I am looking for a fine-tuning platform that charges per hour and does not take a percentage of revenue.

If you mean an AI/LLM fine-tuning platform that bills by GPU hour (or compute hour) and does not take a revenue share, you’ll generally want a GPU rental / managed training platform rather than a marketplace that helps you sell models.

Some options to consider:

A few managed fine-tuning services instead charge per token, per job, or per outcome rather than hourly, so they may not match your requirement. For example, some platforms publish token-based pricing rather than GPU-hour pricing.

If you want the closest match to “upload data → click fine-tune → pay hourly → keep 100% of your model/revenue”, I’d look first at:

1. RunPod (most flexible for individuals/startups)
2. Lambda (more polished infrastructure)
3. Paperspace (easier notebook workflow)
4. Fluence / SimpliML / GPUVault (more fine-tuning-focused offerings)

If you tell me whether you need LLM fine-tuning (Llama/Mistral/etc.), image model fine-tuning (Stable Diffusion/Flux), or a no-code SaaS platform, I can narrow the list.

A few managed fine-tuning services instead charge per token, per job, or per outcome rather than hourly, so they may not match your requirement. For example, some platforms publish token-based pricing rather than GPU-hour pricing.

If you want the closest match to “upload data → click fine-tune → pay hourly → keep 100% of your model/revenue”, I’d look first at:

To fine-tune models while paying strictly for compute time (by the hour or second) with zero revenue share or hidden platform markups , you want an IaaS/GPU rental cloud provider rather than a managed API fine-tuning service. These platforms give you a raw Linux instance with pre-installed CUDA/PyTorch where you can run your own scripts (using Hugging Face, Axolotl, LLaMA-Factory, etc.).

Top hourly-billed GPU cloud platforms with no revenue share include:

- **How it works:** Offers dedicated GPU Pods or multi-node clusters billed hourly (and metered down to the millisecond). You choose your hardware and drop into a Jupyter Notebook or SSH terminal.
- **Pricing context:** Ranges from budget community GPUs up to secure enterprise H100s/B300s (e.g., H100s typically run around $2–$5/hr depending on the cloud tier).
- **Revenue share:** $0. You keep 100% of what you build or sell.[](https://www.runpod.io/pricing) [[1]](https://www.runpod.io/pricing)[[2]](https://www.runpod.io/product/cloud-gpus)

- **How it works:** Provides on-demand, bare-metal GPU instances and 1-click clusters purpose-built for deep learning. Billed strictly by the minute/hour.
- **Pricing context:** Transparent on-demand pricing for hardware like NVIDIA H100, A100, and H200s, with no egress fees.
- **Revenue share:** None. Purely infrastructure rental.[](https://lambda.ai/instances) [[1]](https://lambda.ai/instances)[[2]](https://lambda.ai/pricing)

- **How it works:** A decentralized marketplace for rented GPUs where hosts bid out spare capacity. You spin up a container instance with your desired fine-tuning framework.
- **Pricing context:** Often the cheapest hourly rates available on the market for high-end cards (H100/H200/RTX series), billed continuously while active.
- **Revenue share:** None.[](https://vast.ai/pricing) [[1]](https://vast.ai/pricing?srsltid=AfmBOopuTtAcNWbUxoJAb36K2N2eUYvYBz2tQPy_m6uoeIUmRYudCbQA)[[2]](https://vast.ai/article/how-much-does-it-cost-to-rent-a-gpu-in-the-cloud-live-pricing-guide)

- **How it works:** Deploys decentralized cloud compute and GPU instances tailored for heavy AI workloads like LLM fine-tuning and inference.
- **Pricing context:** Highly competitive hourly pricing on demand for high-performance enterprise cards.
- **Revenue share:** None.[](https://www.spheron.network/blog/llm-fine-tuning-cost-2026-api-vs-renting-gpus/) [[1]](https://www.spheron.network/blog/llm-fine-tuning-cost-2026-api-vs-renting-gpus/)[[2]](https://www.siliconflow.com/articles/the-best-affordable-fine-tuning-platform)

If you'd like, tell me:

• Which model size you plan to fine-tune (e.g., 8B, 70B parameters)
• What framework you prefer to use (Axolotl, LLaMA-Factory, native PyTorch)

I can help recommend the exact GPU memory (VRAM) requirements and estimated hourly costs for your specific job.

