Provider Comparison

Hyperstack vs Vast.ai

Q: Which provider offers spot instances for cost savings?

Vast.ai 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. Hyperstack 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, Vast.ai would be the better choice.

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

Hyperstack bills per-minute, while Vast.ai bills per-hour. Consider your typical workload duration when evaluating which billing model offers better value for your use case.

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

Hyperstack holds GDPR, ISO 27001 certifications. Vast.ai holds GDPR certification. For organizations with strict compliance requirements, Hyperstack offers more comprehensive coverage.

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

Both Hyperstack and Vast.ai 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, Vast.ai offers web-based terminal access for quick debugging.

Q: Which provider has better Kubernetes support for orchestration?

Hyperstack 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, Hyperstack will integrate more seamlessly with your workflow.

Q: What is each provider best suited for?

Hyperstack is best suited for European enterprises requiring GDPR compliance; Sustainable computing initiatives. 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 reserved instances for long-term savings?

Hyperstack offers reserved instance pricing for long-term commitments, while Vast.ai 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.

Q: Which provider offers better enterprise support?

Hyperstack offers dedicated enterprise support options, while Vast.ai may have more limited support tiers.

Q: Which provider has better API and automation support?

Both Hyperstack 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?

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

Hyperstack and Vast.ai represent contrasting approaches in the GPU cloud market for ML/AI workloads. Hyperstack positions itself as an enterprise-grade provider emphasizing sustainability with 100% renewable energy, targeting European enterprises needing GDPR and ISO 27001 compliance. Its AI Studio supports streamlined generative AI workflows, and per-minute billing suits variable workloads. This makes it ideal for organizations prioritizing reliability, data sovereignty, and environmental responsibility. In contrast, Vast.ai operates as a decentralized marketplace aggregating peer-hosted GPUs worldwide, focusing on absolute lowest costs through competitive bidding and spot instances. Granular filters like DLPerf/$ enable precise selection for cost-performance optimization, appealing to individual researchers, startups, and teams running distributed experiments. Per-hour billing with spot options minimizes expenses for interruptible jobs. Key differentiators include Hyperstack's centralized, compliant infrastructure versus Vast.ai's variable, cost-driven peer network. Hyperstack offers predictable enterprise support but at higher base rates, while Vast.ai delivers unmatched price points with potential reliability trade-offs. For ML engineers, Hyperstack suits production-grade deployments in regulated environments, whereas Vast.ai excels in prototyping and budget-constrained scaling, though users must manage instance variability.

Visit Hyperstack Visit Vast.ai

Our Recommendation

Choose Hyperstack for enterprise teams (10+ members) in Europe requiring GDPR/ISO 27001 compliance, sustainability reporting, or seamless gen AI workflows via AI Studio. It's optimal for production workloads with strict SLAs, moderate budgets allowing premium pricing, and needs like multi-GPU bare metal stability. Opt for Vast.ai with small teams or solo practitioners prioritizing costs under $0.10/GPU-hour, spot instances for non-critical jobs, or distributed experiments across heterogeneous hardware. Vast.ai fits tight budgets (<$1K/month) and flexible scaling but demands tolerance for potential downtime or variable performance. For hybrid needs, start with Vast.ai for prototyping and migrate to Hyperstack for production.

Live Pricing

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

83 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
QuantaCloud Partner	A100 · H100 / H200 · B200 / B300 32–1024+ GPUs · InfiniBand	∞	Custom configs	Multiple DCs	Reserved / cluster Get a quote in 24h	Available
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 1660GB 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 1527GB 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 2080 Ti 11GB VRAM	11GB	16 vCPU 31GB RAM 1549GB Storage	Georgia	$0.01/GPU/hr	Sold Out

QuantaCloud

Partner

Available

A100 · H100 / H200 · B200 / B300

32–1024+ GPUs · InfiniBand

Reserved / cluster

Get a quote in 24h

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

1660GB Storage

394 Mbps ↑

689 Mbps ↓

$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

1527GB Storage

$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

Georgia

Sold Out

NVIDIA GeForce RTX 2080 Ti

11GB VRAM

16 vCPU

31GB RAM

1549GB Storage

722 Mbps ↑

388 Mbps ↓

$0.01/GPU/hr

View all 83 offers

QuantaCloud

Comparing providers? We broker across all of them.

Stop tab-switching between pricing pages. Tell us what you need — 16+ GPUs, reserved or cluster capacity — and we return one quote at partner rates within 24 hours.

No waitlist24hr quote turnaroundInfiniBand fabric

Hyperstack(Est. 2021)

A provider focused on sustainable, enterprise-grade GPU acceleration using 100% renewable energy.

Best For

European enterprises requiring GDPR complianceSustainable computing initiatives

Unique Features

100% renewable energy
AI Studio for generative AI workflows

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	Hyperstack	Vast.ai
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	Hyperstack	Vast.ai
Billing Increment	per-minute	per-hour
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	Hyperstack	Vast.ai
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	Hyperstack	Vast.ai
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Hyperstack employs per-minute billing for on-demand instances, enabling fine-grained cost control for short bursts or intermittent workloads without hourly minimums. This contrasts with Vast.ai's per-hour billing, which includes on-demand and spot instances via auction-style marketplace pricing. Vast.ai's spots can drop costs by 50-90% for interruptible jobs but risk preemption. Hyperstack lacks spot options but offers predictable enterprise rates. Implications: Hyperstack favors quick experiments (e.g., 10-min tests cost proportionally less) or steady usage; Vast.ai suits long-running training where spots yield savings, though per-hour granularity may waste budget on sub-hour jobs. No reserved instances noted for either, emphasizing flexibility over commitments.

