Provider Comparison

AWS vs CoreWeave

AWS, the market leader in cloud computing, offers robust GPU infrastructure deeply integrated with services like SageMaker, EC2 P5 instances (H100s), and proprietary Trainium/Inferentia chips for ML workloads. It excels in global redundancy across 30+ regions, making it ideal for enterprises needing seamless integration with storage (S3), data processing (Glue), and orchestration tools. However, its pricing complexity, including data egress fees, and virtualized overhead can increase costs for pure GPU compute. CoreWeave positions itself as a GPU-native hyperscaler, optimized for massive-scale AI training and rendering via Kubernetes-native deployments on InfiniBand-backed clusters with up to thousands of NVIDIA H100/A100 GPUs. It targets sophisticated teams running LLMs or VFX pipelines, providing low-latency, high-bandwidth networking superior for multi-node scaling. Limitations include potential inventory shortages for new users and less mature ecosystem integrations compared to AWS. Key differentiators: AWS prioritizes managed services and compliance for production; CoreWeave emphasizes raw performance and cost-efficiency for bursty, compute-intensive workloads. AWS suits hybrid cloud strategies; CoreWeave delivers better GPU density and pricing for dedicated AI factories. Overall, AWS offers reliability at scale for diverse needs, while CoreWeave provides specialized value for GPU-bound tasks, with choice depending on integration depth versus performance purity. (238 words)

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Our Recommendation

Choose AWS for large enterprises with existing investments in its ecosystem, requiring global availability zones, managed ML pipelines (SageMaker), or compliance-heavy deployments like HIPAA workloads. It's ideal for teams of 50+ managing diverse services alongside GPUs, where budgets accommodate premium pricing for reliability over raw cost savings. Opt for CoreWeave if your team (20-100 engineers) focuses on LLM training or VFX rendering at hyperscale, leveraging Kubernetes for orchestration and InfiniBand for efficient multi-GPU scaling. It's preferable for budgets prioritizing 20-40% GPU cost reductions on long-running jobs, but ensure your workflow tolerates potential queue times for H100 capacity. For small teams experimenting, AWS's spot instances and SageMaker Studio offer easier entry; scale to CoreWeave for production training runs exceeding 100 GPUs. Hybrid approaches work for inference needs. (142 words)

Live Pricing

Compare real-time GPU offers from AWS and CoreWeave

32 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price
AWS	NVIDIA Tesla T4 16GB VRAM	16GB	4 vCPU 16GB RAM	Virginia	$0.53/GPU/hr
AWS	NVIDIA Tesla T4 16GB VRAM	16GB	8 vCPU 32GB RAM	Virginia	$0.75/GPU/hr
AWS	4×NVIDIA Tesla T4 16GB VRAM	16GB	48 vCPU 192GB RAM	Virginia	$0.98/GPU/hr $3.91/hr total (4×)
AWS	NVIDIA RTX A6000 48GB VRAM	48GB	4 vCPU 16GB RAM	Virginia	$1.01/GPU/hr
CoreWeave	8×NVIDIA A100 PCIe 80GB 80GB VRAM	80GB	128 vCPU 0GB RAM 7680GB Storage	United States	$1.19/GPU/hr $9.51/hr total (8×)

AWS

Virginia

NVIDIA Tesla T4

16GB VRAM

4 vCPU

16GB RAM

$0.53/GPU/hr

AWS

Virginia

NVIDIA Tesla T4

16GB VRAM

8 vCPU

32GB RAM

$0.75/GPU/hr

AWS

Virginia

NVIDIA Tesla T44x

16GB VRAM

48 vCPU

192GB RAM

$0.98/GPU/hr

$3.91/hr total (4×)

AWS

Virginia

NVIDIA RTX A6000

48GB VRAM

4 vCPU

16GB RAM

$1.01/GPU/hr

CoreWeave

United States

NVIDIA A100 PCIe 80GB8x

80GB VRAM

128 vCPU

0GB RAM

7680GB Storage

$1.19/GPU/hr

$9.51/hr total (8×)

View all 32 offers

AWS(Est. 2006)

The dominant force in global cloud computing with deep integration of GPUs into its ecosystem for machine learning and other services.

Best For

Large-scale enterprises requiring deep integration with other cloud servicesOrganizations needing globally redundant availability zones

Unique Features

Proprietary silicon like Trainium and Inferentia chips
Fully managed ML development environment with SageMaker

Limitations

High cost relative to specialized clouds
Complexity of pricing including egress fees

CoreWeave(Est. 2017)

A premier specialized GPU cloud designed for massive-scale AI training and VFX rendering with Kubernetes-native architecture.

