Provider Comparison

Atlantic.net 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. Atlantic.net 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?

Atlantic.net bills per-hour, while Vast.ai bills per-hour. Both providers use the same billing granularity, so this factor won't differentiate your decision.

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

Atlantic.net holds SOC 2, HIPAA, GDPR certifications. Vast.ai holds GDPR certification. For organizations with strict compliance requirements, Atlantic.net offers more comprehensive coverage.

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

Vast.ai offers built-in Jupyter notebook support for interactive development, while Atlantic.net 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?

Neither provider offers native Kubernetes support. You would need to manage your own Kubernetes cluster or use alternative orchestration methods for containerized workloads.

Q: What is each provider best suited for?

Atlantic.net is best suited for Healthcare organizations requiring HIPAA-compliant GPU hosting; Enterprises seeking raw performance of bare metal with long-term stability. 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?

Atlantic.net 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?

Atlantic.net offers dedicated enterprise support options, while Vast.ai may have more limited support tiers. Regarding SLAs: Atlantic.net offers SLA guarantees (100% uptime); Vast.ai has no published SLA.

Q: Which provider has better API and automation support?

Both Atlantic.net 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 Atlantic.net 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.

Atlantic.net and Vast.ai represent contrasting approaches in the GPU cloud market for ML/AI workloads. Atlantic.net, a established IaaS provider, emphasizes enterprise-grade infrastructure with a focus on regulated sectors like healthcare. It delivers bare-metal GPU servers backed by a 100% uptime SLA, SOC 2, HIPAA, and GDPR compliance, ideal for organizations prioritizing data sovereignty, stability, and raw performance without virtualization overhead. However, it lacks managed MLOps tools such as Jupyter notebooks or deployment endpoints, and its rigid per-hour pricing excludes spot instances, limiting flexibility for variable workloads. In contrast, Vast.ai operates as a decentralized marketplace connecting users directly to GPU hosts worldwide, enabling the lowest possible costs through competitive bidding and granular filters like DLPerf per dollar. It supports per-hour and spot instances, suiting cost-sensitive users for experiments or distributed training. GDPR compliance is offered, but it lacks enterprise SLAs, HIPAA, or SOC 2, with potential variability in host reliability and performance. Atlantic.net excels in production environments requiring compliance and predictability, while Vast.ai democratizes access for budget-constrained researchers and hobbyists. Key differentiators include Atlantic's bare-metal stability versus Vast.ai's marketplace-driven savings and flexibility. ML engineers should weigh compliance needs, workload reliability, and cost against these providers' value propositions: long-term enterprise reliability for Atlantic.net, opportunistic low-cost scaling for Vast.ai.

Visit Atlantic.net Visit Vast.ai

Our Recommendation

Choose Atlantic.net for compliance-driven workloads in regulated industries like healthcare (HIPAA), where bare-metal performance, 100% uptime SLA, and enterprise support are critical. It's suited for mid-to-large teams (10+ engineers) running stable, long-duration training or inference on dedicated hardware, with budgets tolerant of premium pricing for reliability over cost savings. Opt for Vast.ai when absolute cost minimization is paramount, such as for individual researchers, startups, or small teams (1-5 engineers) conducting experiments, fine-tuning, or distributed jobs. Its spot instances and DLPerf/$ filters shine for interruptible workloads under tight budgets (<$0.50/GPU-hour), but avoid for production due to potential host variability. Hybrid use—Vast.ai for prototyping, Atlantic.net for deployment—may suit scaling teams balancing cost and compliance.

Live Pricing

Compare real-time GPU offers from Atlantic.net and Vast.ai

54 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 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 5070 12GB VRAM	12GB	20 vCPU 31GB RAM 427GB Storage	Israel	$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

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

Israel

Sold Out

NVIDIA GeForce RTX 5070

12GB VRAM

20 vCPU

31GB RAM

427GB Storage

24 Mbps ↑

20 Mbps ↓

$0.01/GPU/hr

View all 54 offers

Atlantic.net(Est. 1994)

A veteran in the infrastructure-as-a-service market focusing on enterprise-class infrastructure with a pivot into high-performance computing for regulated industries.

Best For

Healthcare organizations requiring HIPAA-compliant GPU hostingEnterprises seeking raw performance of bare metal with long-term stability

Unique Features

100% uptime SLA
Bare-metal delivery model

Limitations

Lack of managed MLOps tools like notebooks and endpoints
Rigid pricing model without spot markets

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	Atlantic.net	Vast.ai
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	Atlantic.net	Vast.ai
Billing Increment	per-hour	per-hour
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	Atlantic.net	Vast.ai
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	Atlantic.net	Vast.ai
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Atlantic.net employs a straightforward per-hour billing model for on-demand bare-metal GPU instances, with no spot markets, reserved instances, or per-second granularity. Pricing is fixed and predictable, often at enterprise premiums (e.g., A100 instances around $2-4/hour depending on config), suiting steady, long-running jobs but penalizing short or intermittent usage. Vast.ai offers per-hour on-demand alongside spot instances via its auction-based marketplace, enabling rates as low as $0.20-0.60/GPU-hour for H100s or A100s through bidding. This flexibility favors bursty patterns but introduces variability and potential interruptions for spots. Implications: Atlantic.net minimizes billing surprises for production; Vast.ai maximizes savings for experiments but requires monitoring for preemptions.

