |
Compare the instance, not just the GPU
GPU count and memory
Start with the amount of accelerator memory your model needs, then decide whether one GPU, a multi-GPU host, or a multi-node cluster is required. Lambda’s December 2025 table, for example, lists B200 shapes from one to eight GPUs, with 180 GB of VRAM per GPU. That is materially different from choosing an eight-GPU host merely because the model name sounds similar.
Record both total memory and memory per GPU. Tensor and pipeline parallelism may require a particular number of GPUs and a fast interconnect; a collection of individually available GPUs is not automatically equivalent to one tightly coupled system. Ask each provider whether the advertised shape is a single host, how GPUs are connected, and what topology your job receives.
Host CPU, RAM, disk, and network
GPU utilization can be limited by the host. Capture vCPU count, system RAM, local or attached storage, storage throughput, boot-disk limits, and the network bandwidth between workers and storage. For training, include checkpoint-write throughput and inter-node latency. For inference, include model-loading time, concurrent-request behavior, and the path from the serving process to your data.
Make a one-line specification for every candidate: GPU model and count, memory per GPU, host CPU and RAM, local disk, interconnect, region, operating system or image, and hourly unit. Reject a cheaper-looking option if it cannot hold the model, feed the GPUs, or synchronize workers at the required rate.
Capacity is a technical requirement
A published price does not guarantee that a particular account can obtain the requested GPU, region, quantity, or duration. Capacity may vary by time, demand, reservation status, and customer eligibility. Check availability immediately before launch and again before committing a long training run. For a large cluster, ask for a capacity confirmation that names the exact shape, region, start window, and expected run duration.
Nebius: self-service access and explicit orchestration choices
Nebius’s self-service GPU clusters page describes access to clusters of up to 32 NVIDIA GPUs. The listed families include HGX B300, B200, H200, H100, RTX PRO 6000, and L40S. Its compute overview describes deployments ranging from single virtual machines to multi-node clusters and names managed Kubernetes and Slurm options.
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The self-service positioning is useful for teams that want to start without a lengthy capacity negotiation, but “up to 32 GPUs” is not a guarantee that every model or topology is available in every location. Confirm the exact GPU, host shape, interconnect, and region in the control plane or with sales before designing around it.
Nebius published rates
The Nebius pricing page presents on-demand prices in GPU-hours and a separate preemptible or spot column. The page was accessed October 25, 2026; one rate column is marked effective October 1, 2026.
| Configuration | On-demand price shown | Other published figure | Qualification |
|---|---|---|---|
| NVIDIA HGX H100 | $3.85 per GPU-hour | $4.50 per GPU-hour | The second figure is in the column marked effective October 1, 2026. |
| NVIDIA HGX H200 | $4.50 per GPU-hour | $5.40 per GPU-hour | The second figure is in the column marked effective October 1, 2026. |
| NVIDIA HGX B200 | $7.15 per GPU-hour | $8.50 per GPU-hour | The second figure is in the column marked effective October 1, 2026. |
| HGX H100 and H200 preemptible | From $0.79 per GPU-hour | Dynamic | The page says spot prices can change with availability, utilization, demand, and customer pricing preferences. |
These are not guaranteed long-run rates. Confirm billing increments, taxes, minimums, commitments, and the account-specific quote. A per-GPU-hour number also omits the host, storage, transfer, public IP, and any managed-service charges that appear elsewhere on the bill.
In an October 21, 2026 company announcement, Nebius founder and CEO Arkady Volozh said, “Companies should be able to see what compute costs and access it on terms that fit their work.” That is a vendor statement about its pricing approach, not independent evidence of lower cost or better performance. The announcement is available at Nebius’s newsroom.
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CoreWeave’s pricing page lists many NVIDIA instance families, including GB300 and GB200, HGX B300 and B200, H200 and H100, GH200, RTX PRO 6000, L40/L40S, and A100. Its table separates North America from Europe and distinguishes on-demand from spot pricing.
