Provider Comparison

FluidStack vs Nebius

Q: Which provider offers better spot instance pricing?

Both FluidStack and Nebius 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?

FluidStack bills per-minute, while Nebius bills per-second. Per-second billing from Nebius 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?

FluidStack holds SOC 2, ISO 27001 certifications. Nebius holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. For organizations with strict compliance requirements, Nebius offers more comprehensive coverage.

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

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

Q: Which provider has better Kubernetes support for orchestration?

Both FluidStack and Nebius 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.

Q: 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. Nebius excels at Enterprises needing EU/US compliance and managed K8s. 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?

Both FluidStack and Nebius offer reserved instance pricing for committed usage, typically providing 20-40% discounts compared to on-demand rates. 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?

Both FluidStack and Nebius offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: FluidStack has no published SLA; Nebius offers SLA guarantees.

Q: Which provider has better API and automation support?

FluidStack provides a comprehensive API for programmatic control, while Nebius may require more manual management. If automation is a priority, FluidStack's API support will streamline your infrastructure-as-code workflows.

Q: Which provider has better container and Docker support?

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

FluidStack and Nebius represent distinct approaches in the GPU cloud market for AI/ML workloads. FluidStack operates as a supercloud aggregator, unifying access to vast GPU resources across global data centers, including Tier 1-4 facilities. This model excels in delivering massive, on-demand capacity for large-scale training, leveraging spare capacity for cost efficiency and broad geographic reach. However, resource consistency can vary due to its multi-provider nature. Ideal for teams needing immediate scalability without long-term commitments, it offers per-minute billing with spot instances and SOC 2/ISO 27001 compliance. Nebius, conversely, is an AI-focused infrastructure provider emphasizing managed services, particularly Kubernetes (K8s) orchestration for EU/US-compliant workloads. As a public company, it provides transparency and prioritizes enterprise-grade features like HIPAA and GDPR compliance alongside SOC 2/ISO 27001. Its per-second billing and spot instances cater to flexible usage, with a startup-like agility tailored to AI innovation. Nebius suits organizations requiring reliable, managed environments over raw scale. Key differentiators include FluidStack's aggregation for hyper-scalability versus Nebius's managed compliance ecosystem. FluidStack offers superior global capacity for bursty, high-volume jobs, while Nebius provides better operational simplicity and regulatory adherence. Value propositions hinge on priorities: FluidStack for cost-optimized scale, Nebius for production-ready, compliant deployments. Both enable spot pricing to reduce costs, but choice depends on workload predictability, compliance needs, and team expertise in orchestration.

Visit FluidStack Visit Nebius

Our Recommendation

Choose FluidStack for large-scale, bursty workloads like multi-node LLM training where immediate access to thousands of GPUs across regions is critical, especially for mid-sized teams (10-50 engineers) with DevOps expertise to handle variable consistency. It's ideal for budgets focused on spot instances minimizing costs for non-critical runs, but avoid if strict compliance (e.g., HIPAA) or managed K8s is required. Opt for Nebius when enterprise compliance (GDPR, HIPAA) and managed Kubernetes are non-negotiable, suiting larger teams (50+ engineers) deploying production inference or fine-tuning pipelines. Its per-second billing favors variable-duration jobs, and public status ensures transparency for regulated industries. Budget-conscious users benefit from spot options, but it may lag in raw global scale for extreme training bursts. For hybrid needs, evaluate via trials focusing on latency and uptime SLAs.

