Quick Answer
io.net maintains 99%+ uptime across its decentralized GPU network with automated health monitoring and instant failover. While on-demand GPU instances don't include a formal SLA (similar to AWS spot instances), io.net's decentralized architecture provides inherently higher reliability through redundancy across 200,000+ GPUs. Enterprise customers can access custom SLAs with 99.5-99.9% uptime guarantees, dedicated support, and financial credits for any downtime. Unlike traditional cloud providers that charge 30-50% premiums for SLA-backed instances, io.net's base pricing already reflects decentralized reliability.
Understanding Cloud GPU SLA Models
Service Level Agreements (SLAs) define the minimum uptime percentage a provider guarantees and the compensation if they fail to meet it.
Standard SLA Tiers:
- 99.0% uptime: 7.2 hours/month downtime - Suitable for development and testing
- 99.5% uptime: 3.6 hours/month downtime - Production workloads with some fault tolerance
- 99.9% uptime: 43 minutes/month downtime - Business-critical applications
- 99.99% uptime: 4.3 minutes/month downtime - Mission-critical infrastructure
io.net Uptime Performance:
- On-demand GPUs: 99%+ observed uptime (no formal SLA)
- Enterprise reserved capacity: 99.5-99.9% guaranteed uptime with SLA
- Average unplanned downtime: <2 hours/month per GPU
- Network-wide availability: 99.7% across all GPU types (measured over 12 months)
How io.net Achieves High Reliability Without Traditional Infrastructure
Traditional cloud providers achieve uptime through expensive redundant data centers. io.net uses decentralization:
1. Distributed Redundancy:
With 200,000+ GPUs across 130+ countries, individual GPU failures don't impact overall availability. If one GPU goes offline, thousands of alternatives remain available instantly.
2. Automated Health Monitoring:
Every GPU undergoes health checks every 6 hours:
- Memory integrity tests
- Compute benchmark validation (TFLOPS verification)
- Network connectivity checks
- Temperature and power stability monitoring
GPUs that fail health checks are automatically removed from the marketplace within 15 minutes.
3. Instant Failover:
If a GPU fails during your job:
- Checkpoint detection: io.net detects the failure within 60 seconds
- Automatic migration: Your workload migrates to an equivalent GPU in the same region
- Data persistence: Attached storage volumes transfer automatically
- Refund policy: You receive credits for any compute time during downtime
4. Provider Reputation System:
GPU providers are rated based on:
- Uptime percentage: Last 30 days of availability
- Performance consistency: Benchmark deviation from expected performance
- Response time: How quickly issues are resolved
You can filter GPUs by provider reputation score (1-5 stars). 5-star providers have 99.8%+ uptime.
Comparing io.net Uptime to Traditional Cloud Providers
| Provider | SLA (Standard) | SLA (Premium) | Actual Uptime | Premium Cost |
|---|---|---|---|---|
| io.net | No formal SLA | 99.5-99.9% (enterprise) | 99%+ observed | Included in base price |
| AWS EC2 | No SLA (spot) | 99.99% (on-demand) | 99.95% | 3-5x spot pricing |
| Google Cloud | No SLA (preemptible) | 99.95% | 99.93% | 3-4x preemptible pricing |
| Azure | No SLA (spot) | 99.9% | 99.91% | 3-5x spot pricing |
| CoreWeave | 99.9% | 99.95% (enterprise) | 99.89% | Already premium priced |
Key Insight: io.net's on-demand pricing ($0.18-$2.20/hr) delivers 99%+ uptime at prices comparable to AWS spot instances (which have NO uptime guarantee and can be terminated with 2-minute notice). This makes io.net the best value for reliability.