Representative CoreWeave figures
| Instance | Region | On-demand | Spot | Unit |
|---|---|---|---|---|
| Eight-GPU HGX H100 | North America | $49.24 per hour | $19.71 per hour | Instance-hour, not GPU-hour |
| Eight-GPU HGX H100 | Europe | Not stated in the cited figure | $19.51 per hour | Instance-hour |
| Eight-GPU HGX B200 | North America | $68.80 per hour | $34.11 per hour | Instance-hour |
| Eight-GPU HGX B200 | Europe | Not stated in the cited figure | $34.87 per hour | Instance-hour |
Do not divide or multiply these figures to declare a winner. CoreWeave’s numbers cover a complete multi-GPU instance, while Nebius’s cited numbers are per GPU-hour; the underlying CPU, RAM, storage, topology, and commercial terms may differ. Normalize only after you have matched the full specification.
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Storage, transfer, and inference caveats
The same pricing page lists storage and networking charges. It states that internet transfer is free and lists public-IP and Direct Connect pricing, but current terms should be confirmed when you order. Some single-GPU prices are identified as applying only to customers of CoreWeave’s inference platform. Do not generalize those rates to every CoreWeave customer or to every deployment type.
Lambda: broad documented configurations, incomplete price comparability
Lambda’s on-demand cloud documentation lists Linux GPU-backed virtual machines using B200, GH200, H100, A100, A10, A6000, V100, and RTX 6000 configurations. The table is marked “As of December 2025,” so verify current inventory and pricing before using it for a 2026 purchase decision.
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Lambda can therefore be a good configuration fit when its documented shape matches your memory and framework requirements, but the absence of a comparable published price in the reviewed material means you must do the commercial comparison directly.
How to normalize prices without fooling yourself
Use a total-cost model for one complete job or serving period:
Total cost = accelerator time + host or instance charges + attached and snapshot storage + data transfer + public IP or private connectivity + orchestration and support charges + taxes
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- Define the workload. Write down model size, precision, sequence length, dataset size, expected tokens or samples, target throughput, checkpoint interval, and maximum acceptable interruption.
- Specify the shape. Name the GPU model and count, memory per GPU, host CPU and RAM, local or attached storage, interconnect, operating system, and region.
- Match the billing unit. Convert per-GPU-hour and instance-hour quotes to the same complete shape only after confirming that the host and topology are equivalent. Label any arithmetic as an estimate, not a provider guarantee.
- Add non-GPU costs. Include the storage holding the dataset and checkpoints, egress or transfer, public addresses, private links, orchestration, and support.
- Price interruption risk. For spot or preemptible capacity, estimate checkpoint storage, restart time, wasted work, and the probability that capacity is unavailable when you need to resume.
- Run a short acceptance test. Measure tokens per second, time to first response, utilization, checkpoint time, data-loading throughput, and failure recovery on the exact shape and region.
For example, multiplying Nebius’s $3.85 per-GPU-hour H100 figure by eight produces a GPU-only arithmetic estimate of $30.80 per hour. It does not prove equivalence with CoreWeave’s $49.24 eight-GPU H100 instance-hour figure because the surrounding host and service bundle may differ. Keep that distinction in every spreadsheet and purchase approval.
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Small experiments and prototyping
First check which provider can provision the exact single- or few-GPU shape in your preferred region. Nebius explicitly describes self-service access, which can reduce the time between account creation and a test, but live capacity still needs confirmation. Lambda’s documented menu may be attractive when a specific legacy GPU or B200 memory shape is required. CoreWeave’s regional table helps you estimate the starting cost before requesting capacity.
Large distributed training
Choose the provider that can commit the required cluster shape and network topology for the entire run. Ask for a written capacity confirmation covering GPU count, host grouping, interconnect, region, start date, and expected duration. Compare the cost of the complete cluster, not a per-GPU headline. Nebius’s documented multi-node, Kubernetes, and Slurm options may reduce operational work for teams already using those schedulers; the reviewed material does not establish equivalent managed offerings from the other two providers.
Checkpointed training and batch inference
Interruption-tolerant jobs can consider spot or preemptible capacity. Nebius’s page shows H100 and H200 preemptible pricing from $0.79 per GPU-hour, with rates that change according to availability, utilization, demand, and customer pricing preferences. CoreWeave publishes regional spot rates, including $19.71 per hour for an eight-GPU H100 instance in North America and $19.51 in Europe. Those figures are snapshots, not guarantees.
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Before choosing spot, verify interruption notices, eviction behavior, restart automation, checkpoint durability, and whether the same GPU family is likely to be available when you resume. A job that loses many hours of work can cost more than on-demand capacity.
