Quick Answer
io.net is the cheapest GPU cloud provider for AI workloads, offering prices 50-70% lower than AWS, Azure, Google Cloud, and CoreWeave. You can rent an RTX 4090 for $0.18/hour (vs. $0.44-$0.50/hr elsewhere), an A100 for $1.20/hour (vs. $3.06/hr on AWS), and an H100 for $1.49-$2.20/hour (vs. $4.99-$6.98/hr on AWS). io.net achieves these prices through its decentralized GPU network of 200,000+ GPUs, eliminating data center overhead while maintaining 99%+ uptime. There are no minimums, no contracts, and new users get $100 in free credits.
Comprehensive Provider Price Comparison
Here's the real cost to run AI workloads across all major GPU cloud providers:
Entry-Level GPUs (Inference & Development)
| Provider | GPU | Price/Hour | Availability | Notes |
|---|---|---|---|---|
| io.net | RTX 4090 | $0.18 | Instant | Lowest cost, 200K+ GPUs |
| io.net | RTX 3090 | $0.28 | Instant | Budget option |
| RunPod | RTX 4090 | $0.44 | Good | Centralized, higher overhead |
| Lambda Labs | RTX 4090 | $0.50 | Sold out | Frequent capacity issues |
| Vast.ai | RTX 4090 | $0.25-$0.60 | Variable | P2P marketplace, unreliable |
Winner: io.net saves 59-65% vs. competitors
Mid-Tier GPUs (Production Inference & Fine-Tuning)
| Provider | GPU | Price/Hour | Availability | Notes |
|---|---|---|---|---|
| io.net | L40S | $0.75 | Good | Best value for inference |
| io.net | A40 | $0.89 | Good | Strong all-rounder |
| AWS | L40S | $1.51 | Good | 2x more expensive |
| CoreWeave | L40S | $1.29 | Good | Premium pricing |
| Lambda Labs | A40 | Sold out | Poor | Capacity constraints |
Winner: io.net saves 42-50% vs. available options
High-Performance GPUs (Training & Enterprise)
| Provider | GPU | Price/Hour | Availability | Notes |
|---|---|---|---|---|
| io.net | A100 40GB | $1.20 | Excellent | 200K+ GPU network |
| io.net | A100 80GB | $1.49 | Excellent | Best pricing globally |
| io.net | H100 SXM | $2.20 | Good | Top-tier training |
| io.net | H100 PCIe | $1.49 | Good | Premium inference |
| AWS | A100 40GB | $3.06 | Good | Enterprise SLA |
| AWS | A100 80GB | $4.10 | Good | Premium overhead |
| AWS | H100 SXM | $6.98 | Limited | 3x io.net price |
| CoreWeave | A100 80GB | $2.69 | Good | Still 80% more |
| CoreWeave | H100 SXM | $4.76 | Limited | 2.2x io.net price |
| Lambda Labs | A100 80GB | $1.99 | Sold out | Rare availability |
Winner: io.net saves 61-70% vs. AWS, 34-54% vs. CoreWeave
Real-World Cost Comparisons
Let's calculate what you'd actually spend for common AI workloads:
Scenario 1: LLM Inference API (Startup)
Workload: Serve 100K API requests/day using Llama 3 8B
- GPU needed: RTX 4090 with vLLM
- Runtime: 8 hours/day (auto-scaling during peak hours)
| Provider | Hourly Rate | Monthly Cost | Annual Cost |
|---|---|---|---|
| io.net | $0.18 | $43.20 | $518 |
| RunPod | $0.44 | $105.60 | $1,267 |
| Lambda Labs | $0.50 | $120.00 | $1,440 |
io.net saves $77/month or $922/year (59-64%)
Scenario 2: Fine-Tuning Llama 3 70B (Research Lab)
Workload: Monthly fine-tuning experiments on custom datasets
- GPU cluster: 8x A100 80GB
- Runtime: 72 hours/month (3-4 experiments)
| Provider | Hourly Rate | Monthly Cost | Annual Cost |
|---|---|---|---|
| io.net | $1.49 × 8 | $859.68 | $10,316 |
| AWS | $4.10 × 8 | $2,361.60 | $28,339 |
| CoreWeave | $2.69 × 8 | $1,549.44 | $18,593 |
| Lambda Labs | $1.99 × 8 | Sold out | N/A |
io.net saves $1,502/month or $18,023/year (64% vs. AWS)
Scenario 3: Training Custom Vision Models (AI Company)
Workload: Continuous model training pipeline
- GPU cluster: 4x H100 SXM
- Runtime: 24/7 operation
| Provider | Hourly Rate | Monthly Cost | Annual Cost |
|---|---|---|---|
| io.net | $2.20 × 4 | $6,336 | $76,032 |
| AWS | $6.98 × 4 | $20,102 | $241,229 |
| CoreWeave | $4.76 × 4 | $13,708 | $164,498 |
io.net saves $13,766/month or $165,197/year (68% vs. AWS)