Uptime Performance by GPU Type
Different GPU tiers have different availability patterns:
| GPU Type | Average Uptime | Provider Count | Failover Speed |
|---|---|---|---|
| RTX 4090 | 99.5% | 28,000+ providers | <90 seconds |
| RTX 3090/3090 Ti | 99.3% | 15,000+ providers | <90 seconds |
| A100 80GB/40GB | 99.4% | 9,000+ providers | <2 minutes |
| H100 SXM/PCIe | 99.1% | 1,500+ providers | <3 minutes |
| L40S | 99.2% | 2,000+ providers | <2 minutes |
Why H100 has slightly lower uptime:
- Fewer total providers (1,500 vs 28,000 for RTX 4090)
- Higher performance GPUs are often in active use, so individual failures are more noticeable
- Still 99.1% is excellent for on-demand pricing with no SLA premium
What Happens When a GPU Fails
io.net's automated recovery process:
Step 1: Failure Detection (0-60 seconds)
- Monitoring system detects GPU unresponsive or failing health checks
- Workload is immediately flagged for migration
Step 2: Workload Migration (60-180 seconds)
- System identifies equivalent GPU in same region with similar specs
- Persistent volumes are attached to new GPU
- Checkpoint (if available) is restored
Step 3: Recovery Verification (180-240 seconds)
- New GPU passes health check
- Workload resumes from last checkpoint or restarts
- User is notified of migration (optional)
Step 4: Refund Processing (automatic)
- Downtime is calculated (time between failure and successful restart)
- Credits are automatically applied to your account
- Original GPU provider is flagged for review
Total recovery time: 2-4 minutes for most workloads
For long-running training jobs, we recommend using checkpointing every 30-60 minutes to minimize recovery time.
Enterprise SLA Options
For mission-critical workloads requiring guaranteed uptime:
Standard Enterprise SLA (99.5% uptime):
- Included: Dedicated account manager, 24/7 support, priority provisioning
- Credits: 5% of monthly spend refunded for every 0.1% below SLA
- Minimum commitment: $5,000/month or 10 GPUs for 3 months
- Pricing: Same as on-demand (no SLA premium)
Premium Enterprise SLA (99.9% uptime):
- Included: Everything in Standard + reserved capacity pool, custom monitoring dashboard
- Credits: 10% of monthly spend refunded for every 0.1% below SLA
- Minimum commitment: $15,000/month or 25 GPUs for 6 months
- Pricing: +5-10% above on-demand rates
Mission-Critical SLA (99.95% uptime):
- Included: Dedicated infrastructure, multi-region failover, custom SLA terms
- Credits: 25% of monthly spend refunded for any SLA breach
- Minimum commitment: Custom (typically $50,000+/month)
- Pricing: Custom based on requirements
Contact [email protected] for custom SLA pricing.
Maximizing Uptime for Your Workloads
Best practices to achieve 99.9%+ effective uptime:
1. Implement Checkpointing:
# Save model checkpoints every 30 minutes during training
import torch
def train_with_checkpoints(model, dataloader):
checkpoint_interval = 1800 # 30 minutes in seconds
last_checkpoint = time.time()
for epoch in range(epochs):
for batch in dataloader:
# Training logic
if time.time() - last_checkpoint > checkpoint_interval:
torch.save({
'epoch': epoch,
'model_state': model.state_dict(),
'optimizer_state': optimizer.state_dict(),
}, f'checkpoint_epoch{epoch}.pt')
last_checkpoint = time.time()
2. Use Multi-GPU Redundancy:
For critical inference workloads, deploy across 2-3 GPUs with load balancing. If one fails, traffic automatically routes to healthy GPUs.
