I need to fine-tune an open-source LLM on my own dataset. What managed fine-tuning platform is best for a non-expert user with a small budget?

For a non-expert with a small budget, I’d pick Hugging Face AutoTrain first.

My ranking

🥇 Best overall: Hugging Face AutoTrain

AutoTrain is probably the least intimidating starting point. It supports training from the Hugging Face ecosystem, can be used without managing your own GPU infrastructure, and charges based on the hardware/time consumed. Hugging Face also says you can download the resulting model rather than being locked into their platform.

I'd use it if your situation is roughly:

"I have a few thousand to perhaps tens of thousands of examples, I want to LoRA/SFT an 7B–14B-ish model, and I don't want to learn CUDA, Docker, distributed training, etc."

🥈 Best alternative: Together AI

If you're comfortable with a somewhat more developer-oriented interface, Together AI is arguably the better choice. It specifically focuses on managed fine-tuning of open-source models and supports models from Hugging Face without requiring you to manage training infrastructure.

It becomes particularly attractive if you expect to deploy the fine-tuned model afterward, because training and inference are integrated.

🥉 Fireworks AI

Fireworks AI is another strong option, especially if you want a polished path from fine-tuning → deployment. Its managed fine-tuning handles scheduling, training, checkpointing, and model output, and it supports major open-model families including Llama, Qwen, DeepSeek, Gemma, and others.

For a small personal project, though, I wouldn't choose it over AutoTrain unless you specifically want Fireworks' serving infrastructure.

What I would not do initially

I wouldn't start by renting an H100 from RunPod/Modal and setting up Unsloth + Axolotl + Transformers yourself. That can be dramatically cheaper and more flexible, but you're trading money for your time and complexity.

Unsloth itself is excellent—its current tooling is designed to make local/Cloud training considerably easier and more memory-efficient—but it's more of a training tool than the fully managed experience you're asking for.

My recommendation

If you give me these four things:

1. Dataset size — e.g. 5,000 examples / 2 GB
2. What the data looks like — conversations, instructions, documents, classifications, etc.
3. Model you want to fine-tune — e.g. Llama 3.1 8B, Qwen 3 8B, Mistral, etc.
4. Your approximate budget — e.g. $10, $50, $100

…I can tell you exactly which platform I'd use, which model/configuration to pick, and roughly what the fine-tuning run should cost.

🥇 Best overall: Hugging Face AutoTrain

AutoTrain is probably the least intimidating starting point. It supports training from the Hugging Face ecosystem, can be used without managing your own GPU infrastructure, and charges based on the hardware/time consumed. Hugging Face also says you can download the resulting model rather than being locked into their platform.

I'd use it if your situation is roughly:

"I have a few thousand to perhaps tens of thousands of examples, I want to LoRA/SFT an 7B–14B-ish model, and I don't want to learn CUDA, Docker, distributed training, etc."

🥈 Best alternative: Together AI

If you're comfortable with a somewhat more developer-oriented interface, Together AI is arguably the better choice. It specifically focuses on managed fine-tuning of open-source models and supports models from Hugging Face without requiring you to manage training infrastructure.

It becomes particularly attractive if you expect to deploy the fine-tuned model afterward, because training and inference are integrated.

🥉 Fireworks AI

Fireworks AI is another strong option, especially if you want a polished path from fine-tuning → deployment. Its managed fine-tuning handles scheduling, training, checkpointing, and model output, and it supports major open-model families including Llama, Qwen, DeepSeek, Gemma, and others.

For a small personal project, though, I wouldn't choose it over AutoTrain unless you specifically want Fireworks' serving infrastructure.

What I would not do initially

I wouldn't start by renting an H100 from RunPod/Modal and setting up Unsloth + Axolotl + Transformers yourself. That can be dramatically cheaper and more flexible, but you're trading money for your time and complexity.

Unsloth itself is excellent—its current tooling is designed to make local/Cloud training considerably easier and more memory-efficient—but it's more of a training tool than the fully managed experience you're asking for.

My recommendation

If you give me these four things:

For a non-expert user with a small budget who wants to fine-tune an open-source LLM (like Llama or Mistral) without writing code or managing infrastructure, the sweet spot is Hugging Face AutoTrain.

