Provider Comparison

AWS vs VERDA

Q: Which provider offers spot instances for cost savings?

AWS offers spot/preemptible instances, which can significantly reduce costs (typically 50-80% off on-demand prices) for interruptible workloads like batch processing and training with checkpoints. VERDA does not currently offer spot instances, so all usage is billed at on-demand rates. If cost optimization through spot instances is important for your workflow, AWS would be the better choice.

Q: What is the minimum billing increment for each provider?

AWS bills per-second, while VERDA bills per-hour. Per-second billing from AWS offers better cost efficiency for short experiments and iterative development, as you only pay for exactly what you use.

Q: Which provider has better compliance certifications for enterprise use?

AWS holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. VERDA holds GDPR, ISO 27001 certifications. For organizations with strict compliance requirements, AWS offers more comprehensive coverage.

Q: Which provider offers better development tools like Jupyter notebooks?

Both AWS and VERDA 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, AWS offers web-based terminal access for quick debugging.

Q: Which provider has better Kubernetes support for orchestration?

Both AWS and VERDA 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.

Q: 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. VERDA excels at Sustainable AI training in Europe. 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.

Q: Which provider offers reserved instances for long-term savings?

Both AWS and VERDA 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.

Q: Which provider offers better enterprise support?

AWS offers dedicated enterprise support options, while VERDA may have more limited support tiers. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); VERDA has no published SLA.

Q: Which provider has better API and automation support?

Both AWS and VERDA 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.

Q: Which provider has better container and Docker support?

AWS offers native container support for running Docker images, while VERDA may require additional configuration. Container support is valuable for reproducible ML pipelines and easy deployment of pre-built environments.

AWS stands as the dominant cloud provider with extensive GPU integration for machine learning workloads, offering seamless scalability across global availability zones. It excels in deep ecosystem integration, including SageMaker for fully managed ML pipelines and proprietary chips like Trainium for training and Inferentia for inference, making it ideal for large enterprises needing reliability, compliance (SOC 2, HIPAA, GDPR, ISO 27001), and hybrid services. However, its pricing complexity, including egress fees, and higher costs can deter smaller teams. VERDA, in contrast, positions itself as a sustainable alternative focused on green computing in Europe, repurposing GPU waste heat for district heating. This appeals to environmentally conscious organizations prioritizing carbon-neutral AI training. Its per-hour billing simplifies costs but lacks the granularity of AWS. Compliance covers GDPR and ISO 27001, suitable for EU regulations, though global reach and feature depth are limited. Key differentiators include AWS's mature, feature-rich platform versus VERDA's eco-focus and potentially lower European costs. AWS suits production-scale, globally distributed workloads requiring redundancy and advanced tools, while VERDA targets sustainable, regional projects where green credentials outweigh ecosystem breadth. Overall, AWS offers unmatched reliability for mission-critical AI, but VERDA provides a compelling niche for ethical computing with fewer complexities.

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Our Recommendation

Choose AWS for large-scale enterprises (100+ engineers) running distributed training or inference across regions, needing SageMaker integration, spot instances for cost optimization, or compliance like HIPAA. It's ideal for budgets over $100K/month where global redundancy and ecosystem lock-in justify premiums. Opt for VERDA in Europe-based teams (under 50 engineers) focused on sustainable AI training, with budgets sensitive to per-hour simplicity and green mandates. It's suitable for mid-sized projects emphasizing ESG goals, but verify GPU availability and scaling for production. For hybrid needs, start with AWS prototyping and migrate to VERDA if sustainability trumps performance. Avoid VERDA for latency-sensitive global apps due to regional focus.

