I am looking for a fine-tuning platform that uses my bucket data and does not take ownership of the final model weights.

Managed serverless platforms like Together AI or running open-source training scripts on cloud compute (like AWS, GCP, or Lambda Labs) allow you to use your own bucket data while retaining 100% ownership of your final model weights.

When you fine-tune open-weight models (such as Llama or Mistral) rather than proprietary closed models (like OpenAI or Anthropic), the platform or infrastructure provider grants you complete ownership of the resulting adapter or merged weights, meaning you can download them and run them anywhere.

Top Options for Bucket Data & Weight Ownership

• Together AI: Lets you upload data to train open-source models and provides fully downloadable fine-tuned model weights upon completion so you are never locked into an ecosystem.
• Self-Hosted Cloud Compute (AWS EC2 / GCP / Lambda Labs): Spinning up an instance, pulling your dataset straight from your private S3 or Google Cloud Storage bucket, and running training frameworks like Axolotl or LLaMA-Factory ensures your data never leaves your perimeter and the weights output directly to your storage.
• Modal / Baseten: Serverless infrastructure providers where you write or deploy custom training code, connect your own object storage buckets, and save artifacts directly back to your own cloud storage with absolute intellectual property ownership.

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

• Which base model (e.g., Llama 3, Mistral) you plan to fine-tune
• Your preferred cloud provider or if you need a fully managed API interface
• Whether your data requires zero third-party data retention (strict enterprise compliance)

Yes. If your requirements are:

• Training data stays in your cloud bucket (e.g. S3/GCS)
• You can fine-tune an open-weight model
• The resulting weights belong to you, and you can download/export them
• The vendor does not claim ownership or broad rights over your model/data

then there are a few strong candidates.

Best fits

1. together.ai — probably the closest match. Together explicitly advertises “full control over your model weights” and says you can download the resulting checkpoint and use it locally.

• Good for: Llama, Gemma and other open-weight models
• Fine-tuning: SFT, DPO, continued training
• Weights: downloadable
• Caveat: I'd verify the current enterprise contract regarding ownership versus merely having download/access rights.

2. fireworks.ai — particularly interesting if BYO bucket is important. Fireworks says customers maintain ownership of their data and supports customer-controlled cloud storage; its security documentation also describes customer-controlled model checkpoints and external S3 storage.

• Good for: production fine-tuning + inference
• Data: customer-controlled storage is supported
• Weights: supports full model weights and LoRA checkpoints
• Strong enterprise/security story
• Important: their current documentation says GCS bucket integration for datasets, with AWS S3 listed as coming soon, while external S3 is supported for models.

3. Amazon Bedrock Custom Model / Fine-tuning — worth considering if your data is already in S3. Bedrock can directly access training/validation data in your S3 bucket through an IAM role, and output data can also go to S3.

• Excellent if you're already AWS-native
• Strong IAM/VPC/KMS controls
• But I'd not assume it meets your “I own the final weights” requirement without reviewing the applicable AWS terms for the specific model/customization method.

One important distinction

“You can download the weights” and “you own the weights / vendor has no ownership claim” are legally different.

If this is for proprietary training data or a commercial product, I'd specifically look for contract language saying something like:

Customer retains all right, title, and interest in Customer Data and Customer-created/fine-tuned model artifacts, subject only to the underlying base-model license. I'd also check whether the platform receives any license to use, reproduce, train on, or improve models using your fine-tuned weights.

My shortlist would be Together AI first, Fireworks second, and AWS Bedrock if S3-native infrastructure is the priority.

If you tell me which bucket you have (S3, GCS, Azure Blob), which base model (Llama/Qwen/Mistral/etc.), and whether you need full weights or LoRA adapters, I can narrow this down to 3–5 platforms and compare data ownership, weight ownership, bucket integration, training cost, and vendor lock-in.

To satisfy the strict requirements of using your own storage bucket (S3-compatible) and maintaining 100% ownership of your model weights/artifacts , you need either an enterprise cloud-native service configured via your own IAM/storage permissions or an open-source framework run on your own compute infrastructure.

