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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThere is no evidence-backed universal cheapest cloud for AI GPU workloads. Lambda publishes per-GPU-hour rates and advertises minute-level billing with no egress fees; AWS publishes regional Capacity Blocks rates for specific GPU instances; Google Cloud adds GPU charges to VM machine costs; and Azure directs customers to its pricing calculator. The meaningful comparison is a dated estimate for the same GPU, region, runtime, networking, storage, and purchase terms—not a ranking of headline hourly prices.
What the published prices do—and do not—compare
The figures below come from provider pricing pages accessed in 2026. They are vendor-listed prices, not independent measurements of training performance or total workload cost. The providers do not publish one aligned four-way price table for the same hardware, region, and purchase model.
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| Provider | Published price evidence | What the figure covers or leaves open |
|---|---|---|
| Lambda Cloud | For a one-GPU configuration, Lambda lists B200 SXM6 at $6.99/GPU-hour, H100 SXM at $4.29/GPU-hour, H100 PCIe at $3.29/GPU-hour, A100 SXM 40 GB at $1.99/GPU-hour, and GH200 at $2.29/GPU-hour. | These are listed rates before applicable taxes. They are not matched to equivalent VM configurations at other providers. Lambda lists different per-GPU rates for larger configurations, so the one-GPU figures should not be extrapolated to multi-GPU instances. |
| AWS EC2 | Capacity Blocks for ML lists P5.48xlarge with eight H100 GPUs at $41.528/hour in several US regions, or $5.191 per accelerator. It lists P5e.48xlarge with eight H200 GPUs at $47.76/hour in several regions, or $5.97 per accelerator. | These are Capacity Blocks rates, not general On-Demand prices. The rates apply to the named instances and purchase model; they are not directly comparable with a per-GPU offer without aligning region, capacity, and included resources. |
| Microsoft Azure | Comparable H100/H200 VM price: not stated (Azure Linux Virtual Machines pricing page). | Azure directs customers to its pricing calculator. A quote requires a named GPU VM SKU, region, usage, and purchase plan; storage and network costs also matter. |
| Google Cloud | Comparable all-in H100 VM price: not stated (Google Cloud GPU pricing page). | Google Cloud prices GPUs by region and charges for them in addition to the VM machine type. Use its calculator to combine the GPU and VM costs. |
Even where a rate is published, GPU-hours are only one part of the bill. A workload estimate may also include CPU and memory, persistent or local disks, images, data transfer, and time spent starting, checkpointing, or sitting idle. A cheaper accelerator-hour can still produce a more expensive run if the workload needs more wall-clock time, extra infrastructure, or costly data movement.
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How each provider fits an AI workload
Lambda Cloud: direct GPU-hour pricing
Lambda describes self-serve HGX B200, H100, A100, and GH200 instances in 1-, 2-, 4-, and 8-GPU configurations. Its pricing page says billing is by the minute and advertises no egress fees. The official documentation describes Linux GPU-backed on-demand VMs, lists instance types as of December 2025, and associates instances with geographic regions. Check the live console for the required GPU configuration and region: Lambda describes self-serve access as first-come, and availability should not be assumed from a price listing alone.
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- Graphics Card Interface: Pci E
The published one-GPU rates above are useful for initial screening, not a final multi-GPU quote. Confirm the price for the actual configuration you plan to rent, along with regional availability and any applicable taxes.
AWS EC2: GPU instances and cluster networking
AWS positions P5 instances around H100 GPUs and P5e/P5en around H200 GPUs. The families offer up to eight GPUs per instance and are designed for deep learning and HPC. AWS describes GPU interconnect using NVSwitch and networking with Elastic Fabric Adapter (EFA), including support for scaling clusters. Those specifications may matter for distributed training, where communication between GPUs and nodes can affect how efficiently a workload uses its accelerators.
Rank #2
- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
- Advanced RDNA 4 with AI Accelerators: 64 Compute Units with 3rd Gen Ray Tracing and dedicated 2nd Gen AI Accelerators for groundbreaking AI performance and visual computing.
- Professional Blower Cooling: Efficient single blower design exhausts heat directly out of the chassis, ideal for multi-GPU workstation and server configurations.
- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
That is a description of AWS’s product capabilities, not an independent performance comparison with Lambda, Azure, or Google Cloud. Match the instance and purchase model to your workload before treating an AWS hourly figure as comparable to another provider’s price.
Microsoft Azure: build a SKU-specific estimate
The reviewed Azure Linux Virtual Machines pricing page sends customers to its pricing calculator rather than providing a directly comparable H100/H200 SKU rate. Build an estimate with the GPU VM SKU and region you intend to use, Linux image, expected runtime, and purchase plan. Include persistent disks separately: Azure says they are charged independently, and standard egress charges apply.
