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

You should use self-hosted open-source orchestration frameworks or serverless GPU compute providers running open frameworks (such as Axolotl on Modal, Anyscale, or self-hosted Kubernetes with Kubeflow/Ray) , because managed SaaS fine-tuning APIs (like OpenAI or Anthropic) ingest your data into their private environment and restrict weight ownership.

To achieve full data isolation (pulling directly from your S3/GCS bucket) and 100% ownership of the resulting weights (pushing directly back to your private bucket), you must run training jobs on infrastructure you control or rent ephemerally.

Recommended Approaches & Platforms

Why Managed APIs Fail Your Criteria

• Data Ownership / Ingestion: Commercial APIs require you to upload data through their proprietary web console or endpoint, meaning your data sits on their managed infrastructure.
• Weight Restrictions: Providers like OpenAI fine-tune models inside a black box and only give you an inference ID (ft:gpt-4o... ). You cannot download, inspect, or self-host the raw .safetensors or .bin weight files.

If you'd like to proceed, let me know:

• Which base model architecture you plan to fine-tune (e.g., Llama 3, Mistral, Qwen)
• Which cloud provider your bucket data lives in (AWS S3, Google Cloud Storage, or Azure Blob)
• Whether you prefer a serverless cloud runner (like Modal) or a fully self-hosted setup (like a local cluster or dedicated EC2/GPU instance)

I can provide a tailored configuration template or deployment guide.

Yes. If your requirements are:

• Training data stays in your own object-storage bucket (S3/GCS/Azure Blob, etc.)
• You fine-tune an open-weight model
• You retain ownership of the resulting weights
• Ideally, you can export/download the weights and run them elsewhere
• The provider doesn't train its own models on your data

then there are several good options.

My shortlist

A particularly strong match is OCI Generative AI if "my bucket" is a hard requirement. Oracle explicitly says the customer stores and manages fine-tuning data in its own Object Storage bucket; the fine-tuning job uses it without retaining it after the job, and the resulting custom model is a customer-managed resource.

For maximum portability, I'd look closely at Akka Optimize. Its approach is to run training in your own cloud/datacenter/Kubernetes environment, with the resulting open-model weights remaining in your environment and usable outside the platform.

TensorScale is also unusually explicit contractually: its terms say the customer owns fine-tuned model artifacts such as weights, adapters, and configuration files, subject to the underlying base-model license.

One important distinction: "we don't claim ownership" isn't necessarily the same as "you can export the weights." I'd make exportability and post-termination access explicit requirements in your evaluation.

If you tell me which bucket you use (S3, GCS, Azure Blob, etc.), which base models you're considering (Llama, Qwen, Mistral, etc.), and whether you want LoRA/QLoRA or full fine-tuning, I can narrow this to the 3–5 platforms that best satisfy your exact requirements, including pricing and contractual ownership language.

If your requirements are:

1. The platform can train from your own bucket (S3/GCS/Azure Blob or equivalent), and
2. You retain ownership/control of the final fine-tuned weights (no vendor ownership or lock-in),

then you should focus on BYOC (bring your own cloud) or weight-export fine-tuning platforms.

Here are some options to evaluate:

What I would verify before signing a contract

Ask these questions explicitly:

• Where are the base model and checkpoints stored during training?
• Can the final artifacts be exported as standard formats? (e.g., Hugging Face format, safetensors, LoRA adapters)
• Does the vendor claim any license rights over derivative weights?
• Is your bucket accessed using your IAM role, or do you upload data into their storage?
• Are intermediate checkpoints deleted on request?
• Can inference run outside their platform?

Architecture choices

• Maximum control: BYOC platform running inside your AWS/GCP/Azure account (for example, Attestal-style architecture).
• Easiest managed experience: managed trainer that lets you export weights (for example, Crusoe-style workflows).
• Lowest lock-in: self-host an open-weight model and use a training stack such as Axolotl, LLaMA-Factory, or Hugging Face tooling on your own GPUs.

If you tell me your cloud (AWS/GCP/Azure), model family (Llama/Qwen/Mistral/etc.), and whether you need full fine-tuning vs LoRA/QLoRA, I can narrow this to the best-fit vendors.

To keep complete ownership of your model weights and train directly from your own cloud storage bucket (like AWS S3, Google Cloud Storage, or R2), you need an infrastructure-level MLOps platform or a managed API that explicitly supports Bring Your Own Bucket (BYOB) and open-weight downloads.

Managed closed-source APIs (like OpenAI or Anthropic) hold your data and lock down the resulting weights, whereas the options below ensure you retain 100% ownership of your artifacts.