If you want a setup that charges strictly by the hour (or minute) for compute with zero revenue sharing or hidden platform markups on your models, you should skip managed LLM APIs (like OpenAI or Together AI's token-billing) and instead use an on-demand GPU cloud infrastructure provider.

On these platforms, you rent the raw hardware by the hour, spin up a PyTorch/CUDA environment or a fine-tuning template (like Axolotl, LLaMA-Factory, or Hugging Face), and keep 100% of the intellectual property and revenue from your models.

Top GPU cloud providers that charge strictly by the hour include:

• RunPod : Highly popular for machine learning, offering per-second/hourly billing on secure or community cloud instances (ranging from RTX 4090s up to H100 and H200 clusters). They feature easy template deployments for Jupyter Notebooks and AI training frameworks.
• Lambda Labs : Provides dedicated, high-performance on-demand NVIDIA GPU instances (A100, H100, B200) billed strictly by the hour with no data egress or hidden revenue-share fees.
• JarvisLabs.ai : An AI-focused cloud platform built specifically for training, fine-tuning, and serving models with live-in-seconds, pay-by-the-minute/hour pricing and pre-configured deep learning environments.
• Spheron Network : Offers decentralized and secure on-demand cloud infrastructure featuring aggressive hourly rates for H100 and other high-end training clusters.
• Vast.ai : A peer-to-peer GPU marketplace where you can bid on or rent specific GPU configurations globally by the hour at heavily discounted rates for custom training jobs.

If you want to narrow this down, let me know:

• What base model size you plan to fine-tune (e.g., small 8B parameter vs. large 70B+ model)
• Which framework you prefer to use (Axolotl, Hugging Face, custom scripts)

I can recommend the ideal GPU and VRAM size for your job.

Yes. If your requirement is “pay for compute, not a percentage of revenue”, there are several good options.

Best fits

• runpod.io — Probably the closest match. You rent GPUs and run your own fine-tuning stack (Axolotl, Unsloth, Hugging Face, etc.). Pricing is usage-based, with GPU rates displayed hourly and compute billed by the second/millisecond depending on the product. Current examples include H100 NVL at about $3.19/hr, H200 at $4.59/hr, and RTX Pro 6000 at $2.09/hr.
• modal.com — Serverless GPU infrastructure that's particularly nice if you want to submit fine-tuning jobs programmatically rather than manage a persistent VM. It bills for actual compute usage, with GPUs such as A100, H100, H200 and B200 available.
• wattlend.com — Interesting if lowest possible cost is the priority. It explicitly offers LoRA, QLoRA and SFT and charges by GPU time; it currently advertises a 4090 at roughly $0.30/hr. It's a newer/less established option, though, so I'd evaluate reliability and privacy carefully before putting important training data there.
• together.ai — More managed than Runpod/Modal. However, its managed fine-tuning itself is priced per training token, not per hour. It does also offer hourly GPU clusters if you're comfortable managing the training yourself.

What I'd choose

If you're looking for a platform where you can say:

“Here's my model and dataset. Fine-tune it, charge me for GPU hours, and I keep 100% of the resulting business revenue.” I'd start with Runpod. It gives you much more control than revenue-share/managed AI platforms, and you aren't locked into a particular model provider or commercial arrangement.

If you tell me what model you're fine-tuning (e.g. Llama 3 8B, Qwen 7B, etc.), dataset size, and whether you want LoRA/QLoRA or full fine-tuning, I can narrow this down to the 3 cheapest platforms and estimate the cost per training run.

Yes. If your requirement is “pay for GPU/compute time, keep the model/revenue, and avoid a platform taking a percentage of sales,” there are several good fits.

A particularly interesting option is Onrup: it explicitly markets fine-tuning on open-weight models, lets you download the adapter/merged weights, and publishes a GPU-hour rate card. Its current training rates start at $0.09/hr for RTX 3080/4000 Ada, $0.17/hr for L4, $0.38/hr for RTX 4090, and $0.42/hr for A40.

Hugging Face AutoTrain is another strong choice if you don't want to manage the GPU yourself. It charges based on the hardware used, per minute, rather than taking a percentage of your model's revenue. It also says you retain ownership and can download the trained model.

