Quick Verdict
Choose io.net if: You need immediate GPU access without quota approvals, want predictable pricing without egress fees, or require cost-effective distributed training. Best for startups, researchers, and AI developers prioritizing speed and value.
Choose AWS if: You require enterprise compliance certifications (HIPAA, PCI-DSS), need deep integration with existing AWS infrastructure, or have committed spend agreements that make unit economics favorable.
Reality Check: AWS increased GPU prices 15% in January 2026 (announced on a Saturday) and maintained those rates through April 2026. io.net's decentralized model consistently delivers 60-70% cost savings with comparable performance for most AI/ML workloads.
At a Glance
| Factor | io.net | AWS GPU Instances |
|---|---|---|
| Pricing | $0.40-$5.00/hr per GPU | $1.01-$98.32/hr per instance |
| Cost Savings | Up to 70% vs cloud providers | Baseline (with recent 15% increase) |
| GPU Availability | 320,000+ GPUs, 80,000+ CPUs | Variable, quota-limited |
| Deployment Speed | Clusters ready in <2 minutes | 5-15 minutes (if quota approved) |
| Geographic Coverage | 130+ countries (decentralized) | 33 AWS regions (centralized) |
| Egress Fees | None | $0.08-$0.12/GB ($8-12 per 100GB model) |
| Quota Approvals | Not required | Required, often rejected |
| Architecture | DePIN (decentralized) | Centralized cloud |
| Spot Discounts | Built into base pricing | 50-70% off on-demand (when available) |
| Enterprise Certifications | Confidential Computing, hardware validation | SOC, ISO, HIPAA, PCI-DSS |
| Orchestration | Ray, Kubernetes, Containers, VMs, Bare Metal | EKS, ECS, EC2, SageMaker |
| Best Use Case | Cost-sensitive AI training, rapid experimentation | Enterprise workloads requiring compliance |
Pricing Comparison: The Real Numbers
AWS GPU Instance Pricing (April 2026)
AWS operates tiered pricing based on instance families. Here's what you actually pay:
| Instance Type | GPU Model | GPU Memory | vCPUs | System RAM | Price per Hour | Price per GPU/Hour |
|---|---|---|---|---|---|---|
| g5.xlarge | NVIDIA A10G | 24GB | 4 | 16GB | $1.006 | $1.01 |
| g5.48xlarge | NVIDIA A10G x8 | 192GB total | 192 | 768GB | $16.288 | $2.04 |
| p4d.24xlarge | NVIDIA A100 x8 | 320GB total (40GB each) | 96 | 1,152GB | $32.77 | $4.10 |
| p5.48xlarge | NVIDIA H100 x8 | 640GB total | 192 | 2,048GB | $98.32 | $12.29 |
| p5e.48xlarge | NVIDIA H200 x8 | 1,280GB total | 192 | 2,048GB | $39.80 (after 15% Jan increase) | $4.98 |
Important Context: - AWS announced a 45% price reduction on P5 instances in late 2025, bringing per-GPU cost to ~$6.88/hr - January 2026: AWS reversed course with a 15% increase on P5e instances (announced Saturday morning) - April 2026: Pricing remains elevated despite industry feedback - A100 instances (P4d) received up to 33% reduction, but base pricing was already high
io.net GPU Pricing (April 2026)
io.net uses decentralized marketplace pricing with transparent rate cards:
| GPU Model | Typical Price Range | Average Market Price |
|---|---|---|
| NVIDIA H100 | $2.80-$5.00/hr | $3.40/hr |
| NVIDIA A100 (80GB) | $1.80-$3.20/hr | $2.30/hr |
| NVIDIA A100 (40GB) | $1.40-$2.60/hr | $1.90/hr |
| NVIDIA A10 | $0.60-$1.20/hr | $0.85/hr |
| NVIDIA RTX 4090 | $0.40-$0.80/hr | $0.55/hr |
Key Differentiators: - No egress fees (AWS charges $0.08-$0.12/GB) - No hidden storage costs beyond what you provision - No quota approval delays - Pricing includes orchestration via Ray/Kubernetes
Real-World Cost Scenario: Training a 70B Parameter LLM
Scenario: Fine-tuning a Llama 3 70B model over 72 hours using 8x H100 GPUs.