Production inference
Compare end-to-end serving cost and reliability: sustained capacity, autoscaling behavior, model-loading time, request latency, concurrency, storage reads, network paths, observability, and support. CoreWeave’s page distinguishes some inference-platform prices from general cloud prices, so check that your account and serving architecture qualify. Do not use a low single-GPU inference figure as a general rate for all workloads.
Teams choosing by workflow
If your priority is managed orchestration, Nebius is the only provider in the reviewed material that explicitly names both Kubernetes and Slurm options. That does not by itself prove better usability; evaluate identity, image management, quotas, autoscaling, logs, secrets, node replacement, and upgrade policy in a trial. The reviewed Lambda and CoreWeave pages document compute configurations and prices but do not support a complete feature-by-feature orchestration ranking.
Region, compliance, and data movement checks
- Region: Confirm the exact data-center location for the GPU, attached storage, backups, and control-plane services. Nebius describes Europe and the United States, with a European footprint announcement covering Finland, France, the United Kingdom, Spain, Estonia, and Iceland; availability varies by service and GPU. CoreWeave separates North American and European pricing. Lambda’s reviewed page says instances are region-bound without listing the regions.
- Data residency: Match the provider region and backup path to your contractual, regulatory, and customer requirements. Do not infer compliance from geography alone.
- Transfer: Map every movement: object storage to training nodes, checkpoint uploads, inter-node traffic, logs, and production responses. A free-internet-transfer statement does not eliminate private-link, storage, or other network charges.
- Support and service levels: Verify the actual support plan, incident process, maintenance policy, and service-level agreement in your contract. Marketing descriptions are not an SLA.
- Framework compatibility: Test the CUDA, driver, container runtime, PyTorch or TensorFlow version, distributed-training library, and monitoring agents you will use. A listed GPU does not guarantee that your preferred image is available unchanged.
Common failure modes and how to recover
The GPU is listed but cannot be provisioned
Check region, account eligibility, quota, and current capacity. Reduce the requested cluster size only if your parallelism plan allows it; otherwise request a capacity quote or choose a documented alternative with enough memory. Never redesign around a published model name without confirming inventory.
The bill is higher than the GPU estimate
Inspect instance-hour versus GPU-hour units, attached disks, snapshots, object storage, public addresses, private connectivity, data transfer, taxes, and idle VMs. Set budget alerts and automatic shutdown for test environments. Separate persistent storage from ephemeral training nodes so failed experiments do not leave expensive accelerators running.
Spot capacity interrupts the job
Increase checkpoint frequency, write checkpoints to durable storage, record the exact software environment, and automate restoration. Keep an on-demand fallback for deadlines. Recalculate the expected cost using restart time and lost work rather than the spot rate alone.
Distributed training scales poorly
Measure data-loader wait time, GPU utilization, inter-node bandwidth, all-reduce time, CPU saturation, and storage throughput. Confirm that the allocated GPUs share the topology your framework expects. A smaller tightly connected host can outperform a nominally cheaper collection of remote GPUs for synchronization-heavy models.
Inference latency is unstable
Check cold starts, model reloads, queueing, batch size, CPU preprocessing, network hops, and noisy-neighbor or autoscaling behavior. Test the production request mix, not only a synthetic single-request benchmark.
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A practical buying checklist
- Write the workload and success metric: training completion time, tokens per dollar, throughput, latency, or availability.
- List the minimum GPU memory, GPU count, host RAM, local disk, network, and region.
- Request current quotes from each viable provider using the same specification and duration.
- Ask whether the quote is on-demand, spot, preemptible, reserved, or inference-platform-only, and whether taxes, support, storage, and transfer are included.
- Confirm capacity, quota, billing increment, minimum runtime, interruption notice, and cancellation terms.
- Run an acceptance test with your container, model, dataset slice, checkpoint routine, and monitoring.
- Calculate total cost for the full pipeline and document the fallback provider or region.
Bottom line
Pick Nebius when self-service access, explicitly documented Kubernetes or Slurm workflows, and visible dynamic preemptible pricing are central to your decision. Pick CoreWeave when its regional instance catalog, spot/on-demand table, and documented storage and network costs make procurement easier for your team. Consider Lambda when its documented GPU and memory shapes fit your workload, but obtain a current quote because the reviewed page does not provide comparable prices.
For any of the three, the final choice should follow a capacity-confirmed, like-for-like trial in the required region. GPU price alone cannot establish the best cloud for an AI workload.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
