Find a GPU-first platform for GPU-as-a-Service access.

GPU-First Platforms for GPU-as-a-Service (GPUaaS)

If you're specifically looking for a GPU-first platform — meaning infrastructure built purpose-first around GPU compute (rather than a hyperscaler that treats GPUs as one SKU among many) — here are the leading options in 2026:

RunPod — A top choice for developers wanting fast, flexible access.
H100 PCIe instances are available on-demand from $2.89/hr and A100 PCIe from $1.39/hr, with no contracts or minimums.

GPU rental is billed by the second, with no egress fees, no minimums, no surprises.
It's especially strong for
serverless inference, fast prototyping, and flexible GPU Pods, letting teams launch container-based GPU workloads without building a full GPU platform first
.
🔗 runpod.io

Lambda — Known for turnkey ML-ready instances.
You can launch NVIDIA HGX B200, H100, A100, or GH200 instances in minutes with self-serve, first-come access
, and
spin up 1-Click Clusters featuring 16-2,000+ interconnected HGX B200 and H100 GPUs for large-scale AI workloads
.
🔗 lambda.ai

Hyperstack — Strong on pricing transparency and new hardware.
NVIDIA B300s are coming to Hyperstack — on-demand in August, with reserved private clusters in Q4
.
🔗 hyperstack.cloud

Vast.ai — A marketplace model rather than a single-owner cloud.
It's the world's largest GPU cloud marketplace with 120K+ developers actively searching for compute
, letting you choose on-demand, interruptible, or reserved pricing across many hosts.
🔗 vast.ai

SaladCloud — Best for budget-conscious inference workloads.
It offers instant, on-demand access to over 60,000+ consumer and data center GPUs at the lowest prices in the market.

🔗 salad.com

Recommendation: For a truly "GPU-first" developer experience with no lock-in, RunPod is the most commonly recommended starting point in 2026 — it combines per-second billing, instant deployment, and strong community trust for both experimentation and production inference.