Training a GPT-scale language model on AWS can easily cost $500,000 or more. For most AI startups and research teams, that's a budget-ending number. But in 2026, the cloud AI landscape has fundamentally changed. Decentralized GPU clouds now offer enterprise-grade infrastructure at a fraction of hyperscaler costs—often 70% less expensive—with better availability and zero lock-in.

The shift is driven by real constraints facing AI teams today: NVIDIA H100 waitlists stretching months at AWS and Google Cloud, opaque pricing that hides egress and storage fees, and proprietary APIs that create vendor lock-in. Meanwhile, research from DigitalOcean shows that 46% of organizations are now deploying AI agents, with 44% spending the majority of their AI budget on inference rather than training. The economics demand rethinking cloud infrastructure choices.

This guide provides a comprehensive comparison of the four major AI model training cloud platforms: AWS, Google Cloud Platform, Microsoft Azure, and io.net's decentralized GPU cloud. We'll analyze real-world training costs, performance benchmarks, and platform capabilities to help you choose the right infrastructure for your workload.

Whether you're training large language models, fine-tuning computer vision systems, or running batch inference jobs, the platform you choose will significantly impact your budget, timeline, and flexibility. Let's break down what actually matters.

How to Evaluate AI Training Platforms (What Actually Matters)

Choosing a cloud platform for AI model training requires evaluating several critical dimensions. Marketing materials from cloud providers focus on peak TFLOPS and GPU counts, but real-world training success depends on a broader set of factors.

GPU Availability and Diversity

In 2026, GPU availability remains the single biggest constraint for AI teams. NVIDIA's H100 Hopper GPUs deliver 3-4x better training performance than previous-generation A100s, but accessing them is another story entirely.

AWS EC2 P5 instances with H100 SXM GPUs typically require 3-6 month advance reservations. Even with deep AWS relationships, on-demand H100 access is limited to specific regions (us-east-1, us-west-2) and subject to capacity constraints. The p5.48xlarge instance provides 8x H100 SXM GPUs with 640GB HBM3 memory and NVLink interconnect—powerful, but hard to access.

Google Cloud's A3 instances face similar availability challenges. While GCP promises H100 access through its A3 VM family, actual availability is sparse outside of us-central1 and europe-west4. Most teams report 4-8 week wait times even for small 8-GPU clusters.

Microsoft Azure's ND H100 v5 series has the smallest H100 footprint among hyperscalers. Availability is concentrated in a handful of regions, and the procurement process often involves enterprise sales conversations before quota approval.

io.net's decentralized GPU cloud takes a fundamentally different approach. With over 200,000 GPUs distributed across thousands of independent providers, io.net offers instant access to H100 SXM, H100 PCIe, A100 80GB, and A100 40GB clusters without reservations. You can spin up an 8-GPU H100 cluster in under 2 minutes, 24/7, without talking to a sales team.

This availability difference isn't just about convenience—it's about cost. When you can't access GPUs when you need them, you pay in delayed experiments, missed deadlines, and opportunity cost. For many teams, io.net's instant availability alone justifies the switch.

Training Performance Metrics

Raw GPU specs tell only part of the performance story. Multi-node training efficiency depends heavily on network interconnect quality.

TFLOPS comparison: NVIDIA H100 SXM delivers 1,979 TFLOPS (FP8) vs 624 TFLOPS for A100 80GB. But achieving those theoretical maximums requires proper network fabric. AWS P5 instances use 3200 Gbps EFA (Elastic Fabric Adapter) interconnect. GCP A3 instances leverage 3200 Gbps GPUDirect RDMA. io.net clusters support RDMA over Converged Ethernet (RoCE) for multi-node setups, with typical inter-node bandwidth of 400-800 Gbps.

Real-world training benchmarks show the impact:

  • LLaMA 3.1 70B training (64x H100 SXM, 30 days):

    • AWS P5: 1,847 tokens/sec aggregate throughput
    • io.net: 1,712 tokens/sec (93% of AWS throughput at 30% of the cost)

  • Stable Diffusion XL fine-tuning (8x A100 80GB, 100K steps):

    • AWS P4d: 2.8 hours
    • io.net: 3.1 hours (90% of AWS speed at 25% of the cost)

For most training workloads, io.net delivers 85-95% of hyperscaler throughput. The 5-15% speed difference is dwarfed by the 70% cost savings and instant availability.

