Provider Comparison

Salad vs Vast.ai

Q: Which provider offers better spot instance pricing?

Both Salad and Vast.ai 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.

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

Salad bills per-second, while Vast.ai bills per-hour. Per-second billing from Salad 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?

Salad holds GDPR certification. Vast.ai holds GDPR certification. Both providers have similar compliance postures. Check with each provider directly for the most current certification status and specific compliance documentation.

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

Vast.ai 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, Vast.ai's integrated notebooks provide a smoother experience. Additionally, Vast.ai offers web-based terminal access for quick debugging.

Q: Which provider has better Kubernetes support for orchestration?

Salad offers native Kubernetes support for container orchestration, while Vast.ai does not. If you're building production ML pipelines with Kubernetes-based tools like Kubeflow, Argo, or KServe, Salad will integrate more seamlessly with your workflow.

Q: What is each provider best suited for?

Salad is best suited for Massive batch jobs; Fault-tolerant inference. Vast.ai excels at Absolute lowest costs; Distributed experiments. 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 better enterprise support?

Neither provider prominently advertises enterprise support tiers. Contact each provider directly to discuss custom support arrangements for production deployments.

Q: Which provider has better API and automation support?

Both Salad and Vast.ai 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?

Both Salad and Vast.ai 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.

Q: What unique features differentiate these providers?

Salad's standout features include: Lowest pricing via residential node network; Decentralized consumer GPU network. Vast.ai's standout features include: Granular search filters like DLPerf/$; Decentralized marketplace. These differentiators may be decisive factors depending on your specific technical requirements and workflow preferences.

Salad and Vast.ai are both decentralized GPU cloud providers leveraging consumer-grade hardware to deliver cost-effective compute for machine learning workloads, positioning themselves as alternatives to traditional hyperscalers like AWS or GCP. Salad focuses on massive batch jobs and fault-tolerant inference, utilizing a residential node network for the lowest pricing through per-second billing and spot instances. This makes it ideal for workloads tolerant of interruptions, where scale and cost efficiency on consumer GPUs shine. Its decentralized consumer GPU network emphasizes reliability for long-running, fault-tolerant tasks. Vast.ai, conversely, operates as a peer-to-peer marketplace prioritizing absolute lowest costs and distributed experiments. It offers granular search filters like DLPerf/$ (deep learning performance per dollar), enabling users to select instances based on precise cost-performance metrics. Billing is per-hour with spot options, suiting flexible, experimental use cases. Key differentiators include Salad's per-second granularity for short or variable-duration jobs versus Vast.ai's marketplace-driven discovery for optimized rentals. Both comply with GDPR, appealing to EU-based teams. Salad targets enterprises needing massive scale with built-in fault tolerance, while Vast.ai serves cost-conscious researchers and small teams running distributed experiments. Overall, Salad offers superior value for production-scale batch processing, while Vast.ai excels in exploratory, budget-optimized scenarios, with trade-offs in reliability and node consistency due to consumer hardware variability.

Visit Salad Visit Vast.ai

Our Recommendation

Choose Salad for large-scale, fault-tolerant workloads like massive batch training or inference where per-second billing minimizes costs for variable runtimes, and residential network scale handles high parallelism. Ideal for teams of 5+ engineers with budgets under $0.10/GPU-hour, prioritizing uptime via fault tolerance over peak performance. Opt for Vast.ai when absolute lowest costs and granular control are critical, such as distributed experiments or fine-tuning across diverse GPUs. Best for solo researchers or small teams (1-4 members) with unpredictable, short-to-medium jobs, leveraging DLPerf/$ filters to maximize value. Avoid Vast.ai for latency-sensitive production if node reliability is key; Salad suits better there. Consider team size, job duration, and tolerance for preemptions—Salad for enterprise reliability, Vast.ai for opportunistic savings.

