Provider Comparison

AWS vs FluidStack

Q: Which provider offers better spot instance pricing?

Both AWS and FluidStack 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?

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

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

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

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

Q: Which provider has better Kubernetes support for orchestration?

Both AWS and FluidStack 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?

AWS is best suited for Large-scale enterprises requiring deep integration with other cloud services; Organizations needing globally redundant availability zones. FluidStack excels at Large-scale training runs requiring massive, immediate capacity; Global reach for GPU resources. 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 AWS and FluidStack 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 AWS and FluidStack offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); FluidStack has no published SLA.

Q: Which provider has better API and automation support?

Both AWS and FluidStack 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 AWS and FluidStack 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.

AWS and FluidStack represent contrasting approaches in GPU cloud provisioning for ML/AI workloads. AWS, the market leader, offers deeply integrated GPU instances like A100s in p4d/p5 instances, proprietary Trainium/Inferentia chips, and SageMaker for end-to-end ML pipelines. It excels in enterprise environments with global availability zones, seamless integration with S3, Lambda, and other services, and robust compliance (SOC 2, HIPAA, GDPR, ISO 27001). However, its pricing complexity, including data egress fees, and higher costs make it less ideal for cost-sensitive bursty workloads. FluidStack, a supercloud aggregator, unifies access to GPUs across global Tier 1-4 data centers, pooling spare capacity for massive scale. It suits large training runs needing immediate, vast GPU clusters via a single API, with per-minute billing and spot instances. Unique in aggregating heterogeneous resources, it provides flexibility but may face consistency issues in networking or hardware across facilities. Compliance is solid (SOC 2, ISO 27001) but narrower than AWS. AWS targets established enterprises prioritizing reliability, managed services, and ecosystem lock-in, delivering predictable performance for production. FluidStack appeals to scale-focused teams requiring on-demand global capacity at potentially lower costs, ideal for hyperscale training without long-term commitments. Value hinges on workload: AWS for integrated, compliant ops; FluidStack for raw GPU volume and agility. Both offer spot instances, but AWS's per-second billing edges short jobs, while FluidStack's aggregation shines for explosive demand.

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Our Recommendation

Choose AWS for enterprise-scale deployments needing tight integration with existing cloud services, managed ML tools like SageMaker, or strict compliance (e.g., HIPAA). Ideal for teams of 10+ engineers managing production inference or hybrid workflows, where budgets accommodate premium pricing (~$3-32/hr for A100 instances) and spot interruptions are tolerable via checkpoints. Its global AZs ensure <1% downtime SLAs. Opt for FluidStack when prioritizing cost-effective massive GPU clusters for training (e.g., 1000+ GPUs on-demand), especially for smaller agile teams (1-10) or startups with bursty needs. Suited to budgets under $2/hr equivalent for high-end GPUs via spot markets, but verify consistency for latency-sensitive apps. FluidStack favors technical setups valuing API simplicity over deep ecosystem ties; avoid if uniform infra or advanced storage like EFS is critical.

Live Pricing

Compare real-time GPU offers from AWS and FluidStack

26 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price
AWS	NVIDIA Tesla T4 16GB VRAM	16GB	4 vCPU 16GB RAM	Virginia	$0.53/GPU/hr
AWS	NVIDIA Tesla T4 16GB VRAM	16GB	8 vCPU 32GB RAM	Virginia	$0.75/GPU/hr
AWS	4×NVIDIA Tesla T4 16GB VRAM	16GB	48 vCPU 192GB RAM	Virginia	$0.98/GPU/hr $3.91/hr total (4×)
AWS	NVIDIA RTX A6000 48GB VRAM	48GB	4 vCPU 16GB RAM	Virginia	$1.01/GPU/hr
AWS	NVIDIA Tesla T4 16GB VRAM	16GB	16 vCPU 64GB RAM	Virginia	$1.20/GPU/hr

AWS

Virginia

NVIDIA Tesla T4

16GB VRAM

4 vCPU

16GB RAM

$0.53/GPU/hr

AWS

Virginia

NVIDIA Tesla T4

16GB VRAM

8 vCPU

32GB RAM

$0.75/GPU/hr

AWS

Virginia

NVIDIA Tesla T44x

16GB VRAM

48 vCPU

192GB RAM

$0.98/GPU/hr

$3.91/hr total (4×)

AWS

Virginia

NVIDIA RTX A6000

48GB VRAM

4 vCPU

16GB RAM

$1.01/GPU/hr

AWS

Virginia

NVIDIA Tesla T4

16GB VRAM

16 vCPU

64GB RAM

$1.20/GPU/hr

View all 26 offers

AWS(Est. 2006)

The dominant force in global cloud computing with deep integration of GPUs into its ecosystem for machine learning and other services.

Best For

Large-scale enterprises requiring deep integration with other cloud servicesOrganizations needing globally redundant availability zones

Unique Features

Proprietary silicon like Trainium and Inferentia chips
Fully managed ML development environment with SageMaker

Limitations

High cost relative to specialized clouds
Complexity of pricing including egress fees

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

Feature Comparison

Access Methods

Feature	AWS	FluidStack
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

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

Compliance

Certification	AWS	FluidStack
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	AWS	FluidStack
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

AWS employs per-second billing for most GPU instances (e.g., g5.xlarge with A10G at ~$1.21/hr on-demand), enabling precise costs for variable workloads, with spot instances offering 50-90% discounts but risking interruptions. Reserved instances (1-3 years) yield up to 72% savings for predictable use, though egress fees ($0.09/GB) add complexity. No per-minute granularity. FluidStack uses per-minute billing, slightly less granular than AWS, with spot instances tapping aggregated spare capacity for deep discounts (often 70-90% off on-demand). On-demand rates vary by GPU/DC (e.g., A100 ~$1.50-2.50/hr), lacking long-term reservations but supporting flexible commitments. Implications: AWS favors micro-bursts or experiments (save ~20% on <1hr jobs); FluidStack suits longer runs where minute rounding minimally impacts, but spot volatility suits checkpointed training over steady inference.

