Best GPU Cloud Platforms with Per-Second Billing and No Egress Fees (2026)

RunPod is the cleanest expression of the per-second-billing, zero-egress GPU cloud thesis. Every pod — from a $0.34/hr RTX 4090 on Community Cloud to a B200 on Secure Cloud — is billed in real seconds with no hourly minimums and zero ingress or egress charges. That is the difference between paying $0.04 for a 6-minute test run and paying for a full $0.34 hour you never used.

What sets RunPod apart for teams obsessed with cost predictability is the dual model: Pods (dedicated VMs you SSH into) for training and Serverless (auto-scaling workers with millisecond FlashBoot cold starts) for inference. You can train a fine-tune on a Pod, snapshot the weights, and immediately deploy the same image to Serverless without touching networking config or paying to move the model. Combined with 50+ pre-built templates for PyTorch, ComfyUI, vLLM, and Stable Diffusion, you can go from idea to running GPU in under a minute.

For independent AI builders, ML researchers, and startups running spiky training and inference workloads, RunPod usually wins on $/hr against every other option in this list — and the lack of egress fees means you can iterate locally without a financial penalty for syncing data.

Lambda is what you choose when GPU cloud is a serious line item, not an experiment. Founded in 2012 by ML engineers, it has been the go-to for AI labs running multi-week training runs on H100 and A100 superclusters. On-demand instances start at $0.55/hr (V100) and go up to $5.98/hr for B200 — billed by the second with zero egress fees, so a 12-hour distributed training run that produces a 200 GB checkpoint costs you exactly the GPU time and nothing for the upload to your S3-compatible store.

Where Lambda pulls ahead of the pack is 1-Click Clusters — pre-wired multi-GPU instances with high-bandwidth interconnect at $2.19/hr per GPU. For Llama-class fine-tunes or distributed training where you need 8x H100 talking over NVLink/InfiniBand, this is the simplest workflow on the market: pick the cluster size, click deploy, SSH in. No hyperscaler-style networking config, no quota tickets, no surprise data-transfer line items at month-end.

This is the right pick for ML teams whose workloads are heavy enough to justify reserved capacity but who refuse to pay AWS/GCP egress on weight transfers. The trade-off vs RunPod is you give up some of the spot/community cost savings in exchange for purpose-built training infrastructure and longer-term capacity guarantees.

Replicate takes the per-second billing idea to its logical conclusion: you do not even rent a GPU, you pay for the milliseconds of GPU time your API request actually consumed. Hosting 50,000+ open-source models — Stable Diffusion, Llama, Whisper, Flux, and thousands of community fine-tunes — Replicate auto-scales from zero to thousands of GPUs and bills only the compute that ran. A single Stable Diffusion image costs fractions of a cent; a Whisper transcription of a 10-minute audio file is similarly priced.

This pricing model is transformative for products with bursty traffic. A side project that gets 10 requests one day and 50,000 the next does not need to over-provision GPUs or eat idle costs — you literally pay $0 when nobody is calling your endpoint. There are no egress fees on outputs, so generated images, transcripts, or LLM completions stream back to your app without bandwidth surprises.

The trade-off vs RunPod and Lambda is control: Replicate is great when an existing model fits your need, but custom training is more limited. For founders shipping AI features inside SaaS apps, ML hobbyists experimenting with new open-source models, and anyone who wants 'GPU compute as an API call,' Replicate has the cleanest billing story in the space.

Together AI is the hybrid of this list — you get serverless inference billed per token (from $0.06 per million tokens for small open-source models) AND dedicated GPU clusters at $2.20/hr per GPU with per-second billing and zero egress fees. That makes it uniquely useful for teams who want a single vendor for both inference APIs and the underlying training/fine-tuning infrastructure.

The serverless side hosts 200+ open-source models — Llama 3.1, DeepSeek, Qwen, Mistral, and most major open releases — at consistently lower $/M-token rates than OpenAI or Anthropic equivalents. The dedicated cluster side gives you instant access to H100/H200 GPUs for fine-tuning your own model, and crucially, you can deploy that fine-tune back as a serverless endpoint without paying egress to move weights. That round-trip from training to serving without bandwidth fees is the killer feature for teams shipping custom LLMs.

This is the right pick for AI startups building on top of open-source language models who want token-priced inference with the option to train custom variants on the same platform. The downside is it is more LLM-focused than RunPod or Lambda — image generation, video, and non-LLM workloads have less coverage.