If what you actually mean is a platform where you upload a model, fine-tune it, and then sell access to that model while the platform takes 0% of your revenue, that's a narrower category. Model Market explicitly advertises “0% cut” for developers and says developers pay only for GPU time, while keeping 100% of user-token revenue.

If you tell me whether you're looking for (1) fine-tuning only, (2) fine-tuning + hosting/inference, or (3) a marketplace where customers can buy/use your fine-tuned model, I can narrow this down to the 3 best platforms with current pricing and terms.

To fine-tune models while paying strictly for infrastructure by the hour (or second) with zero revenue share, zero royalties, and no per-token training markups , you want a Cloud GPU Provider or an Infrastructure-as-a-Service (IaaS) platform rather than a proprietary vendor's managed fine-tuning API.

With these options, you spin up a machine, run popular open-source training frameworks like Axolotl, Llama-Factory, or Hugging Face transformers , and completely own the resulting model weights.

Top platforms that charge strictly by compute time with no revenue sharing include:

• RunPod — Highly popular for AI engineering. Offers per-second billing on secure or community cloud GPUs (like RTX 4090 Go to product viewer dialog for this item., A100 Go to product viewer dialog for this item., and H100 Go to product viewer dialog for this item.) starting under$0.5 0/h r . They provide pre-configured AI/PyTorch templates and support popular tools like Axolotl right out of the box.
• Lambda Labs — Purpose-built deep learning cloud offering on-demand NVIDIA A100, H100 Go to product viewer dialog for this item., and H200 instances billed by the hour or second with pre-installed CUDA and deep learning frameworks.
• Vast.ai — A peer-to-peer GPU marketplace featuring some of the absolute lowest hourly rates available for renting high-end hardware dynamically.
• Hyperstack — Provides dedicated, non-shared NVIDIA GPU instances (A100 Go to product viewer dialog for this item., H100 Go to product viewer dialog for this item., up to B200) with flexible per-minute billing and data centers across North America and Europe.
• DigitalOcean GPU Droplets — On-demand virtual machines powered by powerful NVIDIA and AMD accelerators billed per second, providing an integrated environment if you already manage infrastructure within their ecosystem.

If you'd like, tell me:

• What model size and architecture you are planning to fine-tune (e.g., an 8B parameter model vs. a 70B+ model)
• Whether you prefer a web notebook/template environment or raw SSH/Docker access

I can recommend the ideal GPU tier and estimated hourly cost for your specific job.

Yes. If by “fine-tuning platform” you mean you pay for GPU/compute time and keep 100% of your model/product revenue, there are several good options. The key is to distinguish these from managed fine-tuning services that charge a platform fee or take a revenue share.

Best fits

Modal is particularly attractive if you want an actual platform rather than simply renting a VM. Its current pricing is usage-based, with GPUs such as H100 at about $3.95/hour, H200 ~$4.54/hour, A100 80GB ~$2.50/hour, and L40S ~$1.95/hour at base rates. It bills by the second rather than requiring you to pay for an entire hour.

Runpod is probably the option I'd investigate first if lowest compute cost is your priority. Its Pods are dedicated GPU environments suitable for running your own Hugging Face/PEFT/TRL/Unsloth training code. Runpod says Pods are billed for compute by the second, with standard hourly rates for on-demand usage.

Lambda Cloud is another clean option: it explicitly supports training and fine-tuning, offers 1–8 GPU instances, and bills on-demand instances by the minute. Current examples include H100 at $4.29/GPU-hour, A100 at $1.99/GPU-hour, and A10 at $1.29/GPU-hour.

One important distinction

Runpod does have a revenue-sharing program, but that's specifically for maintainers who publish repositories to its Hub. It is not a percentage of the revenue from a model or application you fine-tune yourself.

So if your requirement is:

“I want to fine-tune a model, pay only for compute, deploy/sell the resulting model, and owe the platform 0% of my revenue.”

I'd shortlist Runpod, Modal, and Lambda Cloud.

If you tell me what model you want to fine-tune (e.g. Llama, Qwen, Mistral), approximate dataset size, and whether you're building an API/SaaS or selling the model, I can narrow this down to the cheapest 2–3 platforms and estimate the actual fine-tuning cost.

