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

If you want to fine-tune open-source models (like Llama, Mistral, Qwen, or DeepSeek) using your own code, scripts, or frameworks like Axolotl and Hugging Face, but only want to pay for raw compute by the hour (or second) with zero revenue sharing or lock-in, you should look at GPU Cloud Providers or Serverless Infrastructure Providers rather than managed "one-click" fine-tuning APIs.

Managed APIs (like OpenAI) or niche vertical platforms sometimes structure pricing awkwardly, but infrastructure clouds give you a blank environment and charge strictly for the hardware time used.

Top Platforms with Strict Hourly/Per-Second Billing (No Rev-Share)

• • Best for: The largest selection of community GPUs with ultra-low hourly rates ($0.79/hr for A100s, ~$1.99–$2.49/hr for H100s).

  •   - **How it works:** You spin up a secure cloud pod or a cluster pre-installed with PyTorch, CUDA, and Jupyter, load your dataset, run your fine-tuning script, and turn the pod off. You pay strictly for the time the instance is running.
    

  •   - Explore options on [RunPod](https://www.runpod.io/).[[1]](https://aiccloud.in/blog/top-5-cheap-llm-fine-tuning-cloud-2026)[[2]](https://www.youtube.com/watch?v=Wofw-l7Cnis)
    

• •   - **Best for:** Serverless Python infrastructure where you write code locally and run it in the cloud.
    

  •   - **How it works:** Modal lets you run heavy ML workloads on demand, billing you strictly per-second/hour for exact GPU usage (e.g., A100s or H100s) without keeping instances alive or taking any cut of your product. It scales down to $0 the second your fine-tuning script finishes.
    

  •   - Check the specs on [Modal Pricing](https://modal.com/pricing).[[1]](https://modal.com/pricing)[[2]](https://www.buildmvpfast.com/tools/api-pricing-estimator/modal)
    

• •   - **Best for:** Enterprises or developers who want raw, high-end researcher-grade clusters with predictable networking.
    

  •   - **How it works:** Pure hourly bare-metal or virtualized GPU rentals with zero revenue sharing or hidden platform markups on your end-models.
    

  •   - View configurations via [Lambda Labs](https://lambdalabs.com/).
    

• •   - **Best for:** Scalable multi-node training and dedicated endpoint hosting.
    

  •   - **How it works:** While they offer token-based serverless fine-tuning for some models, they also let you rent raw GPU clusters or dedicated nodes by the hour to run custom training jobs completely independently.
    

  •   - Review details on [Together AI](https://www.together.ai/).[[1]](https://docs.together.ai/docs/fine-tuning/pricing)[[2]](https://www.eesel.ai/blog/together-ai-pricing)
    

What to Look Out For

• Storage vs. Compute Costs: Most hourly GPU providers charge a tiny fee (pennies per GB per month) to store your model weights and datasets on a persistent network volume while the GPU is turned off. Make sure to terminate or pause pods correctly when not actively training.
• Frameworks to use: Because these are raw compute environments, you'll manage the fine-tuning process yourself using standard open-source tools like Unsloth, LoRA/QLoRA , or Hugging Face TRL (Transformer Reinforcement Learning).

If you'd like, let me know:

• Which base model size you plan to fine-tune (e.g., 8B, 70B parameters)
• Whether you prefer a Jupyter notebook interface or a CLI/Python script workflow (like Modal)

I can recommend the most cost-effective GPU type and provider setup for your specific requirements.

Yes. If by “fine-tuning platform” you mean a service where you pay for GPU compute/time and keep 100% of the revenue from your resulting model, there are several good options.

Best fits

• runpod.io — probably the closest match. You rent GPUs by the second/hour and run your own fine-tuning stack. RunPod explicitly supports fine-tuning, and its current on-demand GPU prices include roughly $1.59/hr for A100 80GB, $2.89/hr for H100 PCIe, and $4.59/hr for H200. There is no revenue share.
• modal.com — excellent if you want a developer-friendly platform. Modal charges for actual compute time rather than taking a percentage of your model's revenue. Current rates include about $3.95/hr for an H100, $2.50/hr for an A100 80GB, and $1.95/hr for an L40S, billed by the second. You supply/run the fine-tuning code yourself.
• replicate.com — best if you want a more turnkey fine-tuning/deployment experience. Replicate uses pay-as-you-go compute billing rather than a revenue-share model. It supports custom models and fine-tuning, with training generally billed according to hardware time.

What I'd choose

If your requirement is specifically:

“I upload my dataset → fine-tune an open-source model → pay by GPU hour → I own the resulting model → no percentage of sales/revenue.” I'd start with RunPod. It's essentially GPU infrastructure rather than a marketplace taking a cut, and you have complete control over the training environment and resulting weights.

If you tell me what model you're trying to fine-tune (Llama, Qwen, Flux, SDXL, etc.), approximate dataset size, and whether you want a no-code interface, I can narrow this down to the 3–5 platforms that fit exactly, including their current hourly costs.

Yes. If by “charges per hour” you mean you pay for compute/training time rather than giving the platform a percentage of your revenue, there are several good options.

Best fits

• modal.com — Probably the closest match if you want pure compute economics. You can run your own fine-tuning code on GPUs and pay for compute by the second. For example, Modal currently lists H100 SXM5 at about $3.95/hour, H200 at $4.54/hour, and A100 80GB at $2.50/hour. There is no revenue share.
• together.ai — Has a turnkey fine-tuning platform for open-source models, but importantly it does not currently bill fine-tuning by the hour. It charges based on tokens processed. For example, LoRA SFT is currently $0.48/1M tokens for models up to 16B parameters.
• platform.openai.com — For its Reinforcement Fine-Tuning API, OpenAI currently charges $100/hour of core training time for o4-mini, rather than taking a percentage of your revenue.

If your requirement is specifically “fine-tuning SaaS + hourly billing”

Modal is the one I'd investigate first. It is more of a programmable ML infrastructure platform than a one-click fine-tuning SaaS, but that is precisely why you get straightforward compute-based billing and no platform revenue share.

If you tell me what you're fine-tuning (LLM, image model, voice model, etc.), approximate model size, and whether you want no-code/GUI or API, I can give you a shortlist of 5–10 platforms that fit the hourly/no-revenue-share requirement, including approximate cost per training run.

You can use cloud GPU rental and managed infrastructure platforms like Together AI or raw cloud providers like RunPod and Lambda Labs , which charge flat hourly rates for compute resources with zero revenue-share or hidden percentage fees.

Standard fine-tuning platforms never take a cut of your commercial application revenue; they bill purely based on compute time (per GPU/hour) or training tokens.

Top Hourly Fine-Tuning & GPU Compute Options

If you can share which model architecture (e.g., Llama 3, Mistral) or fine-tuning framework (e.g., LoRA, Full) you plan to use, I can recommend the most cost-effective GPU type and setup.

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.