The Best Choice:

• What it is: A web-based, no-code/low-code graphical interface provided directly by Hugging Face. You upload your dataset (usually a CSV or JSON file), select a base open-source model, choose your target configurations, and click run.
• Why it's great for non-experts: It completely abstracts away the complex configuration files, Python dependencies, and command-line scripts. It guides you through formatting expectations step-by-step.
• Budget friendliness: It runs on a pure pay-as-you-go GPU compute minute model via Hugging Face Spaces. There are no expensive monthly enterprise subscriptions or hidden software markups—you literally pay only for the exact hardware minutes used to train your model. For a standard QLoRA fine-tune on a smaller open-source model (like an 8B parameter model), a training run typically costs just a few dollars.
• Ownership: Unlike proprietary vendor lock-ins, the resulting fine-tuned model adapter is completely yours to download, host, or deploy anywhere.

Alternative Options to Consider

• Together AI
  • Best for: Users who want an easy web dashboard combined with enterprise-grade serverless API scalability later on.
  • Budget note: They feature straightforward API-driven fine-tuning, though they do enforce a small minimum job charge. It is slightly more developer-focused than Hugging Face's pure UI, but still very accessible.
• Predibase
  • Best for: Users looking for an intuitive UI built on top of efficient LoRA/QLoRA methods.
  • Budget note: Excellent for cost-saving because of its ability to efficiently serve multiple adapters on shared resources, though it is geared a bit more toward professional product teams.

Recommended Quick-Start Steps

If you'd like to narrow this down further, let me know:

• What kind of data/task you are working with (e.g., text classification, Q&A chatbot, style imitation)
• Roughly how many rows/examples are in your dataset

I can help you estimate the approximate cost and best base model to choose!

For a non-expert with a small budget, I’d pick Hugging Face AutoTrain first. If you want something more turnkey/API-oriented, Together AI is my second choice.

My ranking

🥇 Hugging Face AutoTrain — best starting point

This is probably the closest match to what you're asking for. AutoTrain provides a relatively simple UI/CLI for LLM fine-tuning, and you can download the resulting model and use it wherever you want rather than being locked into Hugging Face's inference infrastructure.

The big advantage for a small budget is that you're paying for the actual GPU time, rather than having to provision and manage GPUs yourself. Hugging Face also documents a free tier for limited numbers of samples.

I'd use it for something like:

Llama/Qwen 7–8B + a few thousand to tens of thousands of examples + LoRA/QLoRA That's a very reasonable first fine-tuning project.

🥈 Together AI — best "I don't want to deal with infrastructure"

Together is particularly attractive if you want to go from:

dataset → fine-tune → API endpoint

without worrying about GPUs, CUDA, distributed training, etc.

It supports models from the Hugging Face Hub and offers LoRA as the cheaper/faster option.

For models up to 16B parameters, current standard pricing is $0.48 per million training tokens for LoRA SFT and $0.54/M for full fine-tuning.

For example, if you had 10 million training tokens and 3 epochs, the rough LoRA training cost would be:

10M × 3 × $0.48/M = $14.40

That's quite reasonable for experimentation.

One caveat: hosting the resulting model costs extra, so don't leave a dedicated endpoint running continuously if you're just experimenting.

🥉 Fireworks AI

Fireworks is another good choice if your end goal is a production API. Its managed training currently starts around $0.50/M tokens for LoRA SFT on models up to 16B.

I'd choose it over Together if you already know you want to deploy the fine-tuned model through Fireworks.

What I'd actually do

If this is your first fine-tuning project, I'd avoid full fine-tuning entirely.

I'd do:

1. Pick a relatively small open model — 7B–14B is a good starting range.
2. Convert your data into the model's recommended instruction/chat format.
3. Use LoRA/QLoRA rather than full fine-tuning.
4. Start with a small subset of your data.
5. Run a short training experiment.
6. Evaluate against a held-out test set.
7. Only then spend money on a larger run.

My choice: start with Hugging Face AutoTrain if you want the simplest learning experience and ownership of the resulting model. Choose Together AI if you want the easiest path from fine-tuning to a hosted API.

If you tell me (a) which model you want to fine-tune, (b) roughly how many examples/tokens are in your dataset, and (c) your maximum budget, I can estimate the cost and recommend the exact platform/setup.

This is probably the closest match to what you're asking for. AutoTrain provides a relatively simple UI/CLI for LLM fine-tuning, and you can download the resulting model and use it wherever you want rather than being locked into Hugging Face's inference infrastructure.