Live Pricing

Compare real-time GPU offers from AWS and VERDA

73 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
VERDA	4×NVIDIA Tesla V100 16GB 16GB VRAM	16GB	20 vCPU 90GB RAM	Finland	$0.14/GPU/hr $0.55/hr total (4×)	Sold Out
VERDA	NVIDIA Tesla V100 16GB 16GB VRAM	16GB	6 vCPU 23GB RAM	Finland	$0.14/GPU/hr	Sold Out
VERDA	NVIDIA Tesla V100 16GB 16GB VRAM	16GB	6 vCPU 23GB RAM	Finland	$0.14/GPU/hr	Sold Out
VERDA	2×NVIDIA Tesla V100 16GB 16GB VRAM	16GB	10 vCPU 45GB RAM	Finland	$0.14/GPU/hr $0.28/hr total (2×)	Sold Out
VERDA	2×NVIDIA Tesla V100 16GB 16GB VRAM	16GB	10 vCPU 45GB RAM	Finland	$0.14/GPU/hr $0.28/hr total (2×)	Sold Out

VERDA

Finland

Sold Out

NVIDIA Tesla V100 16GB4x

16GB VRAM

20 vCPU

90GB RAM

$0.14/GPU/hr

$0.55/hr total (4×)

VERDA

Finland

Sold Out

NVIDIA Tesla V100 16GB

16GB VRAM

6 vCPU

23GB RAM

$0.14/GPU/hr

VERDA

Finland

Sold Out

NVIDIA Tesla V100 16GB

16GB VRAM

6 vCPU

23GB RAM

$0.14/GPU/hr

VERDA

Finland

Sold Out

NVIDIA Tesla V100 16GB2x

16GB VRAM

10 vCPU

45GB RAM

$0.14/GPU/hr

$0.28/hr total (2×)

VERDA

Finland

Sold Out

NVIDIA Tesla V100 16GB2x

16GB VRAM

10 vCPU

45GB RAM

$0.14/GPU/hr

$0.28/hr total (2×)

View all 73 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

VERDA(Est. 2018)

A provider focused on green computing using waste heat for district heating.

Best For

Sustainable AI training in Europe

Unique Features

Use of waste heat for district heating
Green computing focus

Feature Comparison

Access Methods

Feature	AWS	VERDA
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

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

Compliance

Certification	AWS	VERDA
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	AWS	VERDA
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

AWS employs per-second billing for EC2 instances, enabling precise cost control for variable workloads, complemented by spot instances (up to 90% savings), reserved instances (1-3 year commitments for 40-70% discounts), and savings plans. This flexibility suits bursty ML jobs but introduces complexity with data transfer egress fees ($0.09/GB out). VERDA uses straightforward per-hour billing, likely on-demand without mentioned spot or reserved options, reducing administrative overhead but penalizing short runs (e.g., <1 hour experiments waste partial hours). Implications: AWS favors intermittent or long-running jobs via spots; VERDA benefits steady, predictable usage in Europe, though lacks granular savings mechanisms. Limited VERDA details mean assuming standard on-demand rates.

Value Assessment

For small experiments and fine-tuning (<1 day), AWS spot instances deliver superior value via per-second billing and preemptible savings, often under $0.50/hour for A100 equivalents. VERDA's per-hour model inflates costs for sub-hour tasks. Large training runs (multi-week) favor AWS reserved/spot combos for 50-70% savings, especially with Trainium at lower TCO. Production inference sees AWS edge via Inferentia efficiencies and global scaling. VERDA shines for sustained European batch jobs where green premiums offset 10-20% higher on-demand rates (estimated), appealing to ESG-focused budgets. Overall, AWS provides better value for dynamic, cost-optimized workloads; VERDA for fixed-hour sustainable commitments, pending pricing transparency.

Use Case Comparison

LLM Training

AWS recommended

AWS

AWS excels with Trainium clusters for cost-efficient scaling to thousands of GPUs, SageMaker for distributed training, and spot instances minimizing costs for multi-day runs. Global AZs ensure high availability, with proven performance on models like Llama. Egress and management complexity offset by automation tools.

VERDA

VERDA supports sustainable training via waste-heat GPUs, ideal for Europe-centric large models emphasizing green credentials. Per-hour billing suits long runs, but limited info on multi-GPU scaling or cluster sizes raises uncertainty for massive LLMs. Best for eco-focused mid-scale training.

