Crusoe Serverless Fine-Tuning
Tune it.
Deploy it.
Own it.
Iterate faster with Serverless Fine-Tuning.
No infrastructure wrangling. No surprise bills. Just breakthroughs.
Fine-tuning, simplified
How it works
01
Select your base model
Select your base model
Choose from a curated library of top models. Find the perfect fit for your specific use case, whether you need lightweight agility or frontier reasoning power.
02
Load your dataset
Load your dataset
Upload your training data to your isolated environment in standard JSONL or Parquet format.
03
Configure your fine-tuning job
Configure your fine-tuning job
Tune the settings with pre-configured defaults built on best practices. We run the LoRA (Low-Rank Adaptation) workflow for fast, precise, and cost-effective fine-tuning. Submit your job via the UI, SDK, or API.
04
Deploy or download your model
Deploy or download your model
Go live in one click using Self-Serve Deployments for inference in Crusoe Intelligence Foundry, or download your weights to deploy anywhere.
Choose from top LLMs to fine-tune
Kimi
models
Model
Parameters
Context
Yutori
models
Model
Parameters
Context
DeepSeek
models
Model
Parameters
Context
Google
models
Model
Parameters
Context
OpenAI
models
Model
Parameters
Context
Meta
models
Model
Parameters
Context
Alibaba
models
Model
Parameters
Context
Z.ai
models
Model
Parameters
Context
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NVIDIA
models
Model
Parameters
Context
More discovery. Less drudgery.
Improve accuracy, fix outputs, and ship faster.
Features
Your workload data. Your models. Your keys.
Trust the infrastructure underneath your models.
Bring your own KMS keys to encrypt fine-tuning data and model artifacts with Crusoe Customer-Managed Keys (CCMK) — no exceptions, no shared custody.
Permission is yours to revoke at any moment. We never hold your key in plaintext; we simply ask for permission to use it.
One less blocker for security and compliance teams navigating SOC 2 or ISO 27001 requirements
Our early experience with Crusoe's Serverless Fine Tuning product was seamless, and it worked like a charm. We look forward to leveraging it to optimize the latency and cost of our AI agents as we scale our infrastructure.
Resources
Resources to help you configure, launch, and evaluate fine-tuning jobs without rebuilding your stack.
Frequently
asked questions
asked questions
How does token-based/per-job pricing work vs. GPU-hour pricing?
Can I upload my own training code / custom configs?
What models are supported?
Can I download my fine-tuned weights (safetensors) and host them elsewhere?
Do I retain the models I fine-tune, or are there restrictions?
What sets Crusoe serverless Fine tuning apart from other platforms?
Are you ready to build something amazing?