The big advantage for a small budget is that you're paying for the actual GPU time, rather than having to provision and manage GPUs yourself. Hugging Face also documents a free tier for limited numbers of samples.

I'd use it for something like:

Llama/Qwen 7–8B + a few thousand to tens of thousands of examples + LoRA/QLoRA That's a very reasonable first fine-tuning project.

🥈 Together AI — best "I don't want to deal with infrastructure"

Together is particularly attractive if you want to go from:

dataset → fine-tune → API endpoint

without worrying about GPUs, CUDA, distributed training, etc.

It supports models from the Hugging Face Hub and offers LoRA as the cheaper/faster option.

For models up to 16B parameters, current standard pricing is $0.48 per million training tokens for LoRA SFT and $0.54/M for full fine-tuning.

For example, if you had 10 million training tokens and 3 epochs, the rough LoRA training cost would be:

10M × 3 × $0.48/M = $14.40

That's quite reasonable for experimentation.

One caveat: hosting the resulting model costs extra, so don't leave a dedicated endpoint running continuously if you're just experimenting.

🥉 Fireworks AI

For a non-expert user with a small budget, Google Colab paired with open-source libraries like Unsloth is the best and most affordable choice.

Instead of a costly enterprise-managed training platform, Google Colab lets you run code on cloud-hosted GPUs (like a T4 or A100) via your browser. Using Unsloth , you can fine-tune models like Llama or Mistral 2x to 5x faster with much less memory, often fitting comfortably within Colab's free tier or low-cost Pro tier ($10–$50).

Top Budget-Friendly Options

• Google Colab + Unsloth:
  • Best for: Beginners who want a guided notebook experience.
  • Cost: Free tier available; paid tiers start around $10/month for more GPU compute power.
  • Why it fits: Zero local hardware required; handles memory optimization automatically.
• Predibase:
  • Best for: Users who want a true zero-code managed platform.
  • Cost: Usage-based pricing with free trial credits.
  • Why it fits: Built on low-code frameworks like Ludwig, making fine-tuning open-source models as simple as uploading a CSV file through a clean web UI.
• RunPod / Vast.ai:
  • Best for: Renting raw cloud GPUs cheaply if you outgrow Colab.
  • Cost: Pay-as-you-go, often $0.20 to $0.50 per hour for a solid GPU.
  • Why it fits: Extremely cheap hourly rates, though it requires slightly more manual setup than Colab.

To help narrow down the exact setup you need, tell me:

• How large is your dataset (number of rows or file size)?
• Which open-source model do you want to fine-tune (e.g., Llama 3, Mistral)?

For a non-expert user working with a small budget, using a heavy-duty enterprise cloud infrastructure (like AWS SageMaker or Azure) will feel overly complex and quickly drain your wallet.

Instead, the modern sweet spot for non-experts relies on managed serverless AI providers or automated cloud notebook wrappers utilizing parameter-efficient methods like LoRA/QLoRA.

Top recommendations tailored for a small budget and minimal technical friction include:

1. Unsloth (via Free/Pro Google Colab)
   • What it is: Unsloth is an open-source framework optimized for training models up to 5x faster with 80% less memory. While technically a library rather than a hosted "platform," they provide pre-made, click-and-run Google Colab notebooks.
   • Why it’s great for non-experts: You don't manage any servers. You open their notebook, upload your dataset (in a simple CSV or JSON format), click "Run," and it handles a 7B or 8B model fine-tune using Google Colab’s free or cheap ($10/month) tier.
   • Cost: Free (on Colab free tier) or under $10 in compute credits.
2. Together AI
   • What it is: A managed cloud platform offering simple, API-driven or dashboard-assisted fine-tuning for popular open-source models (like Llama and Mistral).
   • Why it’s great for non-experts: They feature clean web interfaces and straightforward documentation. You upload your training file, select your base model, and their backend manages the cluster, training, and storage.
   • Cost: Pay-as-you-go per training token, making small datasets remarkably affordable (often costing less than $5 to $15 total for a few thousand training examples). You can check current rates directly on Together AI.
3. OpenPipe
   • What it is: A specialized managed fine-tuning platform built specifically to transition expensive prompt workflows into smaller, fine-tuned open-source models.
   • Why it’s great for non-experts: If your goal is to take input/output data (like from an existing app or a log of prompts) and bake it into a small, fast model, OpenPipe has a streamlined, opinionated workflow that guides you step-by-step from data capture to deployed model.
   • Cost: Generous free tier for initial testing, with predictable pay-per-token scaling. Explore their tools on OpenPipe.
4. Hugging Face AutoTrain / HF Jobs
   • What it is: Hugging Face’s no-code/low-code interface built right into the ecosystem where you already find your open-source models.
   • Why it’s great for non-experts: You can select a model, point to a dataset uploaded to your Hugging Face space, choose your target column names, and spin up an automated training job with minimal configuration.
   • Cost: Tied directly to the cost of the underlying GPU instance you select for the training duration (usually pennies to a few dollars per run). Check out the interface on Hugging Face.