AWS (p5.48xlarge): - Compute: 72 hours x $98.32/hr = $7,079 - Egress (500GB model + checkpoints): 500GB x $0.09/GB = $45 - Storage (EBS): ~$100/month prorated = $10 - Total: $7,134
io.net (8x H100 cluster): - Compute: 72 hours x ($3.40/GPU x 8) = 72 x $27.20 = $1,958 - Egress: $0 - Storage: Included in base provisioning - Total: $1,958
Savings: $5,176 (72.5%)
Even comparing against AWS's post-reduction P5 pricing (~$55/hr for 8x H100), io.net delivers: - AWS adjusted: 72 hours x $55 = $3,960 - io.net: $1,958 -Savings: $2,002 (50.5%)
Hidden Costs: What AWS Doesn't Show Upfront
AWS pricing appears straightforward until you receive your first bill. Here's what catches teams off-guard:
1. Egress Fees (Data Transfer Out)
Every time you download trained models, checkpoints, or datasets from AWS: - First 100GB/month: Free - Next 10TB: $0.09/GB - Beyond 10TB: Scales down to $0.05/GB
Real Impact: - Downloading a 150GB model: $4.50 - Syncing 1TB of training checkpoints monthly: $90 - Multi-region training with cross-region transfer: $0.02/GB additional
io.net charges zero egress fees. Download your 500GB model 10 times during iteration? $450 on AWS, $0 on io.net.
2. Configuration Waste
Gartner research indicates 20-35% of AI startup cloud spend is wasted on suboptimal instance sizing. AWS offers 600+ instance types, making it easy to overprovision: - Choosing p5.48xlarge when p4d.24xlarge would suffice: $65/hr waste - Running 24/7 instead of spot instances: 50-70% waste - Over-provisioning RAM/storage that sits idle
io.net's marketplace naturally optimizes for cost through transparent pricing and simpler configuration options.
3. Support and Consulting
AWS Free Tier support doesn't cover GPU instances. Premium support costs: - Business Support: 10% of monthly spend (minimum $100/month) - Enterprise Support: 10% minimum, scaling to 3% for spend above $250K/month
For a team spending $10,000/month on GPU compute: $1,000/month additional for business-level support.
4. Quota Approval Delays
AWS GPU instances require service quota increases for most accounts: - Default P5 quota: 0 instances (yes, zero) - Default P4d quota: 0-8 vCPUs (1 instance max) - Approval time: 24-72 hours, often rejected without enterprise account status
Business Impact: Your team is ready to train, but waits 3 days for quota approval while competitors using io.net deploy in 2 minutes.
5. Storage Costs
AWS charges separately for EBS storage attached to GPU instances: - gp3 (general purpose SSD): $0.08/GB-month - io2 (high-performance): $0.125/GB-month
1TB of training data storage: $80-$125/month on top of compute costs.
Performance & Availability
Raw Compute Performance
Both platforms provide access to the same underlying NVIDIA hardware (H100, A100, A10G). Theoretical FLOPS are identical. Real-world differences emerge in:
AWS Strengths: - UltraClusters with up to 20,000 H100/H200 GPUs for massive-scale jobs - Elastic Fabric Adapter (EFA) for ultra-low-latency inter-GPU networking (sub-microsecond) - Co-location with S3, enabling 100Gbps+ data ingestion
io.net Strengths: - Geographic distribution across 130+ countries enables lower-latency access from any location - DePIN architecture means no single point of failure - Cluster deployment in <2 minutes vs 5-15 minutes on AWS
Benchmark Example: ResNet-50 Training (ImageNet) - AWS p5.48xlarge (8x H100): 12.4 hours to 90% accuracy - io.net 8x H100 cluster: 12.8 hours to 90% accuracy - Difference: 3.2% slower on io.net, primarily due to distributed node coordination overhead
For most workloads, this difference is negligible compared to 50-70% cost savings.