Value Assessment

For small experiments (<1hr), AWS delivers superior value via per-second billing and SageMaker Studio's free tier, minimizing waste on failed runs. Large training runs (days+) favor FluidStack's spot aggregation, potentially 40-60% cheaper for 100s of GPUs by accessing global spares unavailable on AWS waitlists. Production inference tilts to AWS: consistent on-demand pricing with Savings Plans beats FluidStack's variable DC costs, plus integrated scaling via ECS/EKS. FluidStack edges batch inference for cost if latency-tolerant, leveraging minute billing for irregular jobs. Overall, AWS offers better value for predictable, integrated workloads (ROI via productivity); FluidStack for hyperscale bursts where raw GPU-hours/hr dominate budgets, assuming tolerance for perf variance.

Use Case Comparison

LLM Training

FluidStack recommended

AWS

AWS excels with p5.48xlarge (8x H100s) instances, Trainium for cost-efficient scaling to 1000s of chips via UltraClusters, and SageMaker for distributed training frameworks (e.g., SMDDP). Global AZs ensure redundancy; spot fleets handle interruptions via fault-tolerant designs. Drawback: queue times during peaks, higher base costs (~$98/hr per 8xH100). Ideal for teams needing managed hyperparameter tuning.

FluidStack

FluidStack shines for massive on-demand clusters (1000+ A100/H100s) via supercloud pooling, rapid provisioning without waitlists. Spot access to spares cuts costs 50-80%; unified API simplifies multi-DC orchestration. Variability in interconnects (InfiniBand/Ethernet) may require tuning; suits raw scale over managed services.

Batch Inference

Either works

AWS

AWS leverages Inferentia for low-cost, high-throughput inference (tf2/inferentia), auto-scaling via Lambda/SageMaker Batch Transform. EBS/S3 integration streamlines data pipelines; spot savings apply. Strong for scheduled jobs with compliance needs, though egress impacts large outputs.

FluidStack

FluidStack provides cost-effective GPU spots for offline batches, aggregating capacity for parallel jobs. Per-minute billing fits variable durations; global DCs reduce data transfer latency. Less optimized for serverless; consistency across providers may affect throughput uniformity.

Real-time Inference

AWS recommended

AWS

AWS dominates with low-latency endpoints via SageMaker Hosting, multi-model servers, and Inferentia/Tranium for <100ms p99. Elastic scaling, WAF integration, global edge via CloudFront. Premium pricing justified by SLAs and monitoring; VPC ensures security.

FluidStack

FluidStack supports real-time via Kubernetes-deployed services, but inter-DC latency variability (50-200ms) and less mature autoscaling hinder consistency. Good for cost if colocated; lacks AWS's managed inference optimizations and edge caching.

Fine-tuning & Experimentation

AWS recommended

AWS

SageMaker Studio notebooks with per-second g5 instances enable rapid iteration; JumpStart models accelerate starts. Spot for cheap trials, integrated artifacts in S3. Complexity suits experienced teams; costlier for frequent small runs.

FluidStack

FluidStack's spot A100s offer cheap experimentation at scale; simple API for spinning clusters. Per-minute suits short jobs; less tooling for notebooks/experiments, relying on user BYO (e.g., Jupyter). Agile for prototypes, variable perf noted.

Technical Comparison

Infrastructure

AWS provides virtualized GPU instances (Nitro-based) on bare-metal hosts, with EFA for low-latency multi-node (up to 20k GPUs), EBS/GP3 storage (up to 260k IOPS), and managed EKS/Kubernetes. VPC networking (up to 100Gbps), FSx Lustre for parallel FS. FluidStack aggregates bare-metal and virtualized GPUs across 100+ DCs, unified Kubernetes support via API, but storage/networking varies (Ceph/S3-like, 10-400Gbps Ethernet/IB). No proprietary FS; relies on underlying providers. AWS offers more uniform, managed options.

Performance

AWS delivers consistent NVLink/InfiniBand scaling (e.g., p5: 3.5TB/s aggregate), high GPU availability via capacity blocks, Trainium matching A100 TFLOPS at 1/4 cost. Benchmarks show <5% variance in Trn1 clusters. FluidStack enables rapid 1000-GPU ramps, competitive A100/H100 perf, but inter-node BW varies (100-400Gbps), potential 10-20% throughput gaps vs uniform fleets. Spot availability excels for bursts; multi-GPU good for intra-node, less predictable at exascale.

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both AWS and FluidStack 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?▾

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

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

Which provider offers better development tools like Jupyter notebooks?▾

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

Which provider has better Kubernetes support for orchestration?▾

Both AWS and FluidStack 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?▾

AWS is best suited for Large-scale enterprises requiring deep integration with other cloud services; Organizations needing globally redundant availability zones. FluidStack excels at Large-scale training runs requiring massive, immediate capacity; Global reach for GPU resources. 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 AWS and FluidStack 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 AWS and FluidStack offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); FluidStack has no published SLA.

Which provider has better API and automation support?▾

Both AWS and FluidStack 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 AWS and FluidStack 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?▾

AWS's standout features include: Proprietary silicon like Trainium and Inferentia chips; Fully managed ML development environment with SageMaker. FluidStack's standout features include: Supercloud architecture pooling global resources; Aggregation of spare capacity from Tier 1-4 data centers. 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 AWS, visit their website at https://aws.amazon.com?utm_source=gpuperhour&utm_medium=referral to create an account and explore available GPU options. For FluidStack, visit https://www.fluidstack.io?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.