Provider Comparison

FluidStack vs Vast.ai

FluidStack and Vast.ai represent two distinct approaches in the GPU cloud market for ML/AI workloads. FluidStack operates as a supercloud aggregator, unifying access to GPU resources across global data centers (Tier 1-4), offering massive scale and immediate capacity for large-scale training. It's ideal for enterprises needing reliable, high-volume compute with features like per-minute billing and spot instances, backed by SOC 2 and ISO 27001 compliance. However, resource consistency can vary by underlying facility. Vast.ai, conversely, is a decentralized marketplace connecting users directly to individual hosts, prioritizing absolute lowest costs and flexibility for distributed experiments. Its granular search filters, such as DLPerf/$, enable cost-optimized rentals with per-hour billing and spot options, compliant with GDPR. This model suits budget-conscious users but may introduce variability in host quality and uptime. Key differentiators include FluidStack's centralized management for seamless scaling versus Vast.ai's peer-to-peer efficiency for opportunistic pricing. FluidStack appeals to production teams requiring global reach and predictability, while Vast.ai targets researchers and startups optimizing for cost in exploratory work. Overall, FluidStack delivers enterprise-grade reliability at a premium, while Vast.ai maximizes affordability through marketplace dynamics, making the choice dependent on scale, budget, and risk tolerance for ML engineers evaluating GPU providers.

Visit FluidStack Visit Vast.ai

Our Recommendation

Choose FluidStack for large teams (10+ engineers) running production-scale LLM training or inference needing 100+ GPUs with low-latency global access and strong compliance (SOC 2/ISO 27001). It's suited for budgets over $10K/month where consistency trumps cost, especially with per-minute billing minimizing waste on variable workloads. Opt for Vast.ai when prioritizing rock-bottom prices for small-to-medium teams (1-5 engineers) focused on fine-tuning, experiments, or distributed tasks under $1K/month. Its marketplace excels for intermittent use with DLPerf/$ filtering, but avoid if uptime SLAs are critical due to host variability. Hybrid approach: Use Vast.ai for prototyping, FluidStack for scaling to production. Technical requirements like Kubernetes support or NVLink multi-GPU favor FluidStack; pure cost arbitrage suits Vast.ai.

Live Pricing

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

53 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
Q QuantaCloud Partner	A100 · H100 / H200 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 3060 Ti 8GB VRAM	8GB	12 vCPU 15GB RAM 84GB Storage	Mexico	$0.01/GPU/hr	Sold Out

QuantaCloud

Partner

Available

A100 · H100 / H200

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

Mexico

Sold Out

NVIDIA GeForce RTX 3060 Ti

8GB VRAM

12 vCPU

15GB RAM

84GB Storage

245 Mbps ↑

250 Mbps ↓

$0.01/GPU/hr

View all 53 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

FluidStack(Est. 2017)

A supercloud aggregator providing a unified interface to vast GPU resources from global data centers.

Best For

Large-scale training runs requiring massive, immediate capacityGlobal reach for GPU resources

Unique Features

Supercloud architecture pooling global resources
Aggregation of spare capacity from Tier 1-4 data centers

Limitations

Consistency may vary depending on underlying facility

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

Billing Options

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

Compliance

Certification	FluidStack	Vast.ai
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	FluidStack	Vast.ai
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

FluidStack employs per-minute billing with spot and on-demand options, enabling precise cost control for bursty workloads—ideal for short training jobs or interruptions, as users pay only for active minutes. No reserved instances mentioned, but aggregation allows dynamic scaling. Vast.ai uses per-hour billing (often 1-4¢/hr for A100s) with spot auctions, favoring longer runs but risking overpayment for sub-hour tasks due to minimums. Both offer spots for 50-80% savings vs on-demand, but FluidStack's granularity suits variable ML pipelines, while Vast.ai's marketplace yields ultra-low bids via competition. Implications: FluidStack better for unpredictable durations (e.g., hyperparameter sweeps); Vast.ai for steady, cost-sensitive rentals exceeding hours, though bid volatility requires monitoring.