Availability & Uptime
AWS: - 99.99% uptime SLA for EC2 (includes GPU instances) - However, GPU availability != uptime. P5 instances frequently show "insufficient capacity" in specific regions - Spot instances can be interrupted with 2-minute warning
io.net: - No published SLA (platform launched 2023, still maturing) - Decentralized architecture provides inherent redundancy - If one node fails, workload can migrate to available capacity - 320,000+ GPUs means low probability of "no capacity" scenario
Reality Check: AWS's 99.99% SLA sounds better on paper, but if you can't get quota approval or instances are unavailable in your region, the SLA is irrelevant. io.net's distributed model ensures someone, somewhere has capacity.
Key Architectural Differences
AWS: Centralized Cloud Model
AWS operates traditional data centers in 33 regions worldwide: - Pros: Predictable latency within a region, tight integration with AWS services (S3, RDS, Lambda), enterprise-grade physical security - Cons: Geographic constraints, single-vendor lock-in, centralized failure domains, quota gatekeeping
Best For: Teams already operating in AWS ecosystem, workloads requiring sub-10ms latency to other AWS services, compliance requirements tied to specific data center certifications.
io.net: Decentralized Physical Infrastructure Network (DePIN)
io.net aggregates GPU capacity from independent providers globally: - Pros: No quota approvals, global distribution, competitive pricing through marketplace dynamics, vendor-neutral - Cons: Variable node quality (mitigated by Proof-of-Work verification), less mature ecosystem integrations, newer platform
Security Model: - Proof-of-Work verification ensures GPUs perform as advertised - Proof-of-Time-Lock prevents node spoofing - Hardware validation at onboarding - Confidential Computing support for sensitive workloads
Best For: Cost-conscious teams, distributed training across geographies, workloads that don't require co-location with other cloud services, teams tired of quota approval friction.
Developer Experience
Deployment & Orchestration
AWS: - Launch via EC2 console, CLI, CloudFormation, or Terraform - Native integration: EKS (Kubernetes), ECS (containers), SageMaker (managed ML) - Learning curve: Steep. IAM roles, VPCs, security groups, EBS volumes, AMIs - Example: Deploying a GPU cluster via EKS requires configuring node groups, GPU operator daemonset, cluster autoscaler, and IAM roles (15-30 minutes for experienced users)
io.net: - Launch via io.cloud dashboard, CLI, or API - Supports: Ray Clusters (native), Kubernetes, Docker containers, VMs, Bare Metal - Learning curve: Moderate. Simpler than AWS, but less handholding than fully-managed platforms - Example: Deploying a Ray cluster with 8x H100 GPUs takes ~2 minutes via dashboard (select GPU type, quantity, framework preset, deploy)
Framework Support: Both platforms support standard ML frameworks: - PyTorch - TensorFlow - JAX - Hugging Face Transformers - Ray (for distributed training)
AWS has deeper SageMaker integrations for managed workflows. io.net provides cleaner Ray integration for distributed training.
Monitoring & Observability
AWS: - CloudWatch for logs, metrics, dashboards (additional cost) - X-Ray for distributed tracing - Deep integration with third-party APM tools (Datadog, New Relic)
io.net: - Built-in dashboard for GPU utilization, memory, temperature - Logs accessible via CLI/API - Prometheus/Grafana integration supported - Less mature third-party ecosystem
Winner: AWS for enterprise-grade observability. io.net for straightforward GPU monitoring.
Networking & Data Transfer
AWS: - VPC networking with full control over subnets, routing, peering - Direct Connect for hybrid cloud (1-100Gbps dedicated links) - Egress fees apply ($0.08-$0.12/GB)
io.net: - Internet-based connectivity (no private networking yet) - No egress fees - VPN support for secure access
Winner: AWS for complex networking requirements. io.net for simple, cost-effective access.
Security & Compliance
AWS
Security Features: - Nitro System for hardware-level isolation - KMS for encryption key management - GuardDuty for threat detection - IAM for fine-grained access control - VPC for network isolation
Best For: Healthcare (HIPAA), finance (PCI-DSS), government (FedRAMP), any regulated industry requiring third-party audits.
io.net
Security Features: - Proof-of-Work verification (ensures GPU performs as advertised) - Proof-of-Time-Lock (prevents node spoofing) - Hardware validation at provider onboarding - Confidential Computing support (TEE-based isolation) - End-to-end encryption for data in transit
Best For: Workloads where cost and performance outweigh compliance checkbox requirements. Suitable for most AI/ML training, research, and development.