3. Select High-Reputation Providers:
Filter for 5-star providers when provisioning:
io launch --gpu H100 --provider-rating 5 --region us-west
4. Monitor in Real-Time:
Set up monitoring alerts for GPU health:
# Get notified if GPU utilization drops below 50% (potential failure)
io monitor --gpu-id <gpu-id> --alert-threshold 50 --notify [email protected]
5. Use Persistent Storage:
Always attach persistent volumes for data and checkpoints:
io launch --gpu A100 --storage 500GB --storage-type persistent
SLA Credits and Refund Policy
How io.net compensates for downtime:
Unplanned Downtime (GPU failure):
- Automatic credits: 100% of compute cost during downtime
- No action required: Credits appear in your account within 24 hours
- Additional compensation: If downtime exceeds 1 hour, receive 2x credits
Planned Maintenance (rare):
- Advance notice: 7 days for scheduled maintenance
- Migration assistance: Free migration to equivalent GPU
- No credits: Planned maintenance doesn't count against SLA
Network Issues:
- If network connectivity fails (not your application), you receive credits for affected time
- Credits calculated based on GPU cost during outage
Example Credit Calculation:
- H100 at $2.20/hr experiences 30 minutes of downtime
- Credit: $2.20 × 0.5 hours = $1.10
- If downtime exceeded 1 hour: 2x credit = $2.20
Monitoring Your GPU Uptime
Track reliability in real-time:
Dashboard Metrics:
- Current uptime: Running time since last restart
- Historical uptime: Uptime percentage over last 7/30/90 days
- Failure count: Number of unexpected restarts
- Provider rating: Current provider's reliability score
API Access:
# Get uptime stats for your GPU instances
curl -X GET https://api.io.net/v1/instances/<instance-id>/uptime \
-H "Authorization: Bearer <your-api-key>"
# Response:
{
"instance_id": "gpu-h100-abc123",
"uptime_percentage_30d": 99.6,
"total_downtime_minutes": 172,
"last_failure": "2026-04-15T08:23:00Z",
"provider_rating": 4.8
}
Why io.net Doesn't Charge SLA Premiums
Traditional cloud providers charge 3-5x more for instances with SLA guarantees. io.net's decentralized model makes high uptime the default:
Traditional Cloud SLA Economics:
- Build redundant data centers in multiple availability zones
- Maintain spare capacity (20-30% overhead)
- Pass infrastructure costs to customers via SLA premiums
io.net Decentralized Economics:
- Redundancy is inherent (200,000 GPUs globally)
- No need to build spare capacity (idle GPUs become available automatically)
- Savings passed to customers (99%+ uptime at spot-like pricing)
Result: You get better reliability at lower cost.
Related Questions
What happens to my data if a GPU fails?
All data on persistent storage volumes is preserved during GPU failures. When io.net migrates your workload to a new GPU, attached volumes transfer automatically. Ephemeral storage (local SSD) is lost on failure, which is why we recommend persistent volumes for important data. Checkpoints saved to persistent storage allow training jobs to resume from the last saved state, typically with <5 minutes of lost progress.
Can I get an SLA for spot-like pricing?
io.net's base on-demand pricing ($0.18-$2.20/hr) already delivers 99%+ uptime without SLA guarantees - better than AWS/Azure spot instances that can be terminated anytime with zero uptime guarantee. For formal SLA with financial credits, enterprise plans start at $5,000/month but use the same competitive per-GPU pricing. This is fundamentally different from traditional clouds that charge 3-5x premiums for SLA-backed instances.
How does io.net handle provider failures?
If an entire provider (data center, individual operator) goes offline, all their GPUs are immediately marked unavailable. Your workload is automatically migrated to a GPU from a different provider in the same region. The failed provider's reputation score drops, and they must pass recertification health checks before rejoining the network. You never interact with providers directly - io.net handles all reliability management.
Does io.net support multi-region deployments for higher uptime?
Yes. You can deploy identical workloads across multiple regions (US-West, EU-West, APAC) with automatic failover. For example, deploy inference endpoints in 2-3 regions with global load balancing. If one region has an outage, traffic routes to healthy regions automatically. This achieves 99.95%+ effective uptime. Contact [email protected] for multi-region deployment architecture support.
What uptime can I expect for long-running training jobs?
For training jobs running 48+ hours, expect 99%+ uptime on high-reputation providers. Use checkpointing every 30-60 minutes to minimize lost progress if failures occur. The decentralized model means individual GPU failures don't require restarting from scratch - io.net migrates to a new GPU and resumes from your last checkpoint. Most teams experience <2% total overhead from failures over multi-day training runs.
Get Started with Reliable GPU Compute
Experience 99%+ uptime without SLA premiums:
- Decentralized reliability - Thousand of globally distributed GPUs ensure instant failover
- Automatic recovery - Workloads migrate to healthy GPUs in <3 minutes
- Credits for downtime - 100% refund for any unplanned outages
- Enterprise SLAs available - 99.9% guaranteed uptime for mission-critical workloads
View uptime stats → or start deploying →
Last updated: April 2026 | Uptime statistics based on 12-month rolling average across all GPU types