Value Assessment

For small experiments (<1 GPU-hour), Vast.ai dominates with marketplace lows (e.g., $0.20-0.50/A100-hr spot), offering superior value via DLPerf/$ metrics. Large training runs (100+ GPUs) favor FluidStack's aggregated scale and per-minute precision, preventing idle-hour waste despite higher base rates ($0.50-1.50/hr). Production inference benefits FluidStack's reliability over Vast.ai's potential downtime. Overall, Vast.ai wins on raw $/FLOP for hobbyists/prototyping (up to 70% cheaper), but FluidStack provides better TCO for enterprise via uptime and global capacity, especially workloads >24hrs where per-hour gaps narrow with spots.

Use Case Comparison

LLM Training

FluidStack recommended

FluidStack

FluidStack excels for massive-scale LLM training with supercloud aggregation enabling instant 100s-1000s of GPUs across global DCs. Unified interface simplifies multi-node orchestration, per-minute billing optimizes long runs, and Tier 1-4 pooling ensures capacity during peaks. Drawback: facility variability may affect interconnect speeds.

Vast.ai

Vast.ai supports distributed training via marketplace but struggles with coordinating large clusters due to host heterogeneity. Lowest costs appeal for cost-sensitive runs, with DLPerf/$ aiding selection, yet reliability risks (downtime, mismatched GPUs) hinder tight timelines.

Batch Inference

Either works

FluidStack

FluidStack suits batch jobs with scalable GPU pools and spot instances for cost efficiency. Global reach minimizes latency for distributed batches; per-minute billing fits variable queue depths. Consistency issues possible in lower-tier DCs.

Vast.ai

Vast.ai offers cheapest spots for offline batches, granular filters optimize perf/$. Decentralized nature allows easy scaling via multiple cheap hosts, though aggregation overhead and variable uptime require robust fault tolerance.

Real-time Inference

FluidStack recommended

FluidStack

FluidStack's unified supercloud provides reliable low-latency access with strong compliance for production serving. Global DCs ensure regional coverage; however, underlying facility variances could impact p99 latencies without dedicated SLAs.

Vast.ai

Vast.ai's marketplace is suboptimal for real-time due to host unreliability and setup overhead. Cheap rates tempt, but lacks guarantees on uptime/networking, risking SLA violations in latency-sensitive apps.

Fine-tuning & Experimentation

Vast.ai recommended

FluidStack

FluidStack works for experiments with spot access and scale, but higher costs and less granular perf metrics make it overkill for small runs. Global pool aids quick starts.

Vast.ai

Vast.ai shines here with ultra-low $/perf via DLPerf/$ search, perfect for rapid iterations on single/multi-GPU setups. Decentralized flexibility suits parallel experiments across hosts.

Technical Comparison

Infrastructure

FluidStack's supercloud aggregates bare-metal and virtualized GPUs from diverse DCs (Tier 1-4), offering unified APIs, high-bandwidth networking (up to 400Gbps in top facilities), block storage, and Kubernetes compatibility via orchestration layers. Vast.ai is fully decentralized, renting from individual hosts' bare-metal GPUs with user-managed networking/storage; supports Docker/K8s but requires manual host vetting. FluidStack eases ops; Vast.ai demands more DevOps for multi-host setups.

Performance

FluidStack delivers consistent multi-GPU scaling (NVLink/InfiniBand in premium DCs) with high availability, though perf varies by facility—strong for DGX-like clusters. Vast.ai offers variable perf (DLPerf scores help filter), excellent single-host value but scaling limited by host matching; outages common (10-20%). FluidStack better for synchronized training; Vast.ai for embarrassingly parallel tasks. GPU availability: FluidStack's pool ensures supply; Vast.ai peaks at demand-driven shortages.

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both FluidStack 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?▾

FluidStack 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?▾

FluidStack holds SOC 2, ISO 27001 certifications. Vast.ai holds GDPR certification. For organizations with strict compliance requirements, FluidStack 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 FluidStack 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?▾

FluidStack 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, FluidStack will integrate more seamlessly with your workflow.

What is each provider best suited for?▾

FluidStack is best suited for Large-scale training runs requiring massive, immediate capacity; Global reach for GPU resources. 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?▾

FluidStack 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?▾

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

Which provider has better API and automation support?▾

Both FluidStack 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 FluidStack 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?▾

FluidStack's standout features include: Supercloud architecture pooling global resources; Aggregation of spare capacity from Tier 1-4 data centers. 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 FluidStack, visit their website at https://www.fluidstack.io?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.