Provider Comparison

AWS vs Salad

AWS stands as the market leader in cloud computing, offering robust GPU infrastructure deeply integrated with services like SageMaker for end-to-end ML workflows. It excels in enterprise environments needing global redundancy across availability zones, proprietary chips like Trainium and Inferentia for cost-efficient training and inference, and comprehensive compliance (SOC 2, HIPAA, GDPR, ISO 27001). However, its pricing complexity, including egress fees, and higher costs make it less ideal for budget-constrained batch workloads. Salad, conversely, leverages a decentralized network of consumer GPUs from residential nodes, positioning it as a cost-disruptor for massive, fault-tolerant batch jobs and inference. Its unique value lies in the lowest pricing through underutilized hardware, appealing to teams prioritizing affordability over premium reliability. Billing is per-second with spot instances for both, but Salad's model targets opportunistic, high-volume compute. Key differentiators include AWS's ecosystem integration and reliability versus Salad's extreme cost savings and decentralization. AWS suits production-scale enterprises with complex needs, while Salad targets experimental or high-throughput batch processing where fault tolerance mitigates node variability. Overall, AWS provides a mature, full-stack solution at a premium; Salad offers unmatched economics for tolerant workloads, though with potential uncertainties in consistency due to its nascent, distributed nature.

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Our Recommendation

Choose AWS for large enterprises (50+ engineers) requiring seamless integration with tools like SageMaker, global high availability, strict compliance (e.g., HIPAA), and reliable real-time inference or multi-region deployments. It's ideal for budgets supporting premium pricing ($3-10+/hr for A100 equivalents) and teams needing managed services amid complex workloads. Opt for Salad when running massive batch jobs or fault-tolerant inference on tight budgets (<$1/hr potential via consumer GPUs), suitable for smaller teams (1-20 engineers) focused on cost over latency. Best for non-critical, high-volume training where decentralization's variability is acceptable. Avoid Salad for latency-sensitive production without fault tolerance. Hybrid approaches—AWS for dev/prod, Salad for bulk training—maximize value. Evaluate via trials, considering Salad's limited transparency on node quality.

Live Pricing

Compare real-time GPU offers from AWS and Salad

47 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
Salad	NVIDIA GeForce RTX 2060 6GB VRAM	6GB	1 vCPU 1GB RAM 1GB Storage	🌍global	$0.05/GPU/hr	Available
Salad	NVIDIA GeForce RTX 2070 8GB VRAM	8GB	1 vCPU 1GB RAM 1GB Storage	🌍global	$0.06/GPU/hr	Available
Salad	NVIDIA GeForce RTX 2080 8GB VRAM	8GB	1 vCPU 1GB RAM 1GB Storage	🌍global	$0.08/GPU/hr	Available
Salad	NVIDIA GeForce RTX 3060 12GB VRAM	12GB	1 vCPU 1GB RAM 1GB Storage	🌍global	$0.08/GPU/hr	Available
Salad	NVIDIA GeForce RTX 3060 Ti 8GB VRAM	8GB	1 vCPU 1GB RAM 1GB Storage	🌍global	$0.08/GPU/hr	Available

Salad

🌍global

Available

NVIDIA GeForce RTX 2060

6GB VRAM

1 vCPU

1GB RAM

1GB Storage

$0.05/GPU/hr

Salad

🌍global

Available

NVIDIA GeForce RTX 2070

8GB VRAM

1 vCPU

1GB RAM

1GB Storage

$0.06/GPU/hr

Salad

🌍global

Available

NVIDIA GeForce RTX 2080

8GB VRAM

1 vCPU

1GB RAM

1GB Storage

$0.08/GPU/hr

Salad

🌍global

Available

NVIDIA GeForce RTX 3060

12GB VRAM

1 vCPU

1GB RAM

1GB Storage

$0.08/GPU/hr

Salad

🌍global

Available

NVIDIA GeForce RTX 3060 Ti

8GB VRAM

1 vCPU

1GB RAM

1GB Storage

$0.08/GPU/hr

View all 47 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

Salad(Est. 2018)

A decentralized cloud using consumer GPUs for massive batch jobs and fault-tolerant inference.

Best For

Massive batch jobsFault-tolerant inference

Unique Features

Lowest pricing via residential node network
Decentralized consumer GPU network

Feature Comparison

Access Methods

Feature	AWS	Salad
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

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

Compliance

Certification	AWS	Salad
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	AWS	Salad
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Both providers use per-second billing with spot instances for cost savings, enabling fine-grained usage without hourly minimums—ideal for variable ML workloads. AWS offers on-demand, spot (up to 90% discounts), reserved instances (1-3 year commitments for 40-70% savings), and savings plans, but pricing is complex with add-ons like data transfer egress ($0.09/GB out), storage, and regional variances (e.g., A100 at ~$3.50/hr on-demand). Salad emphasizes spot-like access to consumer GPUs at the lowest rates (often <$0.50/hr equivalents), lacking reserved options but minimizing extras via decentralized model. Implications: AWS favors predictable, long-term use with optimization tools; Salad suits bursty, high-volume jobs where absolute cost trumps predictability, though spot interruptions require resilient code.

