The top fine-tuning platforms differ most in two ways: who runs the training loop and where the training data comes from. Adaption, Applied Compute, Baseten, Fireworks AI, Hugging Face, Thinking Machines, and Trajectory answer both differently. Some give you raw tools and expect you to write the code. Others run the whole loop for you.
Key Takeaways
- The training loop is the biggest difference. Adaption runs an automated four-stage loop against a target win rate you set. Baseten, Hugging Face, and Thinking Machines supply the GPUs and leave defining all of the training configuration up to you. Fireworks AI runs managed jobs on your dataset.
- Training data comes from a different place on each platform. Baseten, Fireworks AI, Hugging Face, and Thinking Machines leave preparing the dataset to the user. Applied Compute and Trajectory build data from production use, but are not self-serve and don’t have an api available for everyday developers. Adaption can optimize your raw data or generate synthetic data before training.
- Training methods vary. Adaption’s system runs SFT and DPO, and Applied Compute and Trajectory center on reinforcement learning. Fireworks AI, Hugging Face, and Thinking Machines support both. Baseten runs whatever code you bring.
- A no-data path exists at Adaption. If you need to fine-tune a model but don’t have any data, Invent a Dataset generates an AI-ready training dataset from a plain language description. There is an api available for everyday developers.
What Does a Fine-Tuning Platform Do?
A fine-tuning platform trains an existing AI model on your examples, so it follows your task, format, or domain more reliably than prompting a generic model alone. Training adjusts the model’s weights, and platforms differ in two choices: what the model learns from, and how much it changes.
What the model learns from:
- Supervised fine-tuning (SFT) trains a model on example inputs paired with the outputs you want.
- Direct preference optimization (DPO) trains on pairs of preferred and rejected responses.
- Reinforcement learning (RL) trains against scores from a grader. You need a way to judge outcomes, not example answers.
How much of the model changes:
- LoRA trains a small adapter on top of frozen weights.
- Full fine-tuning updates every weight and costs more compute.
How Should You Evaluate a Fine-Tuning Platform?
Judge a fine-tuning platform on five criteria: who runs the training loop, where the training data comes from, which training methods it supports, what you get after training, and what you own.
- Who runs the training loop. Writing the training loop yourself gives you control over the algorithm, but requires specialized expertise and costs engineering time. Managed jobs and automated systems give up some control for speed.
- Where the training data comes from. Every platform needs a dataset before it can train. Some expect you to bring a cleaned, formatted dataset. Others help you prepare, expand, or generate it. That choice sets how soon your first run can start and how much data work your team does.
- Which training methods it supports. SFT and DPO fit tasks where you have example outputs or preferred answers. RL fits tasks where you can score a result but can’t write the correct answer, such as an agent finishing a multistep job. Match the method to your task before you compare platforms.
- What you get after training. Platforms return different things: a checkpoint (a saved copy of the trained weights), a hosted model, or both. A checkpoint alone means you set up serving yourself. A hosted model means the platform runs it for you.
- What you own. Some platforms let you download the trained weights and serve them anywhere. Others keep the model on their own infrastructure. If you can’t take the weights with you, switching platforms later means retraining.
How Do the Seven Fine-Tuning Platforms Compare?
The seven platforms fall into three groups:
- Adaption AutoScientist runs the end-to-end model training loop and alignment.
- Baseten, Fireworks AI, Hugging Face, and Thinking Machines supply the infrastructure, and you write or configure the training run.
- Applied Compute and Trajectory build the model, either alongside your team or from your production feedback.
Top Fine-Tuning Platforms: Side-by-Side Comparison
| Company (Product) | Who Runs the Training Loop | Training Data | Training Methods | After Training |
|---|---|---|---|---|
| Adaption (AutoScientist) | The system runs the training loop against a target win rate your team sets. It is automated to derisk model training, learning to set up right hyperparameters for your task. | You don’t need to bring training data. An agent goes from a dataset description to a training ready dataset. Public API which developers can use. | SFT and DPO; LoRA or full fine-tuning, by model | Download the best checkpoint |
| Applied Compute (AC2) | Applied Compute researchers beside your engineers, or your team in AC2. AC is not self-serve and doesn’t have an API. | Company data, production traces, and data pipelines built in AC2 | Reinforcement learning in your own harness | Dedicated inference in AC2 |
| Baseten | You choose the hyperparemeters, with your own training code or Baseten Loops | You supply the training data | Whatever your code runs | Deploy a checkpoint to a production endpoint |
| Fireworks AI | Managed jobs with defaults you can override, or you through the Training API | You supply the training data | Managed SFT, DPO, and ORPO; reinforcement learning through the Training API | Served on Fireworks AI inference |
| Hugging Face | You choose the hyperparameters, with TRL scripts run on Hugging Face Jobs | You supply the training data | SFT, DPO, GRPO, KTO, and reward modeling | Push to the Hub; download and serve anywhere |
| Thinking Machines (Tinker) | You write the loop in Python and choose the hyperparameters; Tinker runs the infrastructure | You supply the training data | SFT and reinforcement learning with LoRA | Checkpoints on the platform; serverless inference (beta) |
| Trajectory | The platform collects traces, trains, evaluates, and deploys | Production traces and expert feedback from your application | Reinforcement learning on open models | Deploy the checkpoint you approve |
1. Adaption AutoScientist
Adaption builds self-improving intelligence systems for enterprises. AutoScientist is its system that runs the research loop behind fine-tuning on its own. Your team sets the outcome, and AutoScientist co-optimizes your training data and recipes until it hits the goal. AutoScientist covers SFT and DPO and works with Gemma, gpt-oss, Llama, Mistral, Nemotron, and Qwen models from 0.8B to 122B parameters, including multimodal variants. See the full list of supported models.
