Best GPU Clouds of 2026

Together AI is the open-source model inference platform for teams running Llama, Mixtral, Qwen, and other open-source models without managing GPU infrastructure. Founded in 2022 in San Francisco, Together AI positions around the inference-as-a-service shape with 200+ open-source models plus pay-per-token pricing.

Four tiers serve four buyer profiles. Free ships $5 credits with 200+ models plus inference API plus fine-tuning. Pay-as-you-go ships at $0.10-$0.90 per 1M tokens plus H100 instances at $1.49+/hr. Pro ships at $200 monthly plus usage with priority queue. Enterprise ships custom with reserved H100 plus SOC 2 plus HIPAA available.

The load-bearing wedge is the open-source model inference shape. Where Lambda, CoreWeave, and Modal target raw GPU compute, Together AI targets the use case where teams want to run Llama 3 or Mixtral without provisioning GPUs. The pay-per-token pricing makes inference economics simpler than per-hour GPU costs. The catch is the model-specific pricing variance. For teams running open-source models without infrastructure, Together AI Pay-as-you-go covers the use case better than Lambda raw GPU.

Replicate is the model marketplace plus deployment platform for teams running open-source models without infrastructure overhead. Founded in 2019 in San Francisco and backed by Andreessen Horowitz, Replicate positions around the marketplace shape where developers run models from a public catalog or deploy custom models via Cog framework.

Four tiers serve four buyer profiles. Free ships trial credits with pay-per-second model API. Pay-as-you-go ships at $0.000725 per second on A100 with no monthly minimum plus Cog deployment. Team at $200 monthly with private models. Enterprise ships custom with dedicated GPU instances plus SOC 2.

The load-bearing wedge is the marketplace plus Cog framework. Where Lambda and CoreWeave target raw GPU compute, Replicate targets the model-as-a-service shape where teams deploy and run models without managing GPU infrastructure. The Cog framework simplifies model packaging. The catch is the per-second pricing variance. Heavy usage on Replicate can exceed dedicated GPU costs; for steady production, Lambda or CoreWeave reserved cover better. For teams running open-source models without infrastructure overhead, Replicate Pay-as-you-go is the cheapest entry path.

Modal is the serverless GPU functions platform for developer-friendly auto-scaling workloads. Founded in 2021 in San Francisco by ex-Spotify engineer Erik Bernhardsson, Modal positions around the serverless GPU model where functions scale from zero to thousands of GPUs based on incoming traffic.

Four tiers serve four buyer profiles. The Free tier ships $30 monthly free credits with serverless GPU functions plus pay-as-you-go after credits. The Starter tier ships at $30 monthly with $1.10/hr A10G plus $2.78/hr A100 80GB plus auto-scaling cold-start. The Team tier ships at $250 monthly plus usage with higher concurrency limits plus Slack support. The Enterprise tier ships custom contract with reserved GPU instances plus dedicated CSM.

The load-bearing wedge is the serverless plus dev-friendly shape. Where Lambda, CoreWeave, and RunPod target persistent GPU instances, Modal targets variable bursty workloads where GPUs scale to zero between requests. For inference workloads with variable traffic (RAG, image generation APIs, batch processing), Modal eliminates idle GPU cost entirely. The catch is the cold-start latency. First request to an idle endpoint adds 5-30 seconds; for latency-sensitive workloads, persistent instances (Lambda, RunPod) cover better. For variable bursty inference workloads, Modal Starter at $30/mo plus per-second usage is the cheapest path.

RunPod is the community GPU cloud platform for teams wanting cheap H100 access plus serverless endpoints. Founded in 2022, RunPod positions between Lambda's mainstream pricing and Vast.ai's bid-based marketplace with two-tier offering: Secure Cloud (datacenter-grade) plus Community Cloud (lower-cost spot).

Four tiers serve four buyer profiles. The Community Free tier ships free credits with serverless endpoints plus community templates. The Secure Cloud A100 tier ships at $1.89/hr A100 80GB with persistent volumes plus public network IP option. The Community Cloud H100 tier ships at $1.99/hr H100 80GB community spot tier; less reliable than Secure but the cheapest H100 available. The Reserved tier ships custom multi-month with ~30 percent off on-demand.