Managed third-party SaaS fine-tuning APIs (like OpenAI or Anthropic) take custody of data and weights, so you must look at BYOC (Bring Your Own Cloud) / BYOB (Bring Your Own Bucket) architectures or self-hosted orchestrators.

Recommended Approaches & Platforms

1. Amazon SageMaker AI (JumpStart / Model Customization)
   • How it works with your bucket: You point SageMaker directly to an Amazon S3 bucket location for your training/validation datasets.
   • Weight Ownership: The resulting fine-tuning artifacts and model weights are outputted straight back into your designated S3 bucket. AWS does not claim ownership or rights to your custom weights.
   • Best for: Enterprises already operating inside AWS with strict data governance policies.
2. TrueFoundry
   • How it works with your bucket: TrueFoundry can be deployed inside your own cloud (K8s/AWS/GCP) via a BYOC model. It connects directly to your object storage for reading datasets.
   • Weight Ownership: Because the jobs execute inside your cloud infrastructure and connect to your storage, the model weights land in your storage, retaining complete organizational ownership.
   • Best for: Teams looking for a platform UI/orchestrator that deploys inside their private cloud perimeter.
3. Self-Hosted Open-Source Frameworks (Axolotl, LLaMA-Factory, or Torchtune)
   • How it works with your bucket: You spin up an ephemeral GPU instance (on Lambda Labs, RunPod, AWS EC2, etc.), mount or sync your S3 bucket via s3fs or the AWS CLI to pull training data, and execute the training script.
   • Weight Ownership: Total and absolute. The output adapter/weights write directly back to your local disk or S3 bucket. No third-party platform touches or tracks the weights.
   • Best for: Maximum control, privacy, and zero reliance on commercial platform abstraction layers.

To help narrow down the best path, could you share:

• Are you looking for a fully managed cloud service (like AWS) or an open-source tool/framework you deploy yourself?
• Which cloud provider or infrastructure (AWS, GCP, on-premise) do your buckets reside in?

If your requirements are:

1. The platform trains from data in your own bucket (S3/GCS/Azure Blob or equivalent), and
2. The platform does not claim ownership of the resulting fine-tuned weights,

then you should focus on open-weight model fine-tuning platforms or BYOC (bring-your-own-cloud) training platforms, rather than closed-model customization APIs.

Some options to evaluate:

A few things to check before signing:

• Weight export: “You own the model” can mean either:

• you can download actual weights (.safetensors, adapters, checkpoints), or

• you only get API access to a hosted endpoint.

Prefer the former if portability matters.

• Base model license: The fine-tuned weights may inherit restrictions from the underlying model (for example, Llama-family, Qwen, Mistral, etc.).

• Data path: Confirm whether the service:

• reads directly from your bucket,

• copies data into vendor storage,

• keeps checkpoints in your account,

• deletes intermediate artifacts after training.

• Adapter vs full fine-tune: Many platforms train LoRA/QLoRA adapters. That may satisfy your ownership requirement, but confirm whether you receive:

• adapter weights only, or

• merged full-model weights.

For a strict enterprise requirement (“our bucket, our keys, our weights, vendor never possesses custody”), I would shortlist BYOC platforms (Attestal-style), self-hosted Databricks/Azure/AWS training stacks, or managed open-model fine-tuning services that explicitly provide weight export.

If you tell me your bucket type (S3, GCS, Azure Blob), model family (Llama/Qwen/Mistral/etc.), and whether you need full weights or LoRA adapters, I can narrow this down.

When you need a platform that pulls training data directly from your own cloud bucket (like Amazon S3 or Google Cloud Storage) and leaves you in complete ownership of the resulting model weights/artifacts , fully managed SaaS fine-tuning APIs (like OpenAI or Anthropic) won't work because they lock the weights into their closed ecosystems.