Live Pricing

Compare real-time GPU offers from FluidStack and Nebius

8 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
FluidStack	8×NVIDIA A100 SXM4 80GB 80GB VRAM	80GB	0 vCPU 0GB RAM	🌍Global	$1.30/GPU/hr $10.40/hr total (8×)
Nebius	NVIDIA L40S 48GB VRAM	48GB	8 vCPU 32GB RAM	🌍Europe	$1.55/GPU/hr
Nebius	NVIDIA L40S 48GB VRAM	48GB	16 vCPU 96GB RAM	🌍Europe	$1.82/GPU/hr
FluidStack	8×NVIDIA H100 SXM5 80GB VRAM	80GB	0 vCPU 0GB RAM	🌍Global	$2.10/GPU/hr $16.80/hr total (8×)
Nebius	NVIDIA H100 SXM5 80GB VRAM	80GB	16 vCPU 200GB RAM	🌍Europe	$2.15/GPU/hr

QuantaCloud

Partner

Available

A100 · H100 / H200 · B200 / B300

32–1024+ GPUs · InfiniBand

Reserved / cluster

Get a quote in 24h

FluidStack

🌍Global

NVIDIA A100 SXM4 80GB8x

80GB VRAM

0 vCPU

0GB RAM

$1.30/GPU/hr

$10.40/hr total (8×)

Nebius

🌍Europe

NVIDIA L40S

48GB VRAM

8 vCPU

32GB RAM

$1.55/GPU/hr

Nebius

🌍Europe

NVIDIA L40S

48GB VRAM

16 vCPU

96GB RAM

$1.82/GPU/hr

FluidStack

🌍Global

NVIDIA H100 SXM58x

80GB VRAM

0 vCPU

0GB RAM

$2.10/GPU/hr

$16.80/hr total (8×)

Nebius

🌍Europe

NVIDIA H100 SXM5

80GB VRAM

16 vCPU

200GB RAM

$2.15/GPU/hr

View all 8 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

Nebius(Est. 2023)

An AI-centric infrastructure company providing managed services for EU/US compliant workloads.

Best For

Enterprises needing EU/US compliance and managed K8s

Unique Features

Public company with transparency
Startup-like focus on AI

Feature Comparison

Access Methods

Feature	FluidStack	Nebius
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	FluidStack	Nebius
Billing Increment	per-minute	per-second
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	FluidStack	Nebius
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	FluidStack	Nebius
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

FluidStack employs per-minute billing with spot and on-demand instances, enabling fine-grained cost control for workloads lasting minutes to days. This suits intermittent or short bursts but incurs higher overhead for sub-minute tasks. Spot instances aggregate spare capacity globally, offering deep discounts (up to 70-80% off on-demand) at the risk of interruptions. No reserved instances are highlighted, emphasizing flexibility over long-term locks. Nebius uses per-second billing, more granular than FluidStack's per-minute, ideal for micro-experiments or variable inference loads, reducing waste on idle seconds. Spot instances are available similarly, with comparable discounts, but tied to its managed infrastructure. Both lack explicit reserved options in provided details, focusing on pay-as-you-go. Implications: Nebius minimizes costs for fine-grained usage (e.g., <1min jobs), while FluidStack favors sustained runs; spot viability depends on workload fault-tolerance.

Value Assessment

For small experiments and fine-tuning, Nebius delivers superior value via per-second billing, avoiding FluidStack's per-minute minimums, especially with managed K8s reducing ops overhead—ideal for solo devs or small teams under $10k/month spend. Large training runs favor FluidStack's aggregated scale and spot depth, yielding 20-30% better effective pricing for 100+ GPU clusters due to global spare capacity access, despite consistency risks. Production inference benefits Nebius for reliable, compliant uptime with granular billing suiting variable traffic; FluidStack suits high-throughput batch inference if latency tolerance allows spot interruptions. Overall, FluidStack edges cost for scale-heavy, interruptible jobs; Nebius for predictable, compliance-bound production.

Use Case Comparison

LLM Training

FluidStack recommended

FluidStack

FluidStack excels with supercloud aggregation enabling instant access to massive GPU clusters (thousands of units) across global DCs, perfect for multi-day, multi-node pretraining. Spot instances slash costs for fault-tolerant jobs, though consistency varies by facility, requiring robust checkpointing. Best for scale prioritizing capacity over uniformity.

Nebius

Nebius supports training via managed K8s with reliable EU/US clusters, but scale may be constrained compared to aggregators. Compliance aids enterprise runs; per-second billing optimizes variable phases, yet lacks FluidStack's raw global burst capacity for extreme LLM scales.