Best For

Sophisticated engineering teams training LLMs at scaleVFX studios requiring burst rendering capacity

Unique Features

Kubernetes-native architecture
Access to massive-scale InfiniBand clusters

Limitations

Inventory often constrained for new or smaller users

Feature Comparison

Access Methods

Feature	AWS	CoreWeave
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	AWS	CoreWeave
Billing Increment	per-second	per-second
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	AWS	CoreWeave
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	AWS	CoreWeave
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Both providers bill per-second for on-demand and spot instances, enabling fine-grained cost control for variable workloads. AWS offers spot instances (up to 90% savings), Savings Plans, and Reserved Instances with commitment discounts up to 72%, but pricing varies by region/instance type with add-ons like EBS storage (~$0.10/GB-month) and egress fees ($0.09/GB out). CoreWeave mirrors per-second/spot billing without public reserved options, emphasizing transparent GPU-hour rates (e.g., H100 ~$2.39/hour on-demand vs. AWS ~$32.77/P5.48xlarge, though normalized differently). Implications: Short bursts favor spots on both; long-term commitments save more on AWS via reservations. AWS's complexity suits cost-optimization teams; CoreWeave's simplicity benefits rapid scaling but lacks AWS's volume discounts for non-GPU services. Egress impacts data-heavy AWS pipelines more. (152 words)

Value Assessment

CoreWeave delivers superior value for large-scale training runs (e.g., 100+ H100s), offering 30-50% lower effective GPU costs via high utilization and InfiniBand efficiency, ideal for 24/7 jobs. AWS edges small experiments/fine-tuning with SageMaker's pay-per-use notebooks and spot diversity, minimizing upfront commitments. For production inference, AWS provides better value through auto-scaling endpoints and Trainium (up to 50% cheaper than GPUs), integrating with API Gateway. Batch inference favors CoreWeave for raw throughput on clusters. Budget-conscious teams save most with CoreWeave spots for intermittent loads; AWS suits predictable inference with reservations. Overall, CoreWeave wins GPU-intensive (value/hour), AWS integrated/versatile workloads. (148 words)

Use Case Comparison

LLM Training

CoreWeave recommended

AWS

AWS supports multi-node training via P5 instances with Elastic Fabric Adapter (EFA) for up to 8x H100s/node and SageMaker for managed distributed jobs. Trainium clusters enable cost-effective pre-training, but EFA latency trails InfiniBand, and virtualization adds ~5-10% overhead for massive scales. Strong for hybrid data pipelines with S3/Glue integration. (68 words)

CoreWeave

CoreWeave excels with Kubernetes-orchestrated InfiniBand clusters scaling to 1000s of H100s/A100s, delivering near-line-rate NVLink/RoCE bandwidth for efficient all-reduce. Purpose-built for LLMs, minimizing placement groups and queue times for engineering teams. Ideal for weeks-long runs with high GPU utilization. (64 words)

Batch Inference

Either works

AWS

AWS leverages SageMaker Batch Transform or EC2 autoscaling for cost-optimized inference on G5/Infentia, integrating with S3 for input/output. Spot instances handle variable loads efficiently, with managed monitoring via CloudWatch. Suits enterprises with data gravity in AWS. (62 words)

CoreWeave

CoreWeave's Kubernetes enables elastic batch pods on GPU clusters, with InfiniBand accelerating large-batch parallel inference. High GPU density reduces costs for VFX/rendering batches, but requires custom orchestration vs. AWS managed options. Strong for compute-bound jobs. (60 words)

Real-time Inference

AWS recommended

AWS

AWS shines with SageMaker Endpoints, Lambda@Edge, or ECS Fargate for low-latency serving on Inferentia/Trainium (sub-$1/hour effective). Auto-scaling, API integrations, and global edge caching ensure production reliability for high-QPS apps. Compliance-ready for regulated industries. (64 words)

CoreWeave

CoreWeave supports real-time via Kubernetes deployments on GPUs, but lacks AWS's fully-managed serverless options. InfiniBand aids low-latency clusters, fitting custom serving frameworks like Triton, though setup overhead is higher for small-scale deployments. (60 words)

Fine-tuning & Experimentation

AWS recommended

AWS

SageMaker Studio provides Jupyter-like environments with spot-backed GPUs, hyperparameter tuning, and one-click fine-tuning on Hugging Face models. Easy collaboration and integration with Git/S3 make it accessible for small teams iterating rapidly without infra management. (64 words)