Instead, you need Infrastructure-as-a-Service (IaaS) / MLOps platforms or bring-your-own-cloud systems where the compute runs in your environment or saves outputs directly to your private storage.

• Amazon SageMaker AI / JumpStart : You point the training job to an Amazon S3 bucket containing your dataset. The training runs on AWS compute instances you spin up, and the final fine-tuned model weights and adapters are written straight back to your designated S3 bucket. You retain 100% ownership of the artifacts and can download or deploy them however you choose.
• Databricks (Mosaic AI Training) : Integrates tightly with cloud object storage via Databricks Unity Catalog . It reads your data directly from your cloud buckets, executes distributed fine-tuning jobs (using open-weight models like Llama), and saves the resulting model weights back to your storage or registry without taking ownership of your intellectual property.
• Self-Hosted Open-Source Frameworks (SkyPilot / Anyscale / Vertex AI Custom Jobs) : If you orchestrate training via a tool like SkyPilot on your own cloud account (AWS/GCP), it pulls data from your private bucket, provisions cheap spot or on-demand GPUs, performs QLoRA/SFT training, and syncs the weights right back to your bucket. You own the infrastructure, the data, and the weights entirely.

To help narrow down the best fit, could you tell me:

• Which cloud provider your bucket is hosted on (AWS, GCP, Azure)?
• Are you looking for a fully managed cloud service (like SageMaker) or an open-source orchestrator you run yourself?

Yes. If your key requirements are:

1. Fine-tune using data in your own bucket (e.g. S3/GCS/Azure Blob)
2. You retain ownership of the resulting weights
3. You can export the weights and run them elsewhere
4. Ideally, your training data isn't incorporated into the vendor's shared models

I found several good candidates.

My top 3

1. Tuning Engines — closest match if your data is already in S3.

Their documentation explicitly says you can export a fine-tuned model to an S3 bucket, including the weights/config/tokenizer, and explicitly states: “You own the weights.” You can also import a model from your S3 bucket.

2. Crusoe — best polished managed option I found.

Crusoe's serverless fine-tuning lets you download the resulting weights as standard .safetensors and host them wherever you want. They explicitly state that the fine-tuned models belong to you and that customer data isn't used to improve shared/third-party models.

3. Attestal — best if the bucket/data must never leave your cloud account.

This is particularly interesting for a security-sensitive setup. Attestal runs the fine-tuning pipeline inside your AWS, GCP, or Azure account; its documentation says your data and weights never leave your environment. The platform orchestrates the job rather than taking possession of the artifacts.

One important distinction

I'd separate “you own the model” from “you possess/export the weights.”

For example, SchemaLabs says the customer owns the fine-tuned checkpoint, but the checkpoint is not exportable and is only accessible through its API/web app. schemalabs.ai That's probably not what you want if avoiding vendor lock-in is a requirement.

Conversely, platforms like Crusoe explicitly provide the actual weight files, which is a much stronger guarantee.

If you tell me which bucket you're using (S3, GCS, Azure Blob, Cloudflare R2, etc.) and which base models you want to fine-tune (Llama, Qwen, Mistral, Gemma, etc.), I can narrow this down to 3–5 platforms that meet all of those requirements, including pricing and whether the data has to leave your cloud account.

Finding a fine-tuning platform that leaves you in full control of your data and intellectual property (retaining 100% ownership of your model weights) is essential for enterprise privacy and compliance. Here is a breakdown of top platforms that allow you to connect your own storage bucket (like AWS S3 or Google Cloud Storage) and output weights directly back to your infrastructure without claiming ownership.

