The best company for fine-tuning depends on your team’s constraints. Adaption’s automated model training system, AutoScientist, suits enterprise teams limited by training data or machine learning staff. Together AI, Fireworks AI, and Hugging Face suit teams tuning open models. AWS, Google Cloud, Microsoft, and Databricks suit enterprises already on those platforms.
Key Takeaways
- Fine-tuning allows organizations to build a customized AI model so the model performs a specific task more reliably.
- Adaption, Together AI, Fireworks AI, and Hugging Face all tune open weight models but each take different approaches.
- Adaption's AutoScientist automates training by co-optimizing data and training recipe together until it reaches the outcome a team sets. It has been shown to outperform human-configured training by 35 percent on average.
- Together AI, Fireworks AI, and Hugging Face tune open models with supervised, preference, and reinforcement methods, which lets teams choose where the tuned model runs.
- Amazon Bedrock, Google Vertex AI, Microsoft Foundry, and Databricks run managed fine-tuning inside platforms enterprises already use, which can reduce the number of new vendors a security review must cover.
What Is Fine-Tuning?
Fine-tuning, also called model customization, is a training process that continues training a pretrained AI model on an organization’s own examples so the model performs a specific task more reliably. Three training methods are common:
| Method | What it trains on |
|---|---|
| Supervised fine-tuning (SFT) | Input and output pairs that show the desired response |
| Direct preference optimization (DPO) | Pairs of preferred and rejected responses |
| Reinforcement fine-tuning (RFT) | A reward function that scores each output the model generates |
Each method can update a small set of adapter weights, known as LoRA (low-rank adaptation), or every weight in the model, known as full fine-tuning.
What Should Enterprises Look for in a Fine-Tuning Platform?
Enterprises should weigh five criteria first: model access, data handling, ownership, data quality, and staff effort. Start with the constraint your team cannot change, then use the table to see which kind of company fits.
| Criterion | What to consider |
|---|---|
| Model access | The base models you need, and whether they are open or proprietary. Proprietary models are tuned only on the vendor’s platform. Open-weight models such as Llama, Qwen, and Gemma are tuned by companies including Together AI, Fireworks AI, Hugging Face, and Adaption. |
| Data handling | Where training data is processed and stored, which security controls apply, and whether training can run inside your own environment. |
| Ownership | Whether the tuned model can move to another provider or run on your infrastructure, and what happens to it if the vendor retires the base model or the feature. |
| Data quality | Whether the platform helps improve or generate training data, or only trains on what you supply. This matters most when your dataset is uneven, missing, or thin in some languages. |
| Staff effort | How much ML engineering configuration, hyperparameter sweeps, and iteration require, and whether training can be automated or only managed. |
Which Companies Fine-Tune Open Models?
Together AI, Fireworks AI, Hugging Face, and Adaption work with open-weight models, which are models whose developers publish the weights for others to download and modify, such as Llama, Qwen, and Gemma.
The four companies sit in different layers. Together AI and Fireworks AI provide managed training and hosting infrastructure. Hugging Face provides an open-source training library. Adaption’s AutoScientist, automates the training loop itself. Working with an open-weight model lets a team choose where the tuned model runs, and the team takes on model evaluation and operations.
| Company | Methods | Deployment | Best fit |
|---|---|---|---|
| Together AI | LoRA (default) and full fine-tuning, with SFT, DPO, vision, and reasoning fine-tuning | Dedicated endpoint, or download for local use | Managed training and hosting in one place |
| Fireworks AI | SFT for text and vision models, DPO, and RFT, through managed jobs or a Training API | LoRA adapters deployed on Fireworks | RFT on open models |
| Hugging Face | TRL library with SFT, DPO, GRPO, and reward modeling trainers | Wherever the team runs it | ML engineers who want full control of training code |
| Adaption | AutoScientist optimizes training data and training recipe together; Adaption’s published evaluations used open-weight models | Adaption’s app and API; weights can be downloaded for local or own-infrastructure use | Teams whose limit is training data or ML staff time |
What Does Open-Weight Ownership Mean for Enterprises?
Open-weight ownership means an enterprise holds the weights of its fine-tuned model, so it chooses where the model runs and keeps it if a vendor changes its roadmap. Weights are the numerical parameters a model learns during training. Ownership also means the enterprise takes on hosting, evaluation, and license review.
| Parameter | Proprietary Model Tuned on a Vendor Platform | Open-weight Model |
|---|---|---|
| Where the tuned model runs | On the vendor’s platform | On any host or infrastructure the license permits |
| If the vendor retires the base model | Fine-tuned versions end with it | The team keeps the weights it has downloaded |
| Operating work | The vendor operates serving | The team or its chosen host operates serving and monitoring |
| Governing terms | The vendor’s terms of service | The model’s license, which differs by model |
| Training data location | Data is uploaded to the vendor’s platform | Data can stay in the team’s environment when training runs there |
Owning the parameters has three effects:
- Deprecation: the tuned model does not depend on a vendor’s deprecation schedule.