CoreWeave

CoreWeave's Kubernetes-native pods support experimentation via Helm charts and JupyterHub, with on-demand H100 access. Efficient for multi-GPU fine-tuning, but requires DevOps expertise for scaling experiments compared to AWS's managed UI/tools. Inventory waits possible. (62 words)

Technical Comparison

Infrastructure

AWS employs virtualized EC2 instances (e.g., p5.48xlarge: 8x H100s) with EFA networking (up to 3.2Tbps/node), EBS/GP3 storage, and optional FSx Lustre. Kubernetes via EKS with GPU support. CoreWeave uses bare-metal Kubernetes clusters with InfiniBand (400Gbps+), high-density NVIDIA DGX pods, and distributed block storage. AWS offers broader storage (S3/EFS); CoreWeave prioritizes low-latency NVMe-over-fabrics. (98 words)

Performance

CoreWeave leads in multi-node scaling with InfiniBand enabling 95%+ scaling efficiency for LLM training (e.g., faster than AWS EFA per MLPerf benchmarks). AWS P5 delivers strong single-node NVLink but trails in cross-node all-reduce; Trainium optimizes training FLOPS/cost. Both offer H100/A100 availability, but CoreWeave has denser clusters with less contention for large reservations. AWS better for mixed workloads. (96 words)

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both AWS and CoreWeave offer spot/preemptible instances, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and distributed training with checkpointing. The actual savings depend on current demand and GPU availability, so we recommend comparing real-time spot prices for your specific GPU requirements on both platforms.

What is the minimum billing increment for each provider?▾

AWS bills per-second, while CoreWeave bills per-second. Both providers use the same billing granularity, so this factor won't differentiate your decision.

Which provider has better compliance certifications for enterprise use?▾

AWS holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. CoreWeave holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. Both providers have similar compliance postures. Check with each provider directly for the most current certification status and specific compliance documentation.

Which provider offers better development tools like Jupyter notebooks?▾

Both AWS and CoreWeave offer built-in Jupyter notebook support, making it easy to start experimenting without additional setup. This is particularly valuable for data scientists and researchers who prefer interactive development environments. Additionally, both providers offer web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

Both AWS and CoreWeave support Kubernetes for container orchestration, enabling you to deploy scalable ML pipelines, manage distributed training jobs, and integrate with MLOps tools like Kubeflow. This is essential for teams running production workloads at scale.

What is each provider best suited for?▾

AWS is best suited for Large-scale enterprises requiring deep integration with other cloud services; Organizations needing globally redundant availability zones. CoreWeave excels at Sophisticated engineering teams training LLMs at scale; VFX studios requiring burst rendering capacity. Understanding these specializations helps you choose the provider that aligns with your primary use case, though both can handle a variety of GPU computing needs.

Which provider offers reserved instances for long-term savings?▾

Both AWS and CoreWeave offer reserved instance pricing for committed usage, typically providing 20-40% discounts compared to on-demand rates. Reserved instances are ideal for predictable, steady-state workloads like always-on inference services. For variable workloads, on-demand or spot instances may offer better flexibility.

Which provider offers better enterprise support?▾

Both AWS and CoreWeave offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); CoreWeave offers SLA guarantees.

Which provider has better API and automation support?▾

Both AWS and CoreWeave provide APIs for programmatic instance management, enabling automation of provisioning, scaling, and teardown operations. This is essential for integrating GPU resources into CI/CD pipelines and automated ML workflows.

Which provider has better container and Docker support?▾

Both AWS and CoreWeave support containerized workloads, allowing you to deploy Docker images with your ML frameworks, dependencies, and models pre-configured. This ensures reproducibility and simplifies deployment across development, staging, and production environments.

What unique features differentiate these providers?▾

AWS's standout features include: Proprietary silicon like Trainium and Inferentia chips; Fully managed ML development environment with SageMaker. CoreWeave's standout features include: Kubernetes-native architecture; Access to massive-scale InfiniBand clusters. These differentiators may be decisive factors depending on your specific technical requirements and workflow preferences.

How do I get started with each provider?▾

To get started with AWS, visit their website at https://aws.amazon.com?utm_source=gpuperhour&utm_medium=referral to create an account and explore available GPU options. For CoreWeave, visit https://www.coreweave.com?utm_source=gpuperhour&utm_medium=referral to sign up. Both providers typically offer some form of free credits or trial period for new users. We recommend starting with a small experiment to evaluate the platform's ease of use, instance launch times, and overall fit for your workflow before committing to larger workloads.