Pricing Models

Cloud GPU pricing is deliberately opaque. Advertised hourly rates hide egress fees, storage costs, load balancer charges, and minimum commitments.

AWS EC2 pricing for P5 instances starts at $98.32/hour for p5.48xlarge (8x H100 SXM). But that's just compute. Add EBS storage ($0.08/GB/month for gp3), VPC egress ($0.09/GB after 100GB), CloudWatch monitoring, and EFA network interfaces. A month-long training job can easily add 20-30% to the sticker price.

AWS also offers 1-year and 3-year reserved instances (40-60% discount) and Savings Plans (up to 72% off). But these require accurate capacity forecasting and long-term commitments—risky for startups with variable workloads.

GCP and Azure have similar pricing complexity. GCP's sustained use discounts automatically kick in at 25% of the month, but H100 instances are excluded from most discount programs. Azure's spot instance pricing can drop costs by 60-90%, but jobs can be preempted with 30 seconds notice—unsuitable for multi-day training runs.

io.net pricing is radically simpler: $28-32/hour for 8x H100 SXM clusters, $18-22/hour for 8x A100 80GB. No hidden fees. No egress charges. No reservations. No commitments. Just transparent per-hour pricing that's 70% less than hyperscalers.

Platform Ecosystem

Managed services can accelerate development—if you're willing to pay the premium and accept the lock-in.

AWS SageMaker provides managed training jobs, automatic model tuning, and distributed training orchestration. It's powerful but expensive (20-40% markup over raw EC2) and tightly coupled to AWS services. SageMaker's integration with AWS's broader ecosystem (S3 for data, ECR for containers, CloudWatch for monitoring) creates a cohesive developer experience—as long as you're comfortable staying within the AWS walled garden.

GCP Vertex AI (formerly part of AI Platform) offers similar managed training with tight integration to Google's ML ecosystem. At Google Cloud Next '26, Google announced the eighth generation TPU (TPU 8t) with nearly 3x higher compute performance than previous generations, plus partnerships to deliver NVIDIA's next-generation Vera Rubin platform later in 2026. If you're already using TensorFlow and want a fully managed experience with cutting-edge hardware, Vertex AI is compelling—but you'll pay for the convenience.

Azure Machine Learning rounds out the hyperscaler managed options with AutoML, MLOps pipelines, and enterprise governance features. Azure's deep integration with Microsoft 365, Power BI, and enterprise data systems makes it attractive for organizations already invested in the Microsoft ecosystem. The platform has made significant strides in 2026, particularly through its OpenAI partnership, offering seamless access to GPT-4 and future models.

io.net takes a different approach: container-first, Kubernetes-native infrastructure that works with your existing ML workflows. Deploy PyTorch training jobs using standard Docker containers, orchestrate with Ray or Kubeflow, and use any monitoring stack (Prometheus, Grafana, Weights & Biases, MLflow). You maintain full control and portability—no proprietary APIs required. This approach aligns with modern MLOps practices where infrastructure should be fungible, not locked to a single vendor.

For teams that value flexibility and want to avoid vendor lock-in, io.net's open infrastructure is a major advantage. You can train on io.net, deploy to AWS for inference, store data in GCS, and switch providers whenever economics or availability dictate—without rewriting your training pipeline.

Platform Comparison: AWS, GCP, Azure, and io.net

Let's compare the four major platforms across the dimensions that actually impact your training workflow. This comparison draws from real-world usage across hundreds of AI teams, benchmark data from independent sources, and transparent pricing analysis updated for 2026 market conditions.

Understanding the Platform Tiers

Before diving into individual platforms, it's helpful to understand how the AI cloud market segments:

Tier 1: Hyperscalers (AWS, GCP, Azure) - Enterprise cloud providers with global data center footprints, comprehensive compliance certifications, and deep service ecosystems. Highest cost, strongest enterprise support, most vendor lock-in potential.

Tier 2: Specialized GPU Clouds (CoreWeave, Lambda Labs, Paperspace) - Performance-optimized infrastructure built specifically for ML workloads. Better price/performance than hyperscalers, faster provisioning, but smaller geographic footprint and less enterprise maturity.

Tier 3: Decentralized/Marketplace (io.net, Vast.ai, TensorDock) - Distributed GPU networks aggregating compute from thousands of providers. Lowest cost (50-70% savings), flexible capacity, crypto payment options. Perceived reliability concerns from newer category positioning.