You can bring training-ready data, optimize it first with Adaptive Data, or generate it with Invent a Dataset. Each run ends with a checkpoint you can download, so your team owns the model.
AutoScientist outperforms human-configured training by 35% on average. When used to train AI Singapore’s SEA LION model, AutoScientist saved 56 engineering hours of manual hyperparameter optimization, using a small base model.
Best for: teams that want to fine-tune without writing training code or tuning hyperparameters by hand, and teams that want a model in weeks, not months.
2. Applied Compute
Applied Compute builds custom models and agents for enterprises. Its Applied Compute Agent Cloud (AC2) trains, serves, and improves open-weight models inside the customer’s own agent harness, meaning the tools and execution loop around the model.
Applied Compute researchers work beside customer engineers, and training data comes from your own datasets and production traces. AC2 supports reinforcement learning, SFT, preference optimization, and self-distillation. Any checkpoint can deploy to dedicated inference on AC2, and customers keep the weights.
Best for: teams that want researchers embedded with them and plan to run reinforcement learning in their own agent harness.
3. Baseten
Baseten is a model deployment platform that also runs training. Baseten Training runs your own fine-tuning code on H100 or H200 GPUs, on one node or several, so you choose the method and bring the dataset. One command deploys a trained checkpoint to a production endpoint, and you can download the checkpoint files to serve the model elsewhere. Baseten Loops is a managed, Tinker-compatible option for fine-tuning and reinforcement learning.
Best for: teams that already have training code and a dataset and want training and production serving on one platform.
4. Fireworks AI
Fireworks AI is an inference platform that offers two ways to train a model. Managed Training handles supervised fine-tuning and preference tuning (DPO and ORPO) with no custom code, from a dataset in OpenAI-compatible chat format. The Training API covers custom loss functions and reinforcement learning. Tuned models run on Fireworks AI inference, where one dedicated deployment can host up to 100 LoRA adaptations at no extra cost. You can also download adapters and merge them off-platform.
Best for: teams with a labeled dataset that want managed tuning on the platform that serves the model.
5. Hugging Face
Hugging Face is a platform where the machine learning community shares models and datasets. Its TRL library trains models with SFT, DPO, GRPO, KTO, and reward modeling, and Hugging Face Jobs runs those training scripts on Hugging Face GPUs, so you choose the method and bring the dataset. You push the trained model to the Hub, where you can download it and serve it anywhere.
Best for: teams that want open-source tooling and rented GPUs, and are comfortable writing a training script.
6. Thinking Machines Tinker
Thinking Machines is an AI research and product company. Its first product, Tinker, is a training API for open-weight models. You write the training loop in Python with primitives such as forward_backward and sample, so you choose the method and bring the data. Tinker schedules the work across GPUs and recovers from failures. It supports models from 1B to 1T+ parameters, including mixture-of-experts and vision-language models. Tinker trains LoRA adapters and stores the checkpoints, and you can download them and serve the model elsewhere.
Best for: teams that want full control over the algorithm, including custom reinforcement learning, and have the people to write and evaluate it.
6. Trajectory
Trajectory trains a model on how people use your product. You connect your application, define the tasks and a grader, and collect traces from real usage, including edits and corrections. The platform runs reinforcement learning on an open base model, compares checkpoints on held-out tasks, and deploys the one you approve. It trains open models only. The deployed model runs on a managed endpoint that Trajectory operates, so confirm whether you can export the weights.
Best for: teams with a product in production and domain experts whose corrections can serve as the training signal.
Which Fine-Tuning Platforms Also Create or Improve Training Data?
Three of the seven platforms create or improve training data: Adaption, Applied Compute, and Trajectory. You need to provide training-ready data to train a model with Baseten, Fireworks AI, and Thinking Machines.
- Adaption: optimize, expand, and translate an existing dataset with Adaptive Data, or generate a synthetic dataset from scratch with Invent a Dataset.
- Applied Compute: builds data pipelines with customers in AC2, including task queries generated by prompting frontier models.
- Trajectory: builds training data from production traces, edits, and expert corrections.
How Do You Choose a Fine-Tuning Platform?
Choose a fine-tuning platform based on what you’re starting with: your data, your training code, your team, and your production traffic.
- Choose Adaption AutoScientist (with Invent a Dataset or Adaptive Data) if you need to fine-tune without writing training code, have unprepared or no training data, or need a production-ready model in weeks, not months.
- Choose Thinking Machines Tinker, Baseten, Fireworks AI, or Hugging Face if you need full control of the training algorithm, including custom loss functions or reinforcement learning you write yourself.
- Choose Fireworks AI if you have a labeled dataset and want managed tuning on the platform that serves the model.
- Choose Baseten if you already have training code and care most about deployment and autoscaling.
- Choose Hugging Face if you already work with open-source tools like TRL and the Hub and need GPUs to run your training scripts.
- Choose Applied Compute if you need researchers embedded with your team to run reinforcement learning in your own agent harness.
- Choose Trajectory if you have a product in production and experts whose corrections can train the model.
The right platform depends on how much control you need and the resources your team has for training work. If you need full control and have the engineers, choose a platform that gives you the tools. If you lack the people or time, choose one that runs the training for you. If you need control but lack the team, look at one that puts researchers beside your engineers. With any choice, confirm that you can download the trained weights and keep your datasets, graders, and traces.
Author
Adaption TeamDate