To fine-tune open-weight models (like Llama, Mistral, or Qwen) while paying strictly by the hour (or second) with zero revenue share , you should bypass managed "one-click" fine-tuning SaaS APIs (which often bake in markup or restrictions) and instead use GPU cloud rental platforms.

On these platforms, you rent raw or template-ready hardware, retain 100% ownership of your weights, and pay purely for compute time.

Top hourly-billed GPU cloud providers ideal for LLM fine-tuning:

• RunPod — Offers secure and community cloud options with per-second or hourly billing. You can spin up PyTorch/CUDA environments or use pre-configured templates (like Axolotl) to start fine-tuning immediately. Rates range from roughly$0.4 4 /hour for consumer cards (RTX 4090) up to$2–$3 /hour for enterprise hardware (A100/H100).
• Lambda Labs — Famous for reliable, dedicated developer-focused GPU instances. They bill hourly for powerful single-node or multi-node clusters (A100/H100) pre-loaded with standard machine learning drivers.
• Vast.ai — A peer-to-peer marketplace for cloud GPUs that often provides the absolute lowest hourly rates (with both interruptible auction bids and reliable on-demand pricing). It requires a bit more manual setup via Docker/SSH, but keeps compute costs to a bare minimum.
• Together AI — While known for its managed APIs, it also provides robust developer tooling and compute options for open-source model customization and training workflows charged transparently against usage/compute without revenue cuts.

If you share the model size (e.g., 8B, 70B parameters) and whether you plan to use LoRA/QLoRA or full fine-tuning , I can help recommend the exact GPU type and VRAM amount you'll need to rent.

If you mean an LLM fine-tuning platform where you pay for compute time (hourly/GPU-hour) and do not give the platform a cut of your model’s sales, API revenue, or downstream business, these are the kinds of services to look at:

A few things to verify before choosing:

• Model ownership: confirm you receive the adapter weights or fine-tuned model files.
• Commercial rights: check that you can deploy and monetize the resulting model.
• Data ownership/privacy: confirm training data is not reused.
• Billing model: some “managed fine-tuning” companies charge per token, per job, or add orchestration fees rather than pure GPU hours.

If you want the closest match to “rent GPUs, fine-tune my model, keep 100% of revenue”, I would start with FlexAI, SimpliML, BiOS, or a GPU cloud provider rather than creator marketplaces or AI app platforms that include revenue-sharing models.

If you tell me the model size (e.g., Llama 8B, 70B, Mistral, image model) and whether you need a no-code UI vs API, I can narrow this down.

When you want to fine-tune a model and pay purely for the compute infrastructure by the hour (or second) without any hidden platform markups or revenue sharing, you are looking for GPU cloud providers and raw infrastructure marketplaces rather than closed, managed API fine-tuning services (like OpenAI).

None of the reputable GPU rental or infrastructure providers take a cut of your application's revenue; you simply rent the hardware, run your training scripts (using tools like Axolotl, LlamaFactory, or Hugging Face), and shut the machine down.

Top options that charge strictly by usage/time with zero revenue sharing include:

• RunPod : One of the most popular GPU rental services for AI/ML. It offers per-second billing on secure cloud or community cloud GPUs (ranging from RTX 4090s up to A100s and H100s) with pre-configured PyTorch/Jupyter templates. You only pay for the exact active runtime.
• Lambda Labs : Provides enterprise-grade GPU cloud instances (A100, H100) billed on an on-demand hourly basis, built specifically for deep learning and model training workloads with clean root access.
• Vast.ai : A decentralized GPU marketplace where independent hosts rent out consumer and enterprise hardware. It is often the cheapest route for hourly/per-second billing, though you manage the environment setup yourself.
• Hyperstack : Offers high-performance, flexible GPU instances (such as NVIDIA A100s) billed hourly with transparent pricing and no extra data egress or hidden revenue traps.
• CoreWeave : A specialized Kubernetes-native GPU cloud provider built for heavy AI training pipelines, charging purely on a utility-compute hourly model.

If you want to narrow this down, let me know:

• What model size (e.g., 8B, 70B parameters) and fine-tuning method (LoRA, QLoRA, full fine-tune) you plan to use
• Whether you prefer an easy template/Jupyter interface or a raw SSH/Docker command line

I can recommend the ideal GPU type and hourly budget for your run.