Value Assessment

Vast.ai provides superior value for small experiments and fine-tuning, where DLPerf/$ filters uncover GPUs at $0.05-0.20/hour, outpacing Hyperstack's likely $0.50+/hour enterprise rates. For large LLM training runs (100+ GPU-hours), Vast.ai's spots maximize savings on durable jobs. Hyperstack delivers better value for production inference needing reliability and compliance, avoiding Vast.ai's variability costs (e.g., failed checkpoints). Batch inference leans Vast.ai for cost if preemptible; real-time favors Hyperstack's consistent uptime. Overall, Vast.ai wins on raw cost for <10-hour jobs; Hyperstack for sustained, compliant usage exceeding 100 hours.

Use Case Comparison

LLM Training

Vast.ai recommended

Hyperstack

Hyperstack supports reliable multi-GPU scaling in sustainable data centers, ideal for long training runs with AI Studio integration for workflows. Enterprise compliance ensures data security, but higher per-minute rates suit budgeted enterprises over cost-sensitive users. Predictable availability minimizes interruptions, though lacks spot discounts for massive scales.

Vast.ai

Vast.ai excels via spot instances and DLPerf/$ filters for cost-optimized clusters, enabling distributed training across cheap peer GPUs. Best for large-scale, interruptible jobs, but variability in hardware and preemption risks require robust checkpointing. Unmatched for budget-driven experiments.

Batch Inference

Vast.ai recommended

Hyperstack

Hyperstack's per-minute billing and AI Studio streamline batch jobs with consistent performance and GDPR compliance. Suits enterprises processing regulated data volumes reliably, with renewable energy appealing for ESG goals. Lacks granular cost filters.

Vast.ai

Vast.ai offers lowest-cost spots for high-throughput batches, with filters targeting inference-optimized GPUs. Decentralized nature allows scaling via cheap instances, though quality varies; ideal for non-urgent, cost-focused workloads with tolerance for retries.

Real-time Inference

Hyperstack recommended

Hyperstack

Hyperstack provides stable, low-latency inference via enterprise-grade infrastructure and AI Studio, with ISO 27001 ensuring uptime for production APIs. Per-minute billing fits variable traffic; compliance critical for customer-facing services.

Vast.ai

Vast.ai supports inference via on-demand rentals but decentralized hosting introduces latency variability and eviction risks, unsuitable for strict SLAs. Filters help select performant nodes, yet lacks enterprise guarantees.

Fine-tuning & Experimentation

Vast.ai recommended

Hyperstack

Hyperstack's per-minute model and AI Studio enable rapid prototyping of gen AI models with compliant, sustainable access. Reliable for iterative EU-based teams, though premium pricing limits high-volume trials.

Vast.ai

Vast.ai dominates with ultra-low spot costs and granular filters for quick, cheap experiments across diverse GPUs. Perfect for parallel fine-tunes, despite potential instability; marketplace dynamism aids discovery.

Technical Comparison

Infrastructure

Hyperstack delivers centralized, enterprise-grade bare metal GPUs in renewable-powered EU data centers, supporting Kubernetes and high-speed networking for compliant workloads. Vast.ai's decentralized marketplace aggregates peer-hosted instances globally, offering virtualized access with variable storage/networking. Hyperstack emphasizes GDPR/ISO consistency; Vast.ai provides flexible Kubernetes via host selection but with heterogeneous configs and no unified storage.

Performance

Hyperstack offers consistent GPU availability (e.g., A100/H100 clusters) and seamless multi-GPU NVLink scaling for enterprises. Vast.ai boasts high availability via 10k+ hosts but variable DLPerf due to consumer-grade hardware; excels in cost-perflop but multi-GPU scaling depends on host matching. Limited benchmarks show Vast.ai 20-50% cheaper perf, though Hyperstack edges reliability; both support InfiniBand where available.

Frequently Asked Questions

Which provider offers spot instances for cost savings?▾

Vast.ai 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. Hyperstack 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, Vast.ai would be the better choice.

What is the minimum billing increment for each provider?▾

Hyperstack bills per-minute, while Vast.ai bills per-hour. Consider your typical workload duration when evaluating which billing model offers better value for your use case.

Which provider has better compliance certifications for enterprise use?▾

Hyperstack holds GDPR, ISO 27001 certifications. Vast.ai holds GDPR certification. For organizations with strict compliance requirements, Hyperstack offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

Both Hyperstack and Vast.ai 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, Vast.ai offers web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

Hyperstack 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, Hyperstack will integrate more seamlessly with your workflow.

What is each provider best suited for?▾

Hyperstack is best suited for European enterprises requiring GDPR compliance; Sustainable computing initiatives. 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 reserved instances for long-term savings?▾

Hyperstack offers reserved instance pricing for long-term commitments, while Vast.ai 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?▾

Hyperstack offers dedicated enterprise support options, while Vast.ai may have more limited support tiers.

Which provider has better API and automation support?▾

Both Hyperstack 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?▾

Vast.ai offers native container support for running Docker images, while Hyperstack 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?▾

Hyperstack's standout features include: 100% renewable energy; AI Studio for generative AI workflows. 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 Hyperstack, visit their website at https://www.hyperstack.cloud?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.