Rank #3
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
VM lifecycle state affects billing. According to Azure’s pricing FAQ, a VM that is stopped but remains allocated can continue to incur charges; deallocation ends compute allocation billing. Include the actual shutdown and deallocation behavior in any estimate rather than assuming that a stopped workload has stopped costing money.
Google Cloud: GPU cost is additional to the VM
Google Cloud identifies H100 80 GB GPUs with its A3 accelerator-optimized machine types. GPU charges are additional to the VM machine-type price, and GPU prices vary by region. Use the Google Cloud pricing calculator to combine the accelerator and machine costs for the intended location.
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- Robust Design:Constructed to withstand high temperatures, the V100 16GB SXM2 card operates efficiently up to 105℃.
- Advanced Connectivity:Features a SXM2 connector for seamless integration with a wide range of systems, ensuring compatibility.
Discount eligibility depends on the purchase model. Eligible GPU resources may receive sustained-use discounts. Spot GPU usage follows Spot prices and does not receive sustained-use discounts. Resource-based committed-use discounts require GPU reservations. The published GPU price information also excludes costs such as disks, images, networking, sole-tenant nodes, and VM instance pricing, so it cannot stand alone as a total-cost estimate.
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What matters beyond the GPU-hour rate
- GPU model, memory, and count: Compare the same accelerator generation and memory capacity, and note whether your run fits on one GPU, one node, or a multi-node cluster. A price per GPU-hour does not make unlike configurations equivalent.
- Interconnect and network: For distributed training, include the node-to-node network and GPU interconnect in the decision. AWS publishes EFA and NVSwitch details for its P5 families; the available specifications here do not establish a same-workload network benchmark across all four providers.
- Location: Select the required region or zone before comparing rates. Regional pricing and availability can differ, and data residency or latency requirements may rule out a nominally cheaper location.
- Billing commitment: Distinguish on-demand from Spot or preemptible usage, reservations, Capacity Blocks, and committed-use discounts. Lower or discounted rates can involve interruption risk, capacity constraints, reservation requirements, or a commitment you may not use fully.
- Storage and data movement: Count checkpoint and dataset storage, data staging, and egress. Lambda advertises no egress fees, while Azure states standard egress charges apply; Google Cloud’s listed GPU information excludes networking and disks. Verify the relevant provider’s charge categories for the planned architecture.
- Capacity and operations: Check quota, regional capacity, lead time, access requirements, and how you will start, stop, and recover instances. For Lambda, confirm the desired configuration and location in the live console; for other providers, validate that the required quota and capacity are available before scheduling a long run.
A fair way to compare total workload cost
- Define one workload scenario. Fix the model, GPU type and count, memory needs, training or inference task, and whether it must run on one node or across multiple nodes.
- Choose a location and buying model. Record the region or zone and decide whether the estimate is for on-demand use, Spot/preemptible capacity, a reservation, a commitment, or an AWS Capacity Block. Do not combine rates from different purchase models in a single price ranking.
- Estimate the full runtime. Include startup, data staging, training or inference, checkpointing, idle time, and shutdown. For interruptible capacity, account for the possibility of lost progress or a rerun.
- Add the surrounding resources. Include CPU, RAM, VM or machine-type charges, local and persistent storage, images, and the network transfer required by the workload.
- Check feasibility before committing. Confirm GPU availability, quota, geographic fit, and any reservation or capacity requirements. A published rate is not evidence that the needed capacity is available when you need it.
- Record the estimate’s date and scope. State the provider, SKU, region, usage hours, purchase terms, included resources, and omitted costs. Refresh the calculation before committing: AWS announced reductions for several EC2 NVIDIA GPU instance families effective June 1, 2025 for On-Demand pricing and after June 4, 2025 for Savings Plan purchases, illustrating why older price tables should not be treated as current quotes.
Which provider should you shortlist?
- Start with Lambda if a straightforward GPU-hour offer, minute-level billing, and its advertised no-egress-fee policy fit your needs, and the required configuration is available in the region you need.
- Evaluate AWS P5, P5e, or P5en if the H100/H200 instance specifications and published cluster networking are relevant to your multi-GPU or multi-node design. Price the purchase model you will actually use; Capacity Blocks are not a universal On-Demand quote.
- Build an Azure calculator estimate when an Azure GPU VM is a candidate, using the exact SKU and region and including disks, egress, and instance lifecycle behavior.
- Build a Google Cloud calculator estimate when an A3/H100 configuration is a candidate, combining GPU and VM charges and applying the discount rules that match your reservation or Spot plan.
No independent study identified for this comparison benchmarks the four providers on the same AI workload. Vendor hardware and pricing pages can support a configuration and cost comparison, but they do not establish a universal performance-per-dollar winner.
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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.