Pro-Tip to Keep Costs Near Zero

• Pick a Small Language Model (SLM): Instead of trying to fine-tune a massive 70B parameter model, use a smaller open-source model in the 1B to 8B range (such as Llama 3/3.1/3.2, Mistral, or Qwen variants). They require drastically less GPU memory, train in minutes instead of hours, and when fine-tuned well, often outperform generic giant models on specific tasks.
• Format your data cleanly: Ensure your dataset is formatted cleanly as an instruction-response pair (e.g., {"instruction": "...", "output": "..."} ) before uploading to any of these platforms.

To help narrow down the exact path, tell me:

• What is your dataset about (e.g., text classification, a custom chatbot, medical data, code generation)?
• How many rows/examples do you currently have in your dataset?

For a non-expert with a small budget, I’d pick Together AI as the best default.

My ranking

Why Together AI

• Very little infrastructure to manage. You upload your dataset, choose an open model, configure the training, and Together handles the GPUs and training infrastructure.
• LoRA is supported, which is generally what I'd recommend for a small dataset and budget. It's considerably cheaper and faster than full fine-tuning.
• Pricing is token-based, rather than making you rent a GPU for a fixed amount of time. For models up to 16B parameters, current SFT LoRA pricing is $0.48 per million training tokens, with a $4 minimum for standard jobs.
• It supports models from the Hugging Face Hub, so you're not locked into a small proprietary model catalog.
• You can subsequently deploy the resulting model through the same platform.

For example, if you have 100,000 training examples averaging 200 tokens each, that's roughly 20M tokens per epoch. At $0.48/M, a single epoch on a ≤16B model would be about $9.60 before any applicable evaluation-token charges. That's the sort of pricing that makes experimentation practical.

Fireworks is a close second

fireworks.ai is particularly attractive if you care about taking the fine-tuned model straight into production. Its managed SFT LoRA pricing starts at $0.50/M training tokens for models up to 16B, extremely close to Together. It supports major open-source families including Llama, Qwen, Gemma, DeepSeek, and others.

The main reason I'd put it second for your situation is that Together's fine-tuning workflow is a little more straightforward for a first experiment.

What I'd actually do

If this is your first fine-tuning project, I'd use:

Together AI + a 7–14B-ish Qwen/Llama/Gemma model + LoRA + supervised fine-tuning (SFT).

Don't start with full fine-tuning or DPO. Get a small LoRA run working first, evaluate it against a held-out test set, and then decide whether you need something more sophisticated.

If you tell me (1) what your dataset looks like, (2) approximately how many examples/tokens it has, and (3) which open-source model you're considering, I can recommend the cheapest setup and estimate what the fine-tuning run will cost.

For a non-expert with a small budget, I’d start with Hugging Face AutoTrain. It’s the easiest “upload data → choose model → train” experience, and you retain/download the resulting model rather than being locked into the platform. AutoTrain supports LLM fine-tuning and charges based on the hardware/time used; it also has a limited free tier.

My ranking

Together AI is my second choice—and arguably the better choice if you're willing to tolerate a little more technical setup. It supports fine-tuning models from the Hugging Face Hub, including LoRA, and currently charges by training tokens. For models up to 16B parameters, LoRA SFT is listed at $0.48 per million training tokens, with a $4 minimum job charge.

For example, if you have 100,000 training examples averaging 500 tokens each and train for 3 epochs, that's roughly 150M training tokens, or about $72 for a ≤16B LoRA SFT job on Together AI, before any separate hosting costs.

Fireworks AI is also attractive: its managed LoRA SFT price for models up to 16B is currently $0.50/1M training tokens, and it offers a fairly polished path from fine-tuning to deployment.