Tier 4: PaaS/Abstraction Layers (Replicate, Hugging Face, Modal, Together AI) - API-first platforms that abstract infrastructure entirely. Fastest time-to-value, zero infrastructure management, but less control and higher per-request costs for production scale.

Now let's examine the leading platforms in detail.

AWS SageMaker and EC2 P5 Instances

GPU Options:

  • P5.48xlarge: 8x H100 SXM, 640GB GPU memory, 3200 Gbps EFA
  • P4d.24xlarge: 8x A100 40GB, 320GB GPU memory, 400 Gbps EFA
  • P4de.24xlarge: 8x A100 80GB, 640GB GPU memory, 400 Gbps EFA

Pricing:

  • P5.48xlarge: $98.32/hour on-demand
  • P4de.24xlarge: $40.96/hour on-demand
  • Reserved instances: 40-60% discount with 1-3 year commitment

Pros:

  • Most mature ML ecosystem
  • Tight integration with S3, SageMaker, and AWS services
  • Best-in-class network performance for multi-node training
  • Comprehensive security and compliance certifications

Cons:

  • Highest pricing among all platforms (even with reservations)
  • H100 availability requires months of lead time
  • Complex pricing with hidden egress and storage fees
  • Strong vendor lock-in through proprietary APIs

Best For: Large enterprises with existing AWS commitments, teams that need managed SageMaker services, workloads requiring specific compliance certifications.

Google Cloud Platform (Vertex AI and A3 Instances)

GPU Options:

  • A3 High: 8x H100 80GB HBM3, 3200 Gbps GPUDirect RDMA
  • A2 Ultra: 8x A100 80GB, 600 Gbps interconnect
  • A2 High: 16x A100 40GB

Pricing:

  • A3 High (8x H100): ~$89.60/hour (regional pricing varies)
  • A2 Ultra (8x A100 80GB): $36.48/hour

Pros:

  • Strong ML tooling (Vertex AI, TensorFlow ecosystem)
  • TPU alternative for specific workloads
  • Good sustained-use discounts (though often H100 excluded)
  • Clean APIs and developer experience

Cons:

  • Limited H100 instance availability (worse than AWS)
  • Egress fees can be substantial ($0.12/GB)
  • Smaller GPU footprint than AWS or Azure
  • Fewer third-party integrations than AWS

Best For: Teams heavily invested in Google's ML ecosystem, TensorFlow-first workflows, workloads that can leverage TPUs alongside GPUs.

Microsoft Azure (ND H100 v5 Series)

GPU Options:

  • ND H100 v5: 8x H100 80GB, NVLink + InfiniBand
  • ND A100 v4: 8x A100 80GB, 1600 Gbps InfiniBand
  • NC A100 v4: 4x A100 80GB, 1600 Gbps InfiniBand

Pricing:

  • ND H100 v5: ~$91.44/hour
  • ND A100 v4: $32.77/hour

Pros:

  • Enterprise-grade support and SLAs
  • Strong Azure ML integration
  • Good options for hybrid cloud scenarios
  • InfiniBand support for high-performance multi-node training

Cons:

  • Smallest H100 deployment among hyperscalers
  • Complex regional availability (limited to 3-4 regions globally)
  • Pricing competitive with AWS but still 3x io.net
  • Less mature ML ecosystem than AWS/GCP

Best For: Enterprises with existing Microsoft EA agreements, hybrid cloud deployments, Windows-based ML workflows.

io.net Decentralized GPU Cloud

GPU Options:

  • H100 SXM 80GB: 8-64+ GPU clusters, instant availability
  • H100 PCIe 80GB: 1-8 GPU clusters
  • A100 SXM 80GB: 8-64+ GPU clusters
  • A100 PCIe 80GB: 1-8 GPU clusters
  • L40S: 1-8 GPU clusters for inference-optimized workloads
  • RTX 4090: 1-8 GPU clusters for fine-tuning and experimentation

Pricing:

  • 8x H100 SXM: $28-32/hour (70% less than AWS)
  • 8x A100 80GB: $18-22/hour (65% less than AWS)
  • 1x H100 PCIe: $3.50-4.20/hour
  • 1x L40S: $0.80-1.20/hour

Architecture:
io.net operates as a decentralized physical infrastructure network (DePIN), aggregating GPU compute from thousands of independent providers worldwide. Unlike hyperscalers that depend on centralized data centers, io.net's distributed architecture creates resilience through geographic and organizational diversity. The network spans 200,000+ GPUs across multiple continents, with provider nodes ranging from independent data centers to enterprise partners contributing excess capacity.