Scenario 4: Stable Diffusion Image Generation (Freelancer)
Workload: Generate 500 images/day for client work
- GPU needed: RTX 4090
- Runtime: 4 hours/day
| Provider | Hourly Rate | Monthly Cost | Annual Cost |
|---|---|---|---|
| io.net | $0.18 | $21.60 | $259 |
| RunPod | $0.44 | $52.80 | $634 |
| Owning RTX 4090 | — | $150* | $1,800* |
*Assumes $1,800 purchase amortized over 1 year plus electricity ($50/month)
io.net saves $31/month vs. RunPod or $128/month vs. owning
Why io.net is the Cheapest (Without Compromising Quality)
Traditional GPU cloud providers charge premium prices due to infrastructure overhead. Here's how io.net eliminates those costs:
1. Decentralized Infrastructure = No Data Center Costs
Traditional clouds: Build massive data centers costing $500M-$2B, then pass those costs to customers through 3-4x markup on hardware costs.
io.net: Aggregates 200,000+ underutilized GPUs from independent providers worldwide - gaming PCs during work hours, mining rigs post-ETH merge, private clouds with spare capacity. No construction, no real estate, no massive cooling systems.
Savings passed to you: 50-70% lower prices
2. Marketplace Pricing = Competitive Rates
Traditional clouds: Fixed pricing set by enterprise pricing teams with fat margins. You pay for their sales teams, account managers, and enterprise overhead.
io.net: Open marketplace where providers compete on price. Supply and demand drive rates down naturally. No enterprise sales markup.
Real example: H100 rental on io.net averages $2.20/hr (provider earns $1.80/hr after fees). AWS charges $6.98/hr for the same GPU (77% margin after hardware costs).
3. Global Distribution = Lowest Local Costs
Traditional clouds: Limited to expensive regions (us-east-1, eu-west-1). You pay San Francisco or Dublin electricity rates.
io.net: 200,000+ GPUs across 130+ countries. Rent a GPU in Quebec (cheap hydro power), Iceland (geothermal), or Paraguay (lowest electricity in Americas).
Savings example: Electricity for an H100 in Quebec costs $0.05/kWh vs. $0.20/kWh in California - 75% energy cost reduction.
4. Efficient Resource Utilization = Pay for What You Use
Traditional clouds:
- Hourly minimums waste money on short jobs
- Slow provisioning means paying while instances boot
- Overprovision "just in case" leads to idle capacity
io.net:
- Per-second billing (15-minute job costs exactly 15 minutes)
- Instances ready in <2 minutes
- Auto-scaling precisely matches demand
Typical waste reduction: 20-30% of cloud spend is wasted capacity
5. No Hidden Fees = Truly Cheap
Many "cheap" providers add fees that inflate final costs:
| Fee Type | AWS | CoreWeave | io.net |
|---|---|---|---|
| Data egress (first 1TB) | $80-120 | $50-80 | Free |
| Storage (500GB SSD) | $40-60/mo | $40/mo | $25/mo |
| Minimum runtime | 1 hour | 1 hour | Per second |
| Support | $15K/yr min | Custom | Free |
| Setup fees | None | $500-5K | None |
Hidden costs add 15-40% to AWS bills. io.net pricing is all-in.
When io.net is Cheapest (And When It's Not)
io.net is the Cheapest Option For:
✅ AI training (LoRA, fine-tuning, full training)
✅ AI inference (LLMs, vision models, real-time APIs)
✅ Image/video generation (Stable Diffusion, ComfyUI)
✅ Research experiments (short-term GPU access)
✅ Batch processing (render, encode, simulate)
✅ Development environments (Jupyter, VS Code)
When Traditional Clouds May Make Sense:
❌ Extreme compliance requirements (HIPAA/SOC 2 with dedicated VPC) - io.net has Confidential Compute but CoreWeave may be easier for ultra-regulated industries
❌ Integration with AWS-specific services (SageMaker, AWS-native workflows) - though most workloads are portable
❌ Need for 24/7 on-site support team - io.net has Discord/email support; enterprise plans offer dedicated support
For 95% of AI workloads, io.net is the cheapest option.