The load-bearing wedge is the cheap H100 community spot tier. Where Lambda H100 is $2.49/hr and CoreWeave H100 is $3.49/hr, RunPod Community Cloud H100 at $1.99/hr is the cheapest H100 access for fault-tolerant workloads. For teams iterating on LLM fine-tuning or stable diffusion training where checkpoints save work between interruptions, RunPod Community covers the use case at significant cost savings. The catch is the community-tier reliability. For production inference, Secure Cloud A100 at $1.89/hr is competitive with Lambda.

Lambda Labs is the default GPU cloud for most paid ML teams. Founded in 2012 in San Francisco by brothers Stephen and Michael Balaban, Lambda serves the largest mainstream GPU cloud market with the deepest brand recognition for ML training plus inference.

Four tiers serve four buyer profiles. The On-Demand 1x A100 tier ships at $1.29/hr A100 40GB with pay-per-minute billing plus persistent storage. The On-Demand H100 tier ships at $2.49/hr H100 80GB SXM5 with cloud and 1-Click Clusters. The 1-Click Cluster tier ships custom contract for 16-1024 GPU clusters with InfiniBand networking. The Reserved tier ships custom multi-year reservation with up to 50 percent discount versus on-demand.

The load-bearing wedge is mainstream brand recognition plus the historic on-demand baseline. Lambda set the $1.29/hr A100 baseline that competitors (CoreWeave, RunPod) anchor against. The catch is the no-free-tier model. Where Modal, Replicate, and Vast.ai offer free credits or trials, Lambda requires immediate billing. For mainstream production ML teams wanting reliable on-demand A100 plus H100 with reserved discounts at scale, Lambda Labs covers the use case better than CoreWeave at lower entry friction or RunPod at higher reliability.

CoreWeave is the Kubernetes-native enterprise GPU cloud for large-scale ML training. Founded in 2017 in New Jersey and IPO'd in March 2025 (the largest tech IPO of 2025), CoreWeave serves the enterprise GPU market with deep Kubernetes integration plus NVLink plus InfiniBand for multi-node distributed training.

Four tiers serve four buyer profiles. The On-Demand A100 tier ships at ~$2.39/hr A100 80GB SXM4 with Kubernetes-native object storage plus networking. The On-Demand H100 tier ships at ~$3.49/hr H100 80GB SXM5 with NVLink plus InfiniBand plus bare-metal options. The Reserved 1-year tier ships ~25-40 percent off on-demand with reserved A100/H100 pool. The Enterprise tier ships custom multi-year with reserved clusters plus on-prem hybrid.

The load-bearing wedge is the Kubernetes-native plus NVLink shape. Where Lambda targets single-node on-demand and Modal targets serverless, CoreWeave targets multi-node distributed training where InfiniBand networking is load-bearing for gradient synchronization at scale. For training large language models or computer vision at scale, CoreWeave plus reserved capacity covers the use case better than other cloud GPU providers. The catch is the institutional pricing. On-demand is more expensive than Lambda; reserved requires multi-year commitment. For enterprise ML teams running multi-node distributed training, CoreWeave is the historic gold standard.

Vast.ai is the decentralized GPU marketplace for budget-conscious researchers and fault-tolerant workloads. Founded in 2018 in San Francisco, Vast.ai positions around the marketplace shape where individual hosts (datacenter operators, crypto miners pivoting to AI, individual GPU owners) list capacity at bid-based prices.

Three tiers serve three buyer profiles. The Interruptible RTX 4090 tier ships at ~$0.18/hr with the lowest cost in this lineup; instances may be reclaimed if outbid. The On-Demand A100 tier ships at ~$0.79/hr with bid-based marketplace pricing plus persistent volume option. The Reserved H100 tier ships at ~$1.65/hr with datacenter-tier hosts plus verified-host filtering.

The load-bearing wedge is the cheapest GPU access. Where Lambda, CoreWeave, and Modal price A100 at $1.29-$2.78/hr, Vast.ai prices A100 at ~$0.79/hr (40-70 percent cheaper). For fault-tolerant research workloads (model fine-tuning with checkpoints, hyperparameter sweeps, batch inference), Vast.ai cuts costs dramatically. The catch is the reliability variance. Hosts vary in quality; verified-host filtering helps but does not eliminate risk. For budget-conscious researchers running fault-tolerant workloads, Vast.ai Interruptible at $0.18/hr is the cheapest path; for higher reliability needs, RunPod Secure or Lambda cover better.