What I'd actually do

If this is your first fine-tuning project:

1. Pick a relatively small instruct model (e.g. 7–9B rather than 70B).
2. Use LoRA/QLoRA, not full fine-tuning.
3. Start with Hugging Face AutoTrain.
4. Keep a held-out validation/test set.
5. Do one inexpensive run before spending money on multiple experiments.
6. Only move to Together/Fireworks if you need their deployment infrastructure or more advanced training options.

Bottom line:
👉 Easiest: Hugging Face AutoTrain
👉 Best combination of simplicity, price, and serious fine-tuning capability: Together AI
👉 Best if you also want production serving: Fireworks AI

If you tell me (a) which model you want to fine-tune, (b) roughly how many examples/tokens are in your dataset, and (c) your maximum budget, I can estimate the actual cost and recommend the cheapest setup.

For a non-expert user working with a small budget, the best approach is not a traditional enterprise cloud platform (like AWS SageMaker or GCP Vertex AI), which can rack up surprise bills and require complex configuration. Instead, the sweet spot for budget and simplicity relies on consumer-grade cloud notebooks paired with memory-efficient training software , or straightforward low-cost developer APIs.

The most practical, low-cost pathways for your situation include:

1. Google Colab (Free or Pro) + Unsloth
   • What it is: Google Colab provides browser-based access to cloud GPUs. Paired with Unsloth , an open-source optimization library, you can fine-tune models like Llama or Mistral up to 2.2× faster while using significantly less memory.
   • Why it's great for non-experts: You can use free T4 GPUs for small tests or pay ≈ $10-$12 for a Colab Pro month to get access to better GPUs (like an A100 or L4). Unsloth provides free, ready-to-use Jupyter notebooks where you just plug in your dataset CSV/JSON file and hit "run" without writing complex training code.
   • Cost: Free to ≈ $10 - $50 depending on compute tier.
2. LLaMA-Factory (via a cheap GPU rental like Vast.ai or RunPod)
   • What it is: LLaMA Factory is an open-source framework that provides a slick, out-of-the-box Web UI for fine-tuning over 100 open-source models (Llama, Qwen, Gemma, Mistral) using QLoRA.
   • Why it's great for non-experts: It is entirely point-and-click. You spin up a cheap cloud GPU instance on RunPod or Vast.ai (costing roughly $0.20 to $0.5 per hour for an RTX 3090/4090), launch the LLaMA Factory web interface, upload your dataset through the browser, select your parameters, and click start.
   • Cost: Under $20 for an entire training run.
3. Together AI or Fireworks AI
   • What it is: Managed cloud inference and fine-tuning platforms that let you upload a JSONL dataset via an API or simple dashboard and fine-tune open-weights models natively.
   • Why it's great for non-experts: Zero infrastructure management. You don't have to manage GPU drivers, memory overflows, or code repositories. You pay per million training tokens.
   • Cost: Inexpensive for smaller models, though slightly less transparent for absolute beginners than a fixed-hour GPU rental.

Recommendation on How to Start

• If you want a completely guided, zero-install code walkthrough , look up an Unsloth Google Colab notebook Go to product viewer dialog for this item. for supervised fine-tuning.
• If you prefer a visual web interface without touching code at all, deploy LLaMA-Factory on a cheap hourly rental via RunPod.
• Stick to QLoRA (Quantized Low-Rank Adaptation) and a small model (like a 3B to 8B parameter Small Language Model) to keep your training time under an hour and your cost to just a few dollars.

To help narrow down the exact path, could you tell me:

• How big is your dataset (number of rows/examples)?
• Which base open-source model are you hoping to use (e.g., Llama-3/4, Qwen, Gemma)?

For a non-expert with a small budget, I’d pick Together AI as the best overall managed fine-tuning platform right now.

My ranking

🥇 Together AI — my recommendation

Together is particularly attractive for your situation because you don't have to manage GPUs, training environments, CUDA, distributed training, etc. You upload your dataset, choose a supported open model, configure the fine-tuning job, and get a resulting model you can deploy. It supports LoRA as well as full fine-tuning and says it supports essentially any open-source model from Hugging Face Hub.

The pricing is also unusually friendly to experimentation:

• Up to 16B models: $0.48 / 1M training tokens for LoRA SFT
• 17–69B: $1.50 / 1M tokens
• 70–100B: $2.90 / 1M tokens
• $4 minimum per normal fine-tuning job

So, for example, if you had 500k training tokens × 3 epochs = 1.5M training tokens, a small ≤16B model would cost roughly $0.72 in training compute (subject to the $4 minimum).

