Vast.ai vs RunPod vs AWS: GPU Spot Pricing Comparison with Real Data in 2026

Three distinct GPU spot market tiers have emerged. First, hyperscaler spot markets (AWS EC2 Spot, GCP Preemptible, Azure Spot) offering institutional-grade GPU inventory with standard termination risks but maximum scale. Second, dedicated GPU cloud spot pricing (Lambda, CoreWeave, TensorDock) offering provider-managed GPU clusters with minimal termination risk and fixed low pricing. Third, decentralized GPU marketplaces (Vast.ai, RunPod, Akash) where GPU providers are individual datacenter operators or even consumer GPU owners, offering the lowest prices with the highest variability.

The spot GPU market has matured significantly through 2025-2026. AWS Spot H100 capacity now averages 5-12 percent termination rate per hour versus 15-25 percent in 2024. Decentralized platforms like Vast.ai have improved reliability through provider reputation systems and automated failover. The pricing gap between hyperscaler spot and decentralized GPU spot has narrowed from 5-10x in 2024 to 2-4x in 2026, driven by increased provider competition and more efficient GPU utilization on decentralized platforms.

Vast.ai operates the largest GPU marketplace by number of listings, aggregating GPUs from 5,000+ independent providers across 80+ countries. Pricing is set by individual providers based on their own cost structure (electricity, hardware amortization, desired margin), creating a 3-8x price spread for the same GPU type. An H100 80 GB ranges from $0.42/hr from a low-cost Iceland provider to $2.10/hr from a premium US East Coast provider. The median H100 price as of mid-2026 is $0.68/hr, approximately 35 percent below RunPod and 45 percent below AWS spot.

The trade-off for lower pricing is reliability and consistency. Vast.ai's provider quality varies: top-tier providers (10 percent of listings) offer 99.9 percent uptime with pre-loaded Docker images and dedicated NVLink; bottom-tier providers (20 percent of listings) average 97 percent uptime with frequent disconnects and manual restart requirements. Vast.ai's reputation system with DScript-based automated switching helps mitigate this, but teams running training jobs longer than 24 hours should select providers with 500+ completed jobs and 4.5+ star ratings.

Vast.ai offers Interlink networking between compatible providers, achieving 50-200 Gbps inter-node bandwidth (versus 400-3200 Gbps on hyperscaler clusters). This limits multi-node training to models that tolerate asynchronous or high-latency gradient synchronization, making Vast.ai more suitable for hyperparameter sweeps, ensemble training, and inference than coordinated large-scale distributed training.

RunPod operates a curated GPU marketplace with approximately 200 vetted providers and its own dedicated data centers in the US and EU. The pricing premium over Vast.ai (25-40 percent for equivalent GPUs) buys more consistent performance, faster provisioning (30-60 seconds versus 2-10 minutes on Vast), and better tooling including serverless GPU endpoints, private networking, and template-based deployment. RunPod's dedicated infrastructure accounts for 40 percent of its GPU inventory, with the remainder from third-party providers.

RunPod's serverless GPU offering is unique among decentralized platforms: you define an inference endpoint and RunPod manages GPU scaling, load balancing, and cold-start management. Serverless H100 pricing is $0.85/hr with autoscaling from 0 to 32 GPUs. This competes directly with AWS SageMaker serverless GPU ($1.25/GPU/hr) and GCP Cloud Run GPU ($0.95/GPU/hr). The cold-start time is 8-15 seconds (container download + model loading), faster than most serverless GPU offerings.

AWS EC2 Spot GPU pricing in 2026 has stabilized significantly through market maturation and AWS's supply-demand balancing algorithms. P5 spot pricing (H100) averages $1.05-1.35/hr per GPU in us-east-1, with weekend pricing dipping to $0.65-0.85/hr. The hourly termination rate has decreased from 12-18 percent in 2024 to 5-12 percent in 2026 for P5 instances. AWS capacity pools are now segmented by GPU generation, preventing A100 capacity fluctuations from affecting H100 pricing.

AWS's Spot Fleet with diversified allocation across availability zones achieves <2 percent total termination rate for multi-AZ deployments. Combined with checkpointing at 10-15 minute intervals, effective training GPU cost with spot reaches $1.20-1.50/hr per H100 including checkpoint restarts. This is 1.5-2x Vast.ai median pricing but with guaranteed 1600-3200 Gbps EFA networking, consistent performance, and no provider vetting overhead.

The decentralized platforms versus hyperscaler spot decision depends on workload characteristics. For single-GPU inference and short training runs (<12 hours), Vast.ai offers the lowest absolute cost: a one-hour H100 inference test costs $0.42-0.68 on Vast.ai versus $1.05-1.35 on AWS spot versus $2.50 on Lambda on-demand. For multi-node training runs requiring coordinated gradient synchronization across 8+ GPUs, AWS spot or RunPod dedicated are the minimum viable options due to networking requirements.

For batch inference workloads that can tolerate 5-10 percent failure rate with retry, Vast.ai's cheapest tier provides the lowest per-inference cost. For production inference with SLAs, AWS P5 spot with multi-AZ diversity or RunPod serverless GPU provide acceptable reliability at 2-4x the cost. The overall market trend is converging: Vast.ai is improving reliability (adding dedicated provider tiers with SLA guarantees), while AWS and RunPod are reducing spot GPU pricing to compete with decentralized alternatives.

The effective GPU cost comparison must account for platform-specific overhead. Vast.ai charges $0.08/GB-month for persistent storage (block storage) and $0.01/GB for data transfer between providers. RunPod includes 50 GB persistent storage per instance with additional storage at $0.10/GB-month. AWS charges $0.08/GB-month for gp3 EBS volumes plus $0.05-0.09/GB for data egress to internet. A training run with 500 GB dataset and 50 GB output produces $50-90 in data costs on AWS, $5-10 on RunPod, and variable costs on Vast.ai depending on provider proximity.

Setup overhead also differs significantly. Vast.ai requires Docker containerization (one-time setup, 1-3 hours for team onboarding). RunPod offers pre-built templates for PyTorch, TensorFlow, and popular model repositories (zero setup for common workloads). AWS requires AMI creation, VPC configuration, and security group management (2-8 hours initial setup). The setup cost differential is approximately $5-20 per team member in GPU time equivalent.