Value Assessment

Salad delivers superior value for massive batch training or inference (e.g., 10k+ GPU-hours), potentially 5-10x cheaper than AWS via residential GPUs, ideal for experiments or non-urgent jobs tolerant to interruptions. AWS provides better value for small-to-medium experiments (under 100 GPU-hours) via SageMaker's managed efficiencies and spot reliability, or production inference needing consistent uptime—offsetting costs with Trainium (up to 50% cheaper than GPUs). For large training runs, Salad wins on raw economics if fault-tolerant; AWS excels in integrated workflows reducing dev time. Budgets under $10k/month favor Salad; enterprise-scale with compliance leans AWS despite premiums.

Use Case Comparison

LLM Training

AWS recommended

AWS

AWS excels with scalable P5 instances (8x H100s), Trainium for cost-efficient pre-training, SageMaker for distributed training via Ray/SMX, and global AZs for fault tolerance. Deep integration handles petabyte-scale datasets with EFS/S3, ensuring reliability for weeks-long runs. Spot instances cut costs 70%, but base pricing remains high (~$30+/hr per 8-GPU node). Ideal for enterprises needing orchestration and monitoring.

Salad

Salad suits cost-sensitive large-scale training via cheap consumer GPUs (e.g., RTX 4090 equivalents), leveraging decentralization for massive parallelism. Fault-tolerant designs mitigate node variability/interruptions, but lacks enterprise-scale reliability, managed tools, or high-end interconnects like NVLink. Best for budget runs where checkpointing handles churn; performance uncertainty due to residential hardware.

Batch Inference

Salad recommended

AWS

AWS supports batch inference via SageMaker Batch Transform or EC2 with Inferentia for throughput optimization, integrating with S3 for inputs/outputs. Scalable but costlier (~$1-5/hr per GPU), with egress fees adding overhead for large datasets. Reliable queuing and autoscaling suit moderate volumes.

Salad

Salad shines for massive batch inference on fault-tolerant workloads, offering lowest costs (<$0.50/hr) across decentralized consumer GPUs. Residential network handles high volumes economically, with per-second billing perfect for variable jobs. Variability requires robust error handling, but ideal for non-urgent, terascale inferences.

Real-time Inference

AWS recommended

AWS

AWS dominates with low-latency endpoints via SageMaker, ECS/Fargate, or Inferentia/Tranium for optimized serving (e.g., <100ms p99). Global endpoints, auto-scaling, and VPC networking ensure production reliability. Compliance and monitoring (CloudWatch) support enterprise SLAs.

Salad

Salad's decentralized consumer GPUs introduce latency variability and unreliability, unsuitable for real-time needs without heavy fault tolerance. Residential nodes lack consistent networking/bandwidth for sub-second responses; better for async workloads. Limited data on production viability.

Fine-tuning & Experimentation

Either works

AWS

AWS's SageMaker Studio, JumpStart models, and spot A10G/H100s enable rapid iteration with notebooks, hyperparameter tuning, and cheap storage. Ecosystem accelerates prototyping, though costs accumulate for frequent small runs.

Salad

Salad offers ultra-cheap access for iterative fine-tuning on consumer GPUs, per-second billing minimizing waste for short experiments. Decentralization suits variable needs, but setup lacks managed IDEs; node diversity aids diverse testing if tolerant to interruptions.

Technical Comparison

Infrastructure

AWS provides virtualized EC2 instances with bare-metal options (e.g., i4i), high-speed NVLink/Elastic Fabric Adapter (up to 3.2Tbps), EBS/GP3 storage (up to 256K IOPS), S3 integration, and EKS for Kubernetes orchestration across 30+ regions/AZs. Highly reliable with SLAs >99.99%. Salad uses a fully decentralized, serverless network of residential consumer GPUs (no virtualization overhead), with peer-to-peer storage/networking; lacks native Kubernetes but supports containerized jobs. Limited details on storage (likely ephemeral) and global footprint; focuses on batch via API.

Performance

AWS delivers consistent high performance with datacenter GPUs (H100/A100), excellent multi-GPU scaling via NCCL/Ring, and low inter-node latency; Trainium boosts TFLOPs/watt. Salad's consumer GPUs (e.g., 3090/4090) offer strong single-node perf for price but variable availability, lower interconnects (consumer Ethernet), and churn impacting long runs—suits fault-tolerant apps with checkpointing. Multi-GPU scaling possible but less efficient; uncertainties in aggregate throughput due to node heterogeneity.

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both AWS and Salad 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 Salad 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. Salad holds GDPR certification. For organizations with strict compliance requirements, AWS offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

AWS offers built-in Jupyter notebook support for interactive development, while Salad requires you to set up your own notebook environment. If quick iteration and experimentation are priorities, AWS's integrated notebooks provide a smoother experience. Additionally, AWS offers web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

Both AWS and Salad 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. Salad excels at Massive batch jobs; Fault-tolerant inference. 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?▾

AWS offers reserved instance pricing for long-term commitments, while Salad does not currently offer this option. 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 Salad may have more limited support tiers. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); Salad has no published SLA.

Which provider has better API and automation support?▾

Both AWS and Salad 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 Salad 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. Salad's standout features include: Lowest pricing via residential node network; Decentralized consumer GPU network. 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 Salad, visit https://salad.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.