- Data location: training data and the tuned model can stay in the enterprise’s own environment when training and serving run there.
- Deployment: the weights can be deployed locally or on the enterprise’s own infrastructure. Adaption’s platform supports this through a weights download.
Where Do Cloud Platforms Like AWS, Google Cloud, Microsoft, and Databricks Fit?
Amazon Web Services, Google Cloud, Microsoft, and Databricks each offer managed fine-tuning inside platforms many enterprises already use. They fit best when governance review favors keeping data where it already lives and the team is content to tune the models each platform lists. Each company documents its supported models and methods, and those lists change often.
- Amazon Bedrock: SFT, RFT, and distillation for Amazon Nova models, and RFT for supported open-source models.
- Google Vertex AI: SFT, preference tuning, and continuous tuning for Gemini models.
- Microsoft Foundry: SFT, DPO, and RFT on supported GPT models, plus some open models.
- Databricks Mosaic AI Model Training: fine-tuning of supported foundation models through an SDK, API, or UI.
Tuned models on these platforms are typically deployed on the same platform that trained them, so confirm whether a tuned model can move to another provider before committing.
Why Does Training Data Matter as Much as the Platform?
Training data quality can limit fine-tuning results as much as the platform does, because most platforms train on whatever examples the customer supplies. Enterprises typically face one of three data situations: uneven existing data, no existing data, or language and regional gaps.
Uneven existing data
Uneven existing data is a training dataset whose examples vary in quality, such as ambiguous instructions or incorrect answers, so the model learns mixed signals. Adaptive Data is Adaption’s data optimization platform, which improves and expands training data across 242 languages and localizations.
Its agentic checklist method removes training examples that miss quality requirements. In Adaption’s own evaluation, models trained on 10,000 checklist-filtered examples had relative win rates 9.4 to 13.2 percent higher than models trained on 10,000 randomly sampled examples (Llama3.3-70B, October 8th, 2026).
No existing data
No existing data means an enterprise has no usable examples of the behavior it wants a model to learn, such as a new workflow or a policy that varies by region. Sara Hooker, Adaption’s co-founder and CEO believes that AI systems should generate the data they need on the fly rather than depend on a large static dataset. Invent a Dataset applies that idea by building a training-ready dataset from a description of the behavior wanted, with no seed data required.
Language and regional gaps
Language and regional gaps are missing or thin training examples for particular languages, dialects, or local contexts, so general-purpose models perform unevenly there. Adaption addresses this with Expand Your World, a feature of Adaptive Data, that localizes existing examples instead of translating them.
In a one-month pilot with AI Singapore, the national program behind the SEA-LION model family, Adaption grew a sample of about 1,050,000 rows to about 1,750,000 and expanded coverage from seven to 11 languages and regional variants, as described in a case study from August 2026.
What Does Automated Fine-Tuning With AutoScientist Change for Enterprises?
AutoScientist is Adaption’s model training product that automates fine-tuning by optimizing training data and the training recipe together until the model meets a stated objective. For enterprises, it replaces manual sweeps over hyperparameters, the settings that control how a model trains, with an automated loop.
In Adaption’s own experiments on dataset sizes from 5,000 to 100,000 examples across eight domains, AutoScientist outperformed human-configured training by an average of 35 percent. Win rates rose from 48 percent to 64 percent against configurations that Adaption’s research staff set using knowledge of model type, domain, and dataset size.
In partnership with AI Singapore, a preliminary experiment with the Qwen3.5-4B base model saved 56 engineering hours of manual hyperparameter optimization.
How Should an Enterprise Choose a Fine-Tuning Company?
Choose the company that removes the enterprise’s main limit: existing cloud commitments, control over open models, training data, or ML staff time.
- If the behavior can be reached by prompting a proprietary model API, use prompting without fine-tuning.
- If governance review favors keeping data on AWS, Google Cloud, Microsoft, or Databricks, choose that platform’s native fine-tuning.
- If the team wants to tune and host open-weight models through an API, choose Together AI or Fireworks AI.
- If ML engineers want full control of training code, choose Hugging Face TRL.
- If the enterprise needs a specialized domain model, has limited ML staff time, and wants to own the tuned model, choose Adaption.
- If a new project was planned around OpenAI fine-tuning, choose another provider, given OpenAI’s schedule.
Author
Adaption TeamDate