Value Assessment

Vast.ai delivers superior value for small experiments and fine-tuning, where spot instances can slash costs by 50-80% versus on-demand, ideal for budgets under $1k/month and workloads tolerant of interruptions. For large-scale LLM training (multi-day runs), Atlantic.net's stable bare-metal and SLA provide better value, avoiding downtime costs despite higher per-hour rates. Batch inference favors Vast.ai's low entry barriers for occasional high-volume jobs, while real-time inference production leans toward Atlantic.net's reliability. Overall, Vast.ai wins on cost-per-flop for non-critical tasks; Atlantic.net for TCO in compliant, mission-critical scenarios with sustained utilization >70%.

Use Case Comparison

LLM Training

Atlantic.net recommended

Atlantic.net

Atlantic.net suits large-scale LLM training well with bare-metal multi-GPU configs (e.g., 8x H100), delivering consistent high-bandwidth NVLink performance and 100% uptime SLA. HIPAA/SOC 2 compliance supports regulated data training, but lacks auto-scaling or MLOps integrations, requiring custom setups. Predictable per-hour billing aids budgeting for multi-week runs, though higher costs limit experimentation.

Vast.ai

Vast.ai excels for cost-effective distributed LLM training via spot instances across global hosts, with filters for DLPerf/$ optimizing throughput-per-dollar. Supports multi-node scaling but host variability may cause inconsistencies in interconnects or uptime, unsuitable for strict deadlines. Ideal for pre-training phases where savings outweigh minor preemptions.

Batch Inference

Vast.ai recommended

Atlantic.net

Atlantic.net provides reliable bare-metal for high-throughput batch inference, with stable storage and networking for large datasets. Compliance features enable secure processing of sensitive data, and fixed pricing supports scheduled jobs. However, no serverless options mean overprovisioning for sporadic batches, increasing costs without MLOps conveniences.

Vast.ai

Vast.ai offers unbeatable economics for batch inference via cheap spot GPUs, granular searches matching perf/price for models like Llama. Easy scaling across hosts for parallel jobs, but potential variability in disk I/O or network latency requires robust checkpointing. Best for non-urgent, high-volume offline processing.

Real-time Inference

Atlantic.net recommended

Atlantic.net

Atlantic.net is strong for production real-time inference with bare-metal low-latency, dedicated bandwidth, and 100% SLA ensuring availability. Compliance suits API endpoints handling PHI, but manual scaling and no managed services demand DevOps effort for load balancing or autoscaling.

Vast.ai

Vast.ai struggles here due to spot preemptions and host heterogeneity risking latency spikes or downtime. On-demand options exist but at higher effective costs than spots; lacks SLAs for always-on serving. Viable only for dev/testing, not production SLAs.

Fine-tuning & Experimentation

Vast.ai recommended

Atlantic.net

Atlantic.net fits moderately for fine-tuning with powerful bare-metal, but rigid pricing and no notebooks inflate costs for short trials. Enterprise stability aids iterative tuning on compliant data, yet absence of spot markets or easy spin-up hinders rapid experimentation cycles.

Vast.ai

Vast.ai is optimal for fine-tuning experiments, offering instant access to diverse GPUs at spot prices (<$0.30/hour), DLPerf filters for quick perf matching, and easy parallelism. Variability is tolerable for fail-fast iterations; marketplace enables testing edge configs cheaply.

Technical Comparison

Infrastructure

Atlantic.net focuses on bare-metal GPU servers, bypassing virtualization for maximal performance, with options for high-speed NVLink interconnects, NVMe storage, and private networking. No native Kubernetes but supports custom installs; emphasizes enterprise storage like block/volumes with snapshots. Vast.ai's decentralized marketplace aggregates virtualized instances from global hosts, offering flexible configs but variable networking (often consumer-grade) and storage (host-dependent EBS-like). Kubernetes via user setups possible; lacks unified managed infra, prioritizing searchability over homogeneity.

Performance

Atlantic.net delivers consistent, high-performance with bare-metal (e.g., full A100/H100 specs, reliable multi-GPU scaling via NVSwitch), minimal overhead ideal for bandwidth-intensive training. Vast.ai provides good DLPerf at low cost but with variability—host quality differs, potential throttling, weaker inter-node comms (e.g., no guaranteed InfiniBand). GPU availability high via marketplace but peaks cause bidding wars; scales well distributively yet single-host multi-GPU less reliable than Atlantic's dedicated racks.

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. Atlantic.net 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?▾

Atlantic.net bills per-hour, while Vast.ai bills per-hour. Both providers use the same billing granularity, so this factor won't differentiate your decision.

Which provider has better compliance certifications for enterprise use?▾

Atlantic.net holds SOC 2, HIPAA, GDPR certifications. Vast.ai holds GDPR certification. For organizations with strict compliance requirements, Atlantic.net offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

Vast.ai offers built-in Jupyter notebook support for interactive development, while Atlantic.net 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?▾

Neither provider offers native Kubernetes support. You would need to manage your own Kubernetes cluster or use alternative orchestration methods for containerized workloads.

What is each provider best suited for?▾

Atlantic.net is best suited for Healthcare organizations requiring HIPAA-compliant GPU hosting; Enterprises seeking raw performance of bare metal with long-term stability. 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?▾

Atlantic.net 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?▾

Atlantic.net offers dedicated enterprise support options, while Vast.ai may have more limited support tiers. Regarding SLAs: Atlantic.net offers SLA guarantees (100% uptime); Vast.ai has no published SLA.

Which provider has better API and automation support?▾

Both Atlantic.net 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 Atlantic.net 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?▾

Atlantic.net's standout features include: 100% uptime SLA; Bare-metal delivery model. 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 Atlantic.net, visit their website at https://cloud.atlantic.net/r/t3hjjhja?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.