Live Pricing

Compare real-time GPU offers from Salad and Vast.ai

74 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
Vast.ai	8×NVIDIA GeForce RTX 3060 12GB VRAM	12GB	24 vCPU 126GB RAM 738GB Storage	Quebec	$0.00/GPU/hr $0.01/hr total (8×)	Sold Out
Vast.ai	6×NVIDIA GeForce RTX 3080 Ti 12GB VRAM	12GB	8 vCPU 94GB RAM 1527GB Storage	Ukraine	$0.01/GPU/hr $0.04/hr total (6×)	Sold Out
Vast.ai	6×NVIDIA GeForce RTX 3080 Ti 12GB VRAM	12GB	8 vCPU 94GB RAM 1660GB Storage	Ukraine	$0.01/GPU/hr $0.04/hr total (6×)	Sold Out
Vast.ai	NVIDIA GeForce RTX 3060 12GB VRAM	12GB	4 vCPU 23GB RAM 670GB Storage	Turkey	$0.01/GPU/hr	Sold Out
Vast.ai	NVIDIA GeForce RTX 2060 6GB VRAM	6GB	12 vCPU 15GB RAM 55GB Storage	Romania	$0.01/GPU/hr	Sold Out

Vast.ai

Quebec

Sold Out

NVIDIA GeForce RTX 30608x

12GB VRAM

24 vCPU

126GB RAM

738GB Storage

625 Mbps ↑

626 Mbps ↓

$0.00/GPU/hr

$0.01/hr total (8×)

Vast.ai

Ukraine

Sold Out

NVIDIA GeForce RTX 3080 Ti6x

12GB VRAM

8 vCPU

94GB RAM

1527GB Storage

$0.01/GPU/hr

$0.04/hr total (6×)

Vast.ai

Ukraine

Sold Out

NVIDIA GeForce RTX 3080 Ti6x

12GB VRAM

8 vCPU

94GB RAM

1660GB Storage

394 Mbps ↑

689 Mbps ↓

$0.01/GPU/hr

$0.04/hr total (6×)

Vast.ai

Turkey

Sold Out

NVIDIA GeForce RTX 3060

12GB VRAM

4 vCPU

23GB RAM

670GB Storage

21 Mbps ↑

99 Mbps ↓

$0.01/GPU/hr

Vast.ai

Romania

Sold Out

NVIDIA GeForce RTX 2060

6GB VRAM

12 vCPU

15GB RAM

55GB Storage

304 Mbps ↑

224 Mbps ↓

$0.01/GPU/hr

View all 74 offers

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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

Vast.ai(Est. 2018)

A decentralized marketplace for absolute lowest costs and distributed experiments.

Best For

Absolute lowest costsDistributed experiments

Unique Features

Granular search filters like DLPerf/$
Decentralized marketplace

Feature Comparison

Access Methods

Feature	Salad	Vast.ai
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	Salad	Vast.ai
Billing Increment	per-second	per-hour
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	Salad	Vast.ai
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	Salad	Vast.ai
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Salad employs per-second billing with spot instances, enabling precise cost control for jobs from seconds to days, ideal for bursty or interruptible workloads. This granularity reduces waste on short runs compared to Vast.ai's per-hour billing, which rounds up usage and favors sustained jobs over 1 hour. Both offer spot instances for ~50-80% discounts versus on-demand, but lack reserved instances, emphasizing marketplace dynamics. Implications: Salad suits variable-length batch jobs (e.g., <1 hour saves 20-50% vs hourly), while Vast.ai benefits long training runs where hourly minimums amortize. No volume discounts noted; spot availability fluctuates with supply.

Value Assessment

For small experiments (<1 hour), Salad's per-second billing delivers superior value, avoiding hourly minimums and leveraging residential scale for cheap A100/H100 equivalents. Vast.ai shines in large training runs (days-long) via DLPerf/$ optimization, potentially 10-20% cheaper on high-end rigs. Production inference favors Salad's fault tolerance for cost-effective scaling. Batch jobs: Salad edges out due to network size. Overall, Salad offers better value for fault-tolerant, massive workloads ($0.05-0.15/GPU-hr spots); Vast.ai for perf-tuned experiments ($0.03-0.12/GPU-hr), but with higher variability risk.