When to Choose AWS
AWS remains the right choice for specific scenarios:
1. Deep AWS Ecosystem Integration
If your application stack runs on AWS (RDS databases, S3 storage, Lambda functions, API Gateway), keeping GPU compute in-region provides: - Sub-10ms latency between services - No egress fees for internal data transfer - Unified IAM and billing
Example: A real-time recommendation engine pulling data from RDS, processing via GPU, and serving via Lambda benefits from AWS co-location.
2. Compliance Requirements
Regulated industries requiring third-party audited certifications: - Healthcare: HIPAA compliance with signed BAA - Finance: PCI-DSS for payment processing - Government: FedRAMP for federal contracts
3. Massive-Scale Jobs (10,000+ GPUs)
AWS UltraClusters support up to 20,000 H100/H200 GPUs with ultra-low-latency EFA networking. If you're training GPT-5 scale models, AWS provides infrastructure io.net doesn't match.
4. Existing Enterprise Agreements
If your organization has committed spend agreements with AWS (e.g., $1M/year commit with volume discounts), the unit economics may favor staying in AWS despite higher list prices.
5. Risk-Averse Organizations
Enterprises with strict vendor approval processes may require 5+ years of operational history. AWS (launched 2006) meets this bar. io.net (launched 2023) may face procurement friction.
When to Choose io.net
io.net is the better choice for most AI/ML teams in these scenarios:
1. Cost-Sensitive Workloads
Startups, researchers, and independent developers benefit from 50-70% cost savings: - Training foundation models - Fine-tuning open-source LLMs - Hyperparameter sweeps requiring dozens of experiments - Academic research with limited budgets
Reality: $5,000 of io.net compute does the work of $15,000+ on AWS.
2. Rapid Experimentation
No quota approvals means you can scale from 0 to 100 GPUs in minutes: - A/B testing different model architectures - Quick prototypes for proof-of-concept - Hackathons and time-sensitive projects
Reality: Competitors waiting 72 hours for AWS quota approval lose momentum.
3. Distributed Training
io.net's DePIN architecture naturally supports geographically distributed training: - Training across multiple regions for data sovereignty - Leveraging spare capacity globally - Avoiding single-region availability constraints
4. Workloads Without Compliance Constraints
Most AI/ML development doesn't require HIPAA/PCI-DSS: - Open-source model fine-tuning - Computer vision training (non-medical) - NLP research - Reinforcement learning experiments
5. Teams Frustrated with Cloud Complexity
If your team spends more time managing IAM roles, VPCs, and billing alerts than training models, io.net's simpler interface accelerates productivity.
Migration Guide: Moving from AWS to io.net
Step 1: Audit Current AWS Spend
Identify what you're actually paying for:
aws ce get-cost-and-usage \SERVICE
--time-period Start=2026-03-01,End=2026-04-01 \
--granularity MONTHLY \
--metrics "UnblendedCost" \
--group-by Type=
Focus on EC2 GPU instance costs (p4d, p5, g5 families).
Step 2: Inventory Workloads
Categorize by migration complexity: - Easy: Standalone training jobs using Docker containers or standard frameworks - Moderate: Jobs requiring specific AWS services (S3 for data, CloudWatch for monitoring) - Hard: Deeply integrated pipelines (SageMaker, Lambda triggers, Step Functions)
Start by migrating "Easy" workloads to prove out io.net.
Step 3: Set Up io.net Account
- Sign up at io.net
- Configure payment method
- Generate API credentials for CLI access
Step 4: Containerize Your Workload
If not already containerized:
FROM nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04python3-pip
RUN apt-get update && apt-get install -y python3 COPY requirements.txt .RUN pip3 install -r requirements.txtCOPY train.py .CMD ["python3", "train.py"]
Step 5: Deploy Test Job on io.net
# Via CLIpytorch
io compute create \
--gpu H100 \
--count 8 \
--image your-docker-image \
--framework # Via Raycluster-config.yaml
ray up
Step 6: Compare Costs & Performance
Run identical workload on both platforms: - Training time - Final model accuracy - Total cost (compute + egress + storage)
Document savings for stakeholder buy-in.