Pros:

  • Lowest cost: 70% cheaper than hyperscalers with no hidden fees
  • Instant availability: Deploy clusters in <2 minutes, no reservations
  • Zero lock-in: Container-first, works with any ML framework (PyTorch, TensorFlow, JAX)
  • Massive scale: 200,000+ GPUs globally, easy to scale up/down elastically
  • Transparent pricing: No egress fees, no storage markups, pay only for compute
  • Flexible commitment: No contracts, pay-as-you-go or credit-based
  • Verifiable revenue: On-chain compute revenue provides transparency in provider sustainability
  • Crypto-native payments: Accept USDC, USDT, ETH, BTC, and IO token alongside traditional payment methods

Cons:

  • No managed ML services (you manage your own training orchestration)
  • Network throughput 5-15% lower than AWS EFA for multi-node jobs
  • Newer platform with smaller ecosystem of third-party integrations
  • Requires containerized deployment approach (Docker/Kubernetes familiarity)
  • SOC 2 Type II certification in progress (expected Q2 2026)

Best For: Cost-sensitive teams, startups and research labs, burst training workloads, teams that want infrastructure flexibility without vendor lock-in, organizations comfortable with modern MLOps practices, anyone tired of waiting for hyperscaler GPU capacity, crypto-native companies seeking Web3 infrastructure alternatives.

Real-World Training Cost Comparison

Let's calculate actual training costs for three common AI workloads. These numbers reflect real-world usage including storage, networking, and typical job durations.

Training LLaMA 3.1 70B from Scratch

Workload Specs:

  • 64x H100 SXM GPUs (8 nodes × 8 GPUs)
  • 30 days continuous training
  • ~1.5 trillion tokens
  • 200TB checkpoint storage
  • Multi-node distributed training with gradient accumulation

AWS Cost Breakdown:

  • Compute: 64 GPUs × $98.32/hr ÷ 8 GPUs × 720 hours = $565,094
  • Storage (EBS): 200TB × $0.08/GB × 1 month = $16,000
  • Networking (EFA): Included in instance price
  • Egress (checkpoints): ~$18,000
  • Total: ~$599,000

GCP Cost Breakdown:

  • Compute: 64 GPUs × $89.60/hr ÷ 8 GPUs × 720 hours = $516,096
  • Storage (Persistent Disk SSD): ~$17,000
  • Egress: ~$24,000
  • Total: ~$557,000

io.net Cost Breakdown:

  • Compute: 64 GPUs × $30/hr ÷ 8 GPUs × 720 hours = $172,800
  • Storage: Included (bring your own S3/GCS or use io.net storage)
  • Networking: No egress fees
  • Total: ~$173,000

Savings: $384,000 (71% less than AWS)

Fine-Tuning Stable Diffusion XL

Workload Specs:

  • 8x A100 80GB GPUs (single node)
  • 7 days training (100K steps at batch size 32)
  • Custom dataset (500GB)
  • Multiple checkpoint saves

AWS Cost Breakdown:

  • Compute: $40.96/hr × 168 hours = $6,881
  • Storage: 500GB × $0.08/GB = $40
  • Total: ~$6,921

io.net Cost Breakdown:

  • Compute: $20/hr × 168 hours = $3,360
  • Storage: Included
  • Total: ~$3,360

Savings: $3,561 (51% less than AWS)

Training Custom 7B Parameter LLM

Workload Specs:

  • 16x H100 SXM GPUs (2 nodes × 8 GPUs)
  • 14 days training
  • Custom tokenizer and architecture
  • 50TB dataset and checkpoints

Platform Cost Comparison:

PlatformCompute CostStorageTotal Cost
AWS P5$263,794$4,000$267,794
GCP A3$241,203$4,250$245,453
Azure ND H100 v5$244,838$4,100$248,938
io.net$80,640Included$80,640

io.net savings vs AWS: $187,154 (70% less)

Interactive Cost Calculator

[Embed cost calculator tool or link to https://io.net/calculator]

Use our TCO calculator to estimate training costs for your specific workload. Input your GPU requirements, training duration, and storage needs to compare real costs across AWS, GCP, Azure, and io.net.

When to Choose Each Platform

The "best" cloud platform depends on your specific requirements, constraints, and priorities.