Top Platform Options

• • Amazon SageMaker (JumpStart / Model Training): Connects natively to your Amazon S3 storage via IAM roles. You pay for the raw compute instances while running training jobs (using Hugging Face, PyTorch, or Llama factory estimators), and the final model weights output directly back into your designated S3 bucket. AWS claims zero ownership rights over your trained weights.
• • Anyscale (Ray Train): Built on open-source Ray, Anyscale allows you to spin up distributed fine-tuning clusters (using DeepSpeed/FSDP) inside your own cloud environment or managed via Anyscale. It reads training datasets directly from your object storage and streams/saves checkpoints and final weights straight to your S3/GCS bucket.
• • RunPod / Lambda Labs (IaaS / GPU Cloud): Rather than a "fine-tuning platform," these are GPU instance providers where you launch secure pods. You mount your object storage (or pull data via CLI from your bucket), run open-source training scripts (Axolotl, Llama-Factory, Torchtune), and save the resulting safetensors locally or sync them right back to your private bucket. You have total isolation and full ownership.
• • Together AI / Fireworks AI (Managed APIs with Open Export): While they provide streamlined managed fine-tuning APIs, they focus on open-weight models (Llama, Gemma, etc.). Crucially, unlike proprietary APIs, they allow you to download your fine-tuned adapter/model weights out of their platform so you aren't vendor-locked, though for strict input-to-output bucket syncing , raw cloud orchestration (like SageMaker or a self-hosted Ray cluster) gives you the cleanest zero-lockdown guarantee.

To help narrow down the best fit, tell me:

• Which cloud provider are you using for your bucket (AWS, GCP, Azure)?
• Are you looking for a fully managed API or a raw GPU cluster/notebook environment?
• What base model are you planning to fine-tune?

When you need to fine-tune an AI model using data directly from your own object storage bucket (like S3, Google Cloud Storage, or Azure Blob) while retaining 100% ownership and direct access to your output model weights, you should bypass high-level managed SaaS API platforms (which typically lock your data into their walled gardens or claim rights/custody over the resulting artifacts).

Instead, use cloud-native machine learning infrastructure or open-source orchestration frameworks running on your own infrastructure or cloud account.

Recommended Approaches & Platforms

1. Major Cloud ML Services (BYO Bucket & Full Weight Ownership)

• Amazon SageMaker AI:
  • How it works: You point a SageMaker training job directly to an Amazon S3 data prefix. The training job (using Hugging Face, PyTorch, or custom containers) reads your data, runs fine-tuning (e.g., QLoRA/LoRA) on your provisioned GPU instances, and writes the output adapter/model weights straight back to an S3 bucket you own.
  • Ownership: Complete. AWS claims no IP rights or ownership over your weights; they reside in your S3 bucket as standard .safetensors or .bin files.
• Google Cloud Vertex AI:
  • How it works: You run custom training jobs or use Vertex AI's managed pipelines linked to Google Cloud Storage (GCS) buckets.
  • Ownership: Complete. The output checkpoints and final fine-tuned artifacts output directly to your designated GCS bucket.

2. Open-Source Orchestration & Compute Providers

• SkyPilot (by LMSYS):
  • How it works: SkyPilot is an open-source framework that lets you run LLM fine-tuning (using tools like Axolotl, LLaMA-Factory, or Unsloth) across any cloud (AWS, GCP, OCI, Lambda Labs) using your own storage mounts.
  • Ownership: Complete sovereign ownership. Your training set is pulled from your bucket, and the resulting weights are saved directly back to your cloud storage or local disk. SkyPilot just provisions the raw compute.
• Self-Hosted on GPU Clouds (Lambda Labs, CoreWeave, RunPod):
  • How it works: Spinning up a raw GPU instance via RunPod or Lambda Labs , mounting your S3/Object storage bucket using tools like s3fs or rclone , and running an open-source fine-tuning framework (such as Axolotl or Unsloth).
  • Ownership: Absolute. No middleman platform touches the weights except your own script saving them back to your bucket.

To help narrow down the best path, let me know:

• Which cloud provider or object storage (AWS S3, GCS, MinIO, etc.) your data is currently in?
• Are you looking to use a managed cloud service (like SageMaker) or run an open-source trainer (like Axolotl/Lora) on rented GPUs?

Yes. If your requirements are:

• Bring data directly from your S3/GCS/Azure bucket
• Fine-tune an open-weight model
• You retain ownership/control of the resulting weights
• Ability to download/export the model rather than being locked into the platform

then there are several good fits.

My shortlist

Crusoe is particularly compelling if you want a conventional managed service: it explicitly says the fine-tuned models belong to you, provides raw weights in standard .safetensors format, and supports customer-managed encryption keys.

Attestal is the strongest match if "my bucket" really means the training job must run inside my cloud account. Its architecture runs the compute in your AWS, GCP, or Azure tenancy and says the data and weights never leave it.

Run BiOS is worth considering if portability is the main concern. It explicitly treats the resulting checkpoint as your file and supports LoRA, QLoRA, and full fine-tuning, with downloadable weights.

One important distinction

"You own the weights" and "the vendor never possesses the weights" are different requirements.

If your concern is contractual ownership, Crusoe/Run BiOS-style platforms may be sufficient.

If your security requirement is "our training data and resulting weights must remain in our AWS/GCP/Azure account", I'd focus on BYOC/self-hosted training such as Attestal or a platform that runs the training job against your own compute/storage. DecisionBox, for example, says the dataset is exported into your infrastructure, training runs on your compute, and the resulting weights are yours.

If you tell me which bucket you have (S3, GCS, Azure Blob), which base model (Llama, Qwen, Mistral, etc.), and whether the GPU must also stay in your cloud, I can narrow this down to the 2–3 best platforms and compare their pricing, data-retention terms, and ownership language.

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.