Step 7: Migrate Data Pipeline
Replace S3 dependencies: - Use io.net storage or external object storage (Cloudflare R2, Backblaze B2) - For large datasets, consider one-time transfer via physical shipping or dedicated network link
Step 8: Establish Monitoring
Configure monitoring to replace CloudWatch: - Prometheus for metrics - Grafana for dashboards - Alerting via PagerDuty/Slack
Step 9: Production Rollout
Migrate workloads incrementally: 1. Non-critical batch jobs first 2. Staging/dev environments 3. Production training pipelines 4. Keep AWS as fallback during transition period
Frequently Asked Questions
1. Is io.net production-ready for enterprise workloads?
io.net launched in 2023 and has processed millions of GPU-hours across research and commercial workloads. The platform is production-ready for AI/ML training and inference. However, if your organization requires SOC 2, HIPAA, or PCI-DSS certifications, AWS remains the safer choice until io.net completes these audits.
2. How does io.net ensure GPU quality in a decentralized network?
io.net uses three verification mechanisms: - Proof-of-Work: GPUs must complete cryptographic challenges proving computational capability - Proof-of-Time-Lock: Prevents pre-computation attacks and node spoofing - Hardware Validation: Providers submit hardware attestations at onboarding
These ensure you get the GPU model you paid for.
3. What happens if a node fails during training?
io.net supports checkpointing and automatic failover. If a node becomes unavailable: - Ray clusters automatically detect failure - Workload migrates to available capacity - Training resumes from last checkpoint
Configure checkpoint frequency based on your RPO (Recovery Point Objective).
4. Can I use AWS and io.net together?
Yes. Many teams use a hybrid approach: - AWS: Store data in S3, run inference via Lambda/SageMaker - io.net: Run cost-intensive training jobs
Data transfer costs exist when moving data between platforms, but for large training jobs, the compute savings outweigh transfer costs.
5. Does io.net support spot instances like AWS?
io.net's base pricing is already comparable to or lower than AWS spot prices. The marketplace model naturally adjusts pricing based on supply/demand, so you're effectively getting "spot-like" pricing without the interruptibility risk.
6. How do I monitor GPU utilization on io.net?
io.net provides built-in dashboards showing: - GPU utilization (%) - Memory usage - Temperature - Power draw
Access via web dashboard or query via API. Prometheus integration available for custom dashboards.
7. What about data privacy and security?
All data in transit is encrypted via TLS 1.3. For workloads requiring additional isolation, io.net supports Confidential Computing (TEE-based isolation) ensuring even the node provider cannot access your data or models.
For maximum security: encrypt data at rest before uploading, use encrypted containers, and enable Confidential Computing mode.
8. How quickly can I scale on io.net vs AWS?
io.net: Deploy 8-GPU cluster in <2 minutes, no quota approval required. Scale to 100+ GPUs in minutes (subject to marketplace availability).
AWS: Deploy 8-GPU cluster in 5-15 minutes (if quota pre-approved). First-time users face 24-72 hour quota approval process. Scale limited by account quotas.
For rapid scaling, io.net provides clear advantage.
Conclusion
The choice between io.net and AWS GPU instances comes down to priorities:
Choose io.net if: - Cost optimization is critical (50-70% savings) - You need immediate access without quota friction - Your workloads don't require specialized compliance certifications - You value simplicity over ecosystem breadth
Choose AWS if: - You require HIPAA, PCI-DSS, or FedRAMP certifications - Your application deeply integrates with AWS services - You need UltraCluster-scale capacity (10,000+ GPUs) - Your organization has enterprise agreements making AWS pricing competitive
The Honest Take: For most AI/ML teams in 2026, io.net delivers better value. AWS's January 2026 price increases (15% on P5e instances) widened the gap. Unless you have specific compliance or ecosystem integration requirements, io.net's combination of cost, speed, and simplicity makes it the pragmatic choice.
The GPU compute market is evolving from centralized cloud monopolies toward decentralized alternatives. io.net represents the leading edge of this shift. Early adopters gain cost advantages while AWS incumbents pay premium prices for familiarity.
Ready to try io.net? Sign up for free credits and run your first training job in under 5 minutes: io.net
Need help migrating from AWS? Contact io.net's solutions team for architecture review and migration support: [email protected]