Batch Inference

Either works

AWS

AWS Inferentia chips optimize throughput at low cost, integrated with SageMaker Batch Transform for serverless scaling. Spot instances and per-second billing enhance economics for periodic jobs; S3 integration streamlines data handling across regions.

VERDA

VERDA's green infrastructure suits batch workloads with steady GPU needs, leveraging per-hour simplicity. European locality reduces latency for regional data, but lacks specialized inference hardware or managed services, potentially increasing setup time.

Real-time Inference

AWS recommended

AWS

AWS SageMaker Endpoints with Inferentia/accelerators provide low-latency, auto-scaling inference globally. Per-second billing and Lambda integration support variable traffic; robust networking (e.g., 400Gbps) ensures performance for production APIs.

VERDA

VERDA may handle real-time via standard GPUs, but regional focus limits global low-latency. No proprietary inference chips or endpoint management noted; per-hour billing less ideal for always-on services, with unknown autoscaling capabilities.

Fine-tuning & Experimentation

AWS recommended

AWS

AWS spot instances and per-second billing make short, iterative experiments economical (e.g., $0.20-1/hour A10G). SageMaker notebooks accelerate prototyping with Jupyter integration and JumpStart models.

VERDA

VERDA's per-hour model suits longer experiments but wastes on quick iterations. Green focus appeals for proof-of-concepts, though sparse tooling details suggest more manual setup compared to AWS ecosystems.

Technical Comparison

Infrastructure

AWS offers virtualized EC2 with Nitro hypervisor, bare-metal options, high-bandwidth Elastic Fabric Adapter (up to 3.2Tbps/node), EBS/EFS storage, and EKS for Kubernetes orchestration across 30+ regions. Multi-AZ redundancy ensures 99.99% uptime. VERDA, focused on Europe, likely provides GPU instances with green data centers; details sparse on virtualization (possibly bare-metal for efficiency), networking, storage (e.g., no global object store equivalent), or Kubernetes support, limiting portability.

Performance

AWS delivers benchmarked multi-GPU scaling (e.g., 16x H100s via Trainium2 at 50% Trainium1 perf), with low inter-node latency and P4d/P5 instances topping MLPerf charts. VERDA's GPU availability (types/sizes unknown) emphasizes sustainability over raw speed; waste-heat reuse may constrain dense packing, potentially impacting scaling. No public benchmarks; assume standard NVIDIA GPUs with European locality aiding data transfer but lacking AWS's optimized interconnects.

Frequently Asked Questions

Which provider offers spot instances for cost savings?▾

AWS offers spot/preemptible instances, which can significantly reduce costs (typically 50-80% off on-demand prices) for interruptible workloads like batch processing and training with checkpoints. VERDA does not currently offer spot instances, so all usage is billed at on-demand rates. If cost optimization through spot instances is important for your workflow, AWS would be the better choice.

What is the minimum billing increment for each provider?▾

AWS bills per-second, while VERDA bills per-hour. Per-second billing from AWS offers better cost efficiency for short experiments and iterative development, as you only pay for exactly what you use.

Which provider has better compliance certifications for enterprise use?▾

AWS holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. VERDA holds GDPR, ISO 27001 certifications. For organizations with strict compliance requirements, AWS offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

Both AWS and VERDA 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, AWS offers web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

Both AWS and VERDA 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. VERDA excels at Sustainable AI training in Europe. 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 VERDA 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?▾

AWS offers dedicated enterprise support options, while VERDA may have more limited support tiers. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); VERDA has no published SLA.

Which provider has better API and automation support?▾

Both AWS and VERDA 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?▾

AWS offers native container support for running Docker images, while VERDA may require additional configuration. Container support is valuable for reproducible ML pipelines and easy deployment of pre-built 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. VERDA's standout features include: Use of waste heat for district heating; Green computing focus. 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 VERDA, visit https://verda.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.