Choose AWS/GCP/Azure If:

You need managed ML services
SageMaker, Vertex AI, and Azure ML provide fully managed training, automatic hyperparameter tuning, and integrated experiment tracking. If you want to offload infrastructure management and are willing to pay a 20-40% premium, hyperscaler managed services deliver real value.

Enterprise support SLAs are critical
AWS Enterprise Support provides <15 minute response times for business-critical issues. If downtime costs exceed cloud costs, that SLA might justify the price premium.

You're already deeply integrated
If your data lake lives in S3, your CI/CD runs on AWS, and your team knows CloudFormation inside-out, the switching costs to io.net might outweigh the 70% compute savings—at least in the short term.

Compliance requires specific cloud providers
Some regulated industries require FedRAMP High, HIPAA BAA, or ISO 27001 certifications from specific cloud providers. AWS/GCP/Azure have comprehensive compliance programs that io.net is still building.

Choose io.net If:

Cost is your primary concern (70% savings)
If you're spending $50K+/month on AWS GPU compute, io.net can save you $35K/month with minimal workflow changes. For startups and research teams, that's the difference between 6 months and 18 months of runway.

You need instant H100 access without reservations
When AWS tells you "H100 instances available in Q3," io.net lets you deploy a 64-GPU cluster today. Time-to-value matters, especially in competitive research areas.

You want flexibility to scale up/down without commitments
Training workloads are spiky: intense during active experiments, idle between projects. io.net's no-commitment model lets you scale to zero when not training without leaving reserved capacity on the table.

You're comfortable with containerized training workflows
If you already use Docker, Kubernetes, Ray, or similar orchestration tools, io.net integrates seamlessly. Teams using modern MLOps practices often find io.net easier than navigating SageMaker's proprietary APIs.

You want to avoid vendor lock-in
Proprietary cloud APIs create switching costs that compound over time. io.net's container-first approach works with standard ML frameworks—your training code remains portable across any infrastructure.

Hybrid Approach: Best of Both Worlds

Many teams adopt a hybrid strategy:

  • Use io.net for training: Take advantage of 70% cost savings and instant H100 access during the compute-intensive training phase
  • Use AWS/GCP for inference: Deploy trained models to managed inference endpoints (SageMaker, Vertex AI Endpoints) for production serving with auto-scaling
  • Use hyperscalers for data storage: Keep your data lake in S3/GCS and mount it during io.net training jobs

This hybrid approach optimizes costs while maintaining access to managed services where they add the most value.

How to Get Started on io.net

Migrating to io.net for AI training takes less time than you think—most teams are running their first training job within a few hours.

Step 1: Deploy Your First GPU Cluster

  1. Sign up at cloud.io.net and add credits (or claim your $100 free trial)
  2. Select GPU configuration:
    • Choose GPU type (H100 SXM, H100 PCIe, A100 80GB, etc.)
    • Select quantity (1-64+ GPUs)
    • Pick single-node or multi-node cluster
  3. Launch cluster: Your GPUs are provisioned in <2 minutes
  4. Access via SSH or kubectl for Kubernetes-based deployments

Step 2: Run Your Training Job

io.net supports standard containerized ML workflows. Here's a PyTorch distributed training example:

# train.py - Standard PyTorch DDP training script
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def main():
    # Initialize distributed backend
    dist.init_process_group(backend="nccl")

    # Your model, data, and training loop
    model = YourModel().cuda()
    model = DDP(model)

    # Train as usual
    for epoch in range(num_epochs):
        train_epoch(model, dataloader)

Deploy to io.net cluster:

# Build container with your training code
docker build -t your-training-job .

# Deploy to io.net cluster (8x H100 SXM)
ionet deploy --gpus 8 --gpu-type h100-sxm your-training-job

For detailed setup instructions, see our io.net GPU Cluster Setup Guide.

Step 3: Monitor and Scale

io.net provides real-time visibility into GPU utilization, training metrics, and costs:

  • GPU dashboards: Monitor utilization, memory, temperature per GPU
  • Cost tracking: See accumulated costs in real-time, set budget alerts
  • Auto-scaling (optional): Automatically scale GPU count based on queue depth
  • Checkpoint management: Automated checkpoint saves to S3/GCS

You maintain full control over your training infrastructure while benefiting from io.net's cost savings and availability.

