Tune it.
Deploy it.
Own it.
Iterate faster with Serverless Fine-Tuning.
No infrastructure wrangling. No surprise bills. Just breakthroughs.
Fine-tuning, simplified








Choose from top LLMs to fine-tune
More discovery. Less drudgery.
Improve accuracy, fix outputs, and ship faster.
Tune reliably. Scale seamlessly.
With Serverless Fine-Tuning in Crusoe Intelligence Foundry, you can quickly customize state of the art models on proprietary data, without provisioning clusters. Use Self-Serve Deployments for one-click inference — same platform, same data isolation, zero friction.
Resources
Resources to help you configure, launch, and evaluate fine-tuning jobs without rebuilding your stack.
asked questions
Crusoe Serverless Fine-Tuning bills per token processed during training, not per GPU-hour. GPU-hour pricing charges for the entire time a machine is reserved, including setup, idle time, queueing, and failures, so your cost depends on infrastructure efficiency you don't control. Token-based pricing ties spend directly to actual training work, making costs predictable from your dataset size and epoch count, with no GPUs to provision or right-size. You also only pay for what works: early stopping ends the job, and the billing, the moment your model stops improving, so you're never charged for epochs that add no accuracy.
Crusoe Serverless Fine-Tuning is a managed workflow, so you don't have to bring your own training code or container. Instead, it runs a tuned LoRA (Low-Rank Adaptation) pipeline built on proven defaults, and you customize the configuration that matters: your base model, your dataset in standard JSONL and Parquet formats, and the hyperparameters and training settings for each job. You can submit and adjust jobs through the UI, SDK, or API, which gives you control over how a model is tuned without having to write or maintain the underlying training infrastructure. If your use case needs fully custom training code, that's better suited to Crusoe Cloud, where you manage the environment yourself.
Crusoe Serverless Fine-Tuning supports a curated library of leading open LLMs, so you can pick the right fit for your use case, whether you need a lightweight model or frontier reasoning power. The library is updated as new open models are released, and you can browse the full, current list here.
Yes. Crusoe Serverless Fine-Tuning is built with no lock-in, so your model goes with you. When a job finishes you can deploy it in one click to Self-Serve Deployments for inference, or download the raw weights in