🥈 Hugging Face AutoTrain — best if you want maximum ownership

AutoTrain is arguably the easiest choice if your priority is "I want the resulting model files and don't want to be locked into a provider." Hugging Face explicitly says you can download the trained model and use it anywhere. It also has a free tier for limited datasets.

The tradeoff is that its pricing is based on the hardware/minutes used, so estimating the final bill can be less intuitive than Together's per-token pricing.

🥉 Fireworks AI — excellent if you're going straight to production

Fireworks is another very strong option. Its managed training supports SFT, DPO and reinforcement fine-tuning across major open-model families including Llama, Qwen, DeepSeek, Kimi, Gemma and GLM.

Its LoRA SFT pricing starts at $0.50/1M tokens for models up to 16B, essentially matching Together.

The catch for a tiny-budget hobby/project user is that serving the resulting LoRA model has additional deployment costs, whereas training itself is inexpensive.

What I'd actually do

If this is your first fine-tuning project, I'd use:

Together AI + a 7–14B Qwen/Llama model + LoRA/QLoRA-style SFT

rather than full fine-tuning.

Start with a small representative dataset, perhaps 500–5,000 high-quality examples. Don't spend money training a huge model until you've established that fine-tuning actually improves your evaluation set.

One important distinction: if your dataset is primarily documents/knowledge that you want the model to answer questions about, I'd probably not fine-tune at all. RAG is usually cheaper and easier to update. Fine-tuning makes more sense when you're teaching the model a behavior, format, style, task, or domain-specific response pattern.

If you tell me (1) roughly how many examples/tokens are in your dataset, (2) what the data looks like, and (3) your maximum budget—e.g. $10, $50, or $100, I can recommend a specific open model and give you an estimated training cost.

Fine-tuning an open-source model doesn't require a massive enterprise budget or a machine learning PhD anymore. For non-experts on a small budget, the best managed platforms abstract away the complex GPU infrastructure, distributed training configurations, and CLI tools behind simple web dashboards or straightforward API calls.

Here are the top managed fine-tuning platforms well-suited for beginners and small budgets, broken down by their core strengths:

Easiest Dashboard Experience (No Code / Low Code)

• Hugging Face AutoTrain : Hugging Face’s managed auto-training feature allows you to fine-tune models like Llama or Mistral directly through your browser with a simple user interface. You just upload your dataset in CSV/JSON format, select the target model, and the platform handles the training infrastructure on managed AWS or GCP instances. Pricing is pay-as-you-go based on the compute time used, making it very budget-friendly for small jobs. Learn more on the Hugging Face AutoTrain Documentation.
• Openpipe : Built specifically to make fine-tuning accessible, Openpipe lets you upload datasets, track experiments, and fine-tune open-source models with minimal configuration. It features a great UI for analyzing where your model succeeds or fails and automatically optimizes the training process. You can check out their platform on the Openpipe Website.

Developer-Friendly & Cost-Optimized APIs

• Together AI : Together AI offers serverless fine-tuning for popular open-source models (like Llama 3) via a simple API or web console. They use optimized training stacks (like FlashAttention and FSDP) that significantly reduce the training time and GPU cost compared to traditional setups. It is ideal if you want a managed backend without managing cloud virtual machines. Explore options on the Together AI Fine-tuning Guide.
• Fireworks AI : Similar to Together AI, Fireworks provides blazing-fast inference and cost-effective serverless fine-tuning. Their platform is geared toward developers who want a streamlined, highly optimized pipeline where you pay strictly for the tokens/compute used during training without spinning up dedicated cluster instances. Visit the Fireworks AI Platform.

Open-Source Orchestration & Cloud Hosting

• Modal : If you have a tiny bit of Python knowledge but don't want to manage AWS billing or GPU provisioning, Modal is fantastic. They allow you to run serverless Python scripts that spin up GPUs (like an A10G or H100) only for the exact seconds your training script runs. You can run popular fine-tuning libraries like Unsloth or Axolotl on Modal with pay-per-second billing, which is often the cheapest route for small datasets. Learn how to get started on the Modal Documentation.

To help narrow down the best choice for you, could you share:

• Which open-source model you are hoping to fine-tune (e.g., Llama 3, Mistral, Phi-3)?
• How large your dataset is (approximate number of rows or token count)?
• Your comfort level with code (do you prefer a 100% visual web dashboard or a simple Python script)?