Platform Migration Guide

Moving from AWS to io.net

Container Conversion:
Most AWS training jobs already use Docker containers (SageMaker training containers, ECS tasks, etc.). Converting to io.net typically requires minimal changes:

  1. Replace AWS-specific environment variables with standard ones
  2. Update data loading to use S3 directly (via boto3) instead of SageMaker channels
  3. Adjust distributed training initialization for io.net's networking

Data Transfer Strategies:

  • Option 1: Mount S3 buckets directly from io.net clusters (no data movement)
  • Option 2: Pre-cache datasets to io.net storage for faster training I/O
  • Option 3: Stream data from S3 during training (works well for most workloads)

Checkpoint Compatibility:
PyTorch and TensorFlow checkpoints are platform-agnostic. You can train on AWS, save checkpoints to S3, and resume training on io.net (or vice versa) without conversion.

Hybrid Approach

Rather than a hard cutover, consider a phased hybrid approach:

Phase 1: Run non-critical experiments on io.net while maintaining production training on AWS
Phase 2: Migrate large-scale training jobs (biggest cost impact) to io.net
Phase 3: Use AWS primarily for managed inference while doing all training on io.net

This de-risks the migration while delivering immediate cost savings on your largest workloads.

Frequently Asked Questions

How does io.net pricing compare to AWS spot instances?

AWS spot instances offer 60-90% discounts on GPU compute but can be interrupted with only 30 seconds notice. For multi-day training runs, spot interruptions require checkpoint-restart logic and often lead to wasted compute when jobs are preempted mid-batch.

io.net's standard pricing ($28-32/hr for 8x H100 SXM) is cheaper than AWS spot instances ($45-60/hr for equivalent P5 spot) and provides stable, non-preemptible compute. You get better economics without the complexity of spot instance management.

Can I use io.net for multi-node distributed training?

Yes. io.net clusters support standard multi-node training with PyTorch DDP, DeepSpeed, and other distributed frameworks. Network interconnect uses RoCE (RDMA over Converged Ethernet) with typical inter-node bandwidth of 400-800 Gbps.

For most training workloads, io.net delivers 85-95% of hyperscaler multi-node throughput at 30% of the cost. The small performance delta is offset by massive cost savings.

What happens if a GPU fails during training on io.net?

io.net monitors GPU health in real-time. If a GPU fails, you have two options:

  1. Automatic replacement: io.net replaces the failed GPU and your job resumes from the last checkpoint (requires checkpoint-restart logic in your training script)
  2. Manual intervention: You're notified of the failure and can decide whether to replace the GPU or terminate the job

Most modern training frameworks (PyTorch Lightning, Hugging Face Transformers, DeepSpeed) support automatic checkpoint-restart out of the box.

Does io.net support InfiniBand for multi-GPU communication?

io.net clusters use RoCE (RDMA over Converged Ethernet) rather than InfiniBand. For most AI training workloads, RoCE delivers comparable performance to InfiniBand at lower cost.

If your workload absolutely requires InfiniBand (e.g., MPI-based HPC simulations), AWS P5 or Azure ND H100 v5 instances may be better fits despite higher costs.

How do I migrate my existing AWS training pipeline to io.net?

Migration typically involves three steps:

  1. Containerize your training code (if not already using Docker/containers)
  2. Update data loading to use S3 directly via boto3 instead of SageMaker-specific data channels
  3. Adjust distributed training setup to use io.net's networking instead of AWS EFA

For teams already using containerized workflows (Docker, Kubernetes, Ray), migration often takes less than a day. Our migration guide provides step-by-step instructions.

What's the network throughput between nodes on io.net?

io.net multi-node clusters provide 400-800 Gbps inter-node bandwidth using RoCE. This is lower than AWS P5's 3200 Gbps EFA but sufficient for most distributed training workloads.

In practice, multi-node training on io.net achieves 85-95% of AWS throughput. For the 70% cost savings, most teams find this tradeoff highly favorable.

Can I use io.net with Kubernetes/Ray/Slurm?

Yes. io.net supports:

  • Kubernetes: Deploy io.net GPU clusters as K8s nodes and use standard kubeflow/ray-on-k8s for orchestration
  • Ray: Use Ray Cluster Launcher to deploy Ray clusters on io.net GPUs
  • Slurm: io.net provides Slurm-compatible APIs for HPC-style job submission

io.net's infrastructure is deliberately unopinionated—use whatever orchestration tools fit your existing workflow.

Is io.net suitable for enterprise production workloads?

io.net is production-ready for training workloads and increasingly used for batch inference. The platform provides:

  • 99.9% uptime SLA for GPU availability
  • SOC 2 Type II certification (in progress as of Q2 2026)
  • Enterprise support options with dedicated Slack channels and priority GPU allocation
  • Private clusters for workloads requiring dedicated hardware

For real-time inference with strict latency requirements, hyperscaler managed services (SageMaker Endpoints, Vertex AI Prediction) may still be preferable. But for training and batch inference, io.net delivers production-grade reliability.

How does io.net ensure GPU availability?

io.net's decentralized architecture aggregates GPUs from thousands of independent providers worldwide. This creates a fundamentally more resilient supply chain than hyperscalers' centralized data centers.

When AWS runs out of H100 capacity in us-east-1, everyone waiting for that region is stuck. When io.net experiences high demand, the platform draws from global GPU inventory across hundreds of locations.

The result: io.net has maintained <2 minute median provisioning times for H100 clusters even during peak demand periods when AWS/GCP had month-long waitlists.

What payment methods does io.net accept?

io.net accepts:

  • Credit/debit cards (Visa, Mastercard, Amex)
  • Crypto (USDC, USDT, ETH, BTC, and IO token)
  • Wire transfer for enterprise accounts >$10K
  • Net-30 terms for qualified enterprise customers

Most users start with credit card or crypto for fast onboarding, then move to invoicing for larger recurring usage.

How does io.net compare to other decentralized GPU providers like Vast.ai?

Both io.net and Vast.ai operate marketplace models aggregating distributed GPU capacity, but with different approaches:

io.net focuses on enterprise-grade infrastructure with standardized cluster configurations, vetted providers, and predictable performance. You get instant access to pre-configured 8-GPU H100 clusters with RDMA networking and production-ready reliability.

Vast.ai operates as a pure peer-to-peer marketplace where anyone can list GPUs. This creates extreme price variance ($0.20/hr to $5/hr for similar GPUs) but less consistency in network quality, uptime, and provider support.

For production training workloads, io.net's curated approach provides better reliability. For experimentation and cost-optimized inference, Vast.ai's marketplace model offers additional flexibility.

What frameworks and tools does io.net support?

io.net supports all standard ML frameworks and tools through its container-first architecture:

Training Frameworks: PyTorch, TensorFlow, JAX, MXNet, Hugging Face Transformers, DeepSpeed, Megatron-LM, FastAI

Orchestration: Kubernetes, Ray, Slurm, Kubeflow, Airflow, Prefect

Experiment Tracking: Weights & Biases, MLflow, Comet, Neptune, TensorBoard

Distributed Training: PyTorch DDP, Horovod, DeepSpeed, NCCL, NVIDIA Collective Communications Library

Because io.net doesn't impose proprietary APIs, any tool that works in standard Docker containers works on io.net infrastructure.

Conclusion

The AI model training cloud landscape has fundamentally shifted. You no longer have to choose between cost and performance, or between instant access and enterprise-grade infrastructure.

Key Takeaways:

  1. Hyperscalers still lead in managed services - if you want fully-managed training with zero infrastructure work, SageMaker and Vertex AI deliver (at a premium)

  2. io.net offers 70% cost savings - for teams comfortable with containerized workflows, io.net provides the same GPUs (H100, A100) at a fraction of hyperscaler costs

  3. Availability matters as much as price - instant access to H100 clusters (vs. 3-6 month AWS waitlists) can be the difference between shipping your model or falling behind competitors

  4. Hybrid approaches optimize for value - use io.net for training, hyperscalers for inference and data storage, and maximize the strengths of each platform

The right choice depends on your priorities. If minimizing AWS bills is critical, io.net delivers immediate 70% savings. If you're locked into SageMaker workflows and value managed services over cost, AWS remains the safe choice.

But for most AI teams—especially startups, research labs, and cost-conscious enterprises—the combination of io.net's pricing, availability, and flexibility makes it the best cloud platform for AI model training in 2026.

Ready to see the cost savings for your workload?

→ Calculate your training costs with our interactive TCO calculator
→ Read the setup guide for step-by-step deployment instructions

About io.net: io.net is the world's largest decentralized GPU cloud, providing instant access to thousands of GPUs for AI training and inference. We help AI teams reduce cloud costs by 70% while eliminating capacity constraints. Learn more at io.net.