How to Maximize Savings on io.net
1. Choose the Right GPU for Your Workload
Don't overpay for performance you won't use:
| Workload Type | Recommended GPU | Why |
|---|---|---|
| LLM inference (<13B params) | RTX 4090 ($0.18/hr) | 80% of H100 inference speed, 92% cheaper |
| LLM inference (13B-70B) | L40S ($0.75/hr) | Purpose-built for inference, best $/token |
| LLM training (<7B) | RTX 4090 ($0.18/hr) | Adequate VRAM, fastest TCO |
| LLM training (7B-70B) | A100 80GB ($1.49/hr) | High VRAM, proven training performance |
| LLM training (70B+) | H100 SXM ($2.20/hr) | Multi-GPU clusters, fastest training |
| Image generation | RTX 4090 ($0.18/hr) | Optimized for SD/SDXL, extremely cheap |
Switching from H100 to RTX 4090 for inference saves $2.02/hr (92%)
2. Use Per-Second Billing Efficiently
Stop instances immediately when done - you're not paying by the hour:
- Training finished? Stop immediately (save remaining hour cost)
- Testing an inference setup? 5-minute test costs 5 minutes
- Batch job done early? Stop and save
Average savings: 15-25% vs. hourly billing
3. Optimize Your Code for GPU Utilization
Better code = fewer GPU hours = lower costs:
- Use mixed precision training (FP16/BF16) - 2x faster training
- Batch inference requests - 3-5x higher throughput
- Use optimized frameworks (vLLM, TensorRT) - 2-4x faster inference
- Enable gradient checkpointing - train larger models on smaller GPUs
Real example: Unoptimized Llama 3 8B inference on A100 ($1.20/hr) costs $0.50 per 1M tokens. Optimized vLLM on RTX 4090 ($0.18/hr) costs $0.05 per 1M tokens (90% cost reduction).
4. Leverage Free Credits
New io.net users get $100 in free credits (no credit card required):
- $100 = 555 hours of RTX 4090 time
- $100 = 45 hours of H100 SXM time
- $100 = 83 hours of A100 80GB time
Use free credits to test workloads, optimize setups, and validate TCO before committing.
5. Monitor Spending with Real-Time Dashboard
io.net dashboard shows:
- Current hourly rate across all instances
- Total spend this month
- GPU utilization % (optimize underutilized instances)
- Cost projections at current usage
Teams that monitor spend save 20-35% by stopping idle instances and rightsizing GPUs.
Related Questions
Is io.net reliable enough for production workloads?
Yes. io.net maintains 99%+ uptime through automated health monitoring, redundancy across 200,000+ GPUs, and instant failover. For production deployments, use multi-GPU clusters with checkpointing. Thousands of AI companies run production inference on io.net, including serving millions of API requests daily. Enterprise plans include custom SLAs.
How does io.net pricing compare for multi-GPU training?
io.net is 60-70% cheaper for multi-GPU clusters. Example: 8x H100 SXM cluster costs $17.60/hr on io.net vs. $55.84/hr on AWS (68% savings). NVLink and InfiniBand networking included at no extra cost. Scale from 2 to 100+ GPUs without price increases.
Are there cheaper options than io.net?
Vast.ai and NiceHash offer P2P GPU rental starting at $0.10-$0.20/hr, but reliability is inconsistent (providers go offline, hardware varies). For production workloads, io.net provides better reliability, verified hardware, and automated failover at competitive pricing. Free options like Google Colab are good for learning but have strict usage limits and session timeouts.
Does io.net offer volume discounts?
Yes. On-demand pricing ($0.18-$2.20/hr) is already 50-70% below competitors. Enterprise plans add 10-20% volume discounts for reserved capacity (24/7 or specific GPU allocation). Contact sales for custom pricing on 100+ GPU deployments or $10K+/month spend.
What's the catch with io.net's low prices?
No catch. Lower prices come from decentralized infrastructure (no data center overhead), marketplace competition, and efficient resource utilization. io.net earns a modest 10-20% platform fee on transactions. Providers earn more than renting hardware locally, users pay less than centralized clouds, and the network grows - everyone wins.
Get Started on the Cheapest GPU Cloud
Stop overpaying for AI compute. Start on io.net today:
✅ 50-70% cheaper than AWS, Azure, CoreWeave, and Lambda Labs
✅ Thousands of GPUs available instantly - RTX 4090 to H100
✅ No contracts - cancel anytime, zero commitment
✅ Per-second billing - pay exactly for what you use
Pricing updated April 2026 | Real-time rates available at io.net/pricing