• Anyscale : Built on Ray, Anyscale allows you to run distributed fine-tuning jobs (using OSS frameworks like Llama Factory, Axolotl, or native Ray Train) directly inside your own cloud infrastructure (BYOC - Bring Your Own Cloud). Your data stays in your bucket, the training runs in your environment, and the final weights are saved directly to your storage.
• Hugging Face AutoTrain / Spaces (Enterprise/Dedicated) : While standard Hugging Face handles things on their managed infrastructure, Hugging Face offers enterprise solutions and Dedicated Accelerators where you can run training jobs connecting to private data sources and export/push weights directly to your own private model repository or cloud bucket.
• RunPod / Latitude.sh (Infrastructure Providers) : If you want maximum control without a managed orchestration layer taking ownership, you can spin up dedicated GPU instances on providers like RunPod or Latitude.sh . You can pull data from your S3-compatible bucket via CLI/API, run your own training scripts (e.g., using torchtune or LLaMA-Factory ), and push the resulting weights back to your bucket. You own everything completely.
• BentoML / Yatai : For teams looking to orchestrate the pipeline themselves, BentoML provides open-source tooling to manage fine-tuning and model serving on your own Kubernetes clusters, ensuring weights remain entirely within your storage ecosystem.

Would you like to explore setting up an open-source training script on a raw GPU instance, or do you prefer a fully managed BYOC platform like Anyscale?

Yes. If your requirements are:

1. Use data directly from your own bucket (S3/GCS, rather than handing the platform permanent custody of the dataset).
2. Fine-tune an open-weight model.
3. You retain ownership/control of the resulting weights, including the ability to download them and run them elsewhere.

I’d shortlist these:

1. Baseten — probably the closest match

Baseten explicitly says “full ownership of your trained weights” and that all artifacts—including model weights, evaluations, and training scripts—belong entirely to you.

More importantly for your bucket requirement, its training infrastructure supports loading training data directly from S3, GCS, Hugging Face, or Baseten's own delivery layer.

So you can have roughly:

Your S3/GCS bucket → Baseten training job → your fine-tuned checkpoint → your infrastructure

Baseten also says its inference system can load weights directly from sources such as S3/GCS, and it doesn't store model weights by default.

I'd investigate Baseten first if “the model is ours, not theirs” is a hard contractual requirement.

2. Together AI — excellent managed option

Together explicitly states that your data and models remain fully under your ownership.

It supports both LoRA and full fine-tuning, and its API allows you to download the resulting model weights, including checkpoints and LoRA adapters.

Their documentation also says they don't store inputs/outputs by default, with training-data sharing opt-in rather than enabled by default.

The catch for your specific requirement is that their normal fine-tuning workflow expects a training file uploaded through their Files API, rather than simply mounting your existing S3 bucket into the training job.

So I'd put Together second if “don't copy my bucket data into the platform” is important.

3. Self-hosted training

If the requirement is actually “the platform must never obtain ownership or custody of my data/model”, the strongest architecture is to use something like managed GPU infrastructure but run your own training stack (PyTorch/Transformers/TRL/DeepSpeed, etc.) against your bucket.

That gives you:

S3/GCS → your training environment → your bucket

with no platform-level model ownership question.

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My recommendation

I'd distinguish two requirements:

• “The vendor may process/store my data, but I must retain IP ownership of the weights.” → Together AI or Baseten
• “The vendor shouldn't take custody of my bucket data or weights at all.” → Baseten with direct bucket ingestion, or self-hosted training
• “I need the contractual right to download the actual full checkpoint and run it anywhere.” → Baseten and Together both look promising; get the current MSA/DPA/terms reviewed before relying on the marketing language.

For your exact wording—“uses my bucket data and does not take ownership of the final model weights”—Baseten is the strongest match I found.

If you tell me AWS S3 vs GCS, which base model (Llama/Qwen/etc.), and whether you need LoRA or full fine-tuning, I can narrow this down to 3–5 platforms and compare data residency, bucket access, weight ownership, deletion guarantees, VPC/private networking, and pricing.

Yes. If your requirements are:

1. Fine-tune using data that stays in your own cloud bucket (S3/GCS/Azure Blob), and
2. You retain ownership/control of the resulting weights,

then Fireworks AI and Together AI are the strongest candidates I’d investigate first.

1. Fireworks AI — probably your best match

Fireworks has a Secure Training / BYOB offering specifically designed around this requirement. You can point training at your own GCS, S3, or Azure Blob bucket, with least-privilege access. Their documentation says the training data remains in your bucket and is only accessed during the fine-tuning workflow.