Use Case Comparison

LLM Training

Salad recommended

Salad

Salad excels for massive-scale LLM training via its residential network, supporting fault-tolerant, distributed batch jobs across consumer GPUs. Per-second billing optimizes long runs with preemptions, minimizing costs for 100s of GPUs. Handles interruptions seamlessly, ideal for checkpointed training.

Vast.ai

Vast.ai suits distributed LLM training through marketplace filtering for DLPerf/$, enabling cost-optimized multi-node setups. Per-hour billing works for sustained runs, but less fault-tolerant; good for experimental scales.

Batch Inference

Salad recommended

Salad

Salad is optimized for massive batch inference with fault-tolerant design and low-cost residential GPUs. Per-second spots handle variable queue depths efficiently, scaling to enterprise volumes without premium pricing.

Vast.ai

Vast.ai supports batch inference via cheap marketplace instances, using filters for inference-optimized GPUs. Hourly billing suits steady loads, but spot preemptions may disrupt large batches.

Real-time Inference

Either works

Salad

Salad's fault-tolerant inference focus makes it viable for real-time, with residential scale for low-latency serving. However, consumer GPU variability may impact consistency; per-second aids dynamic scaling.

Vast.ai

Vast.ai offers flexible GPU selection for inference, but decentralized nature introduces latency/jitter risks. Hourly billing less ideal for always-on; better for non-critical real-time.

Fine-tuning & Experimentation

Vast.ai recommended

Salad

Salad works for experimentation with cheap spots, but lacks granular perf/cost filters; suits larger, batch-style fine-tunes tolerant of faults.

Vast.ai

Vast.ai is ideal for fine-tuning experiments, with DLPerf/$ and marketplace search for precise GPU-cost matches. Hourly billing fine for iterative, short runs; enables rapid distributed trials.

Technical Comparison

Infrastructure

Both use decentralized consumer GPU networks—Salad via residential nodes (bare-metal-like, home-hosted), Vast.ai as a P2P marketplace. Networking is host-dependent (1-10Gbps typical), with ephemeral storage; persistent options limited or via user NFS. No native Kubernetes; API-driven instance orchestration. Salad emphasizes fault-tolerant clustering; Vast.ai offers advanced filtering but variable node uptime. Storage: local SSDs (1-4TB), no managed volumes standard.

Performance

Consumer GPUs (RTX 3090/A100) yield 70-90% datacenter perf with variability; Salad's fault tolerance mitigates preemptions for better effective throughput in long jobs. Vast.ai's DLPerf/$ benchmarks aid selection, supporting multi-GPU via NCCL. Scaling: both handle 10s-100s GPUs distributedly, but Salad better for massive parallelism. Availability fluctuates; Salad reports higher residential density, Vast.ai more high-end options. Latency higher than cloud (50-200ms inter-node).

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both Salad and Vast.ai 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?▾

Salad bills per-second, while Vast.ai bills per-hour. Per-second billing from Salad 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?▾

Salad holds GDPR certification. Vast.ai holds GDPR certification. 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?▾

Vast.ai 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, Vast.ai's integrated notebooks provide a smoother experience. Additionally, Vast.ai offers web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

Salad offers native Kubernetes support for container orchestration, while Vast.ai does not. If you're building production ML pipelines with Kubernetes-based tools like Kubeflow, Argo, or KServe, Salad will integrate more seamlessly with your workflow.

What is each provider best suited for?▾

Salad is best suited for Massive batch jobs; Fault-tolerant inference. Vast.ai excels at Absolute lowest costs; Distributed experiments. 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 better enterprise support?▾

Neither provider prominently advertises enterprise support tiers. Contact each provider directly to discuss custom support arrangements for production deployments.

Which provider has better API and automation support?▾

Both Salad and Vast.ai 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 Salad and Vast.ai 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?▾

Salad's standout features include: Lowest pricing via residential node network; Decentralized consumer GPU network. Vast.ai's standout features include: Granular search filters like DLPerf/$; Decentralized marketplace. 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 Salad, visit their website at https://salad.com?utm_source=gpuperhour&utm_medium=referral to create an account and explore available GPU options. For Vast.ai, visit https://cloud.vast.ai/?ref_id=375842&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.