Batch Inference

Either works

FluidStack

FluidStack's spot-heavy model and global reach suit high-volume, interruptible batch jobs, pooling spare capacity for cost-effective scaling. Per-minute billing aligns with bulk processing; variability manageable with queuing, ideal for non-real-time analytics.

Nebius

Nebius offers managed K8s for streamlined batch orchestration, with per-second precision reducing costs for uneven workloads. Strong compliance supports regulated data processing; consistent performance aids reliability over FluidStack's potential variability.

Real-time Inference

Nebius recommended

FluidStack

FluidStack provides on-demand GPUs for low-latency serving, but aggregator variability risks inconsistent networking/slates, challenging for strict SLAs. Global DCs aid multi-region deployment; spot unsuitable due to interruptions.

Nebius

Nebius shines with managed services ensuring stable, compliant inference endpoints via K8s autoscaling. Per-second billing fits fluctuating traffic; HIPAA/GDPR support critical for production APIs, offering predictable performance.

Fine-tuning & Experimentation

Nebius recommended

FluidStack

FluidStack's vast spot capacity enables rapid prototyping on diverse GPUs, with per-minute billing economical for short runs. Global access tests regional effects; ops teams handle variability for iterative experiments.

Nebius

Nebius's granular per-second billing and managed K8s minimize costs/waste for frequent, small-scale tuning. Compliance and transparency suit collaborative teams; easier onboarding for non-experts versus FluidStack's aggregation complexities.

Technical Comparison

Infrastructure

FluidStack's supercloud aggregates bare-metal and virtualized GPUs from Tier 1-4 DCs worldwide, offering unified APIs but exposing variability in networking (e.g., 100Gbps+ interconnects unevenly) and storage (NFS/Object per facility). No native managed K8s; users deploy custom clusters. Nebius provides fully managed K8s on dedicated AI infra, with consistent high-speed networking (400Gbps RoCE), NVMe storage, and EU/US regions. FluidStack prioritizes scale over uniformity; Nebius emphasizes managed reliability.

Performance

FluidStack boasts superior GPU availability for massive scaling (e.g., 10k+ H100s on-demand), excelling in multi-node training via aggregation, but inter-DC latency/jitter impacts tightly coupled jobs. Nebius offers predictable single-cluster performance with optimized multi-GPU (NVLink) scaling, managed for low variance; availability solid but less bursty globally. Both support spot for cost; FluidStack edges raw throughput for large runs, Nebius for consistent inference/training latency—test via benchmarks for specifics.

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both FluidStack and Nebius 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 Nebius bills per-second. Per-second billing from Nebius 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?▾

FluidStack holds SOC 2, ISO 27001 certifications. Nebius holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. For organizations with strict compliance requirements, Nebius offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

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

Which provider has better Kubernetes support for orchestration?▾

Both FluidStack and Nebius 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?▾

FluidStack is best suited for Large-scale training runs requiring massive, immediate capacity; Global reach for GPU resources. Nebius excels at Enterprises needing EU/US compliance and managed K8s. 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?▾

Both FluidStack and Nebius offer reserved instance pricing for committed usage, typically providing 20-40% discounts compared to on-demand rates. 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?▾

Both FluidStack and Nebius offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: FluidStack has no published SLA; Nebius offers SLA guarantees.

Which provider has better API and automation support?▾

FluidStack provides a comprehensive API for programmatic control, while Nebius may require more manual management. If automation is a priority, FluidStack's API support will streamline your infrastructure-as-code workflows.

Which provider has better container and Docker support?▾

FluidStack offers native container support for running Docker images, while Nebius 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?▾

FluidStack's standout features include: Supercloud architecture pooling global resources; Aggregation of spare capacity from Tier 1-4 data centers. Nebius's standout features include: Public company with transparency; Startup-like focus on AI. 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 Nebius, visit https://nebius.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.