They also state that your training data is used solely to fine-tune your models and isn't used or shared for other purposes, with a zero-data-retention posture.

This is especially attractive if you want:

Your bucket → fine-tuning job → your resulting model/LoRA → deploy wherever you choose

rather than uploading the dataset into a vendor-controlled training repository.

2. Together AI

Together explicitly states that your data and models remain fully under your ownership. www.together.ai Their privacy documentation also says inputs/outputs aren't stored by default and that sharing data for training other models is opt-in.

I'd put Together very high on the shortlist, but I'd verify the exact "training directly from my existing S3/GCS bucket" workflow for your particular account/enterprise configuration before signing.

3. Replicate — ownership is good, but I'd scrutinize the contract

Replicate's terms are quite explicit that you retain ownership of your Customer Data and that Replicate assigns you its rights in Output.

However, the same terms give Replicate a license to process your data for providing the service, training and generating Customer Derivative Models, etc. That's not necessarily bad, but if your requirement is "the vendor must never have rights beyond operating my training job" I'd want legal to review that language rather than relying on the headline ownership clause.

What I'd require in the contract

Don't stop at "you own your model." Ask the vendor to confirm these five things in writing:

• Dataset: remains in your AWS/GCP/Azure account.
• Training: vendor gets only temporary, least-privilege access to the required bucket/path.
• Weights: you own the resulting full fine-tuned weights and/or LoRA adapters.
• Training rights: vendor cannot use your dataset, gradients, checkpoints, or weights to improve its general models.
• Deletion: temporary copies, checkpoints, logs, and cached datasets are deleted after the job according to a defined retention period.

If bucket-native training + ownership of the resulting weights is the decisive requirement, I'd start with Fireworks AI, then compare Together AI against it. Fireworks has unusually explicit documentation around the exact BYOB architecture you're describing.

If you tell me AWS S3 vs. GCS vs. Azure, and whether you're fine-tuning Llama/Qwen/Mistral/etc., I can narrow this down to 3–5 platforms and compare their actual ownership/retention terms.

When you want to fine-tune models using your own cloud object storage (like an S3 bucket) without handing over ownership of your resulting model weights to a managed SaaS platform, you generally need to look at cloud-native machine learning services or bring-your-own-bucket (BYOB) infrastructure frameworks rather than standard consumer or API-locked LLM wrappers.

Here are the best ways and platforms to achieve this:

• Amazon SageMaker AI: Through Amazon SageMaker JumpStart , you can pull training datasets directly from your own Amazon S3 buckets and output the final fine-tuned model artifacts (weights and checkpoints) back to an S3 path within your own AWS account. AWS does not claim ownership of your weights.
• Amazon Bedrock (Custom Model Import): While standard Bedrock fine-tuning hosts the weights inside their managed service, Amazon Bedrock Custom Model Import allows you to train a model independently (e.g., using open-source toolkits in your own cloud environment) and import the weight artifacts residing in your S3 bucket into Bedrock for private serving.
• Self-Managed Cloud Batch + Axolotl: Using open-source fine-tuning frameworks like Axolotl deployed on serverless or batch infrastructure (such as Google Cloud Batch or AWS EC2), you control the pipeline entirely. The data is read straight from your storage bucket, and the final weights are written directly back to your secure storage.
• Weights & Biases (BYOB Architecture): If you use Weights & Biases with their dedicated or enterprise infrastructure, their Bring Your Own Bucket (BYOB) secure storage connector ensures that artifacts, metrics, and training run files remain isolated inside your cloud infrastructure without copying proprietary data over to their managed multi-tenant infrastructure.

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

• Which cloud provider you are using (AWS, GCP, Azure)
• Whether you prefer a fully managed service (like SageMaker) or an open-source framework (like Axolotl/Hugging Face) to run the training job

I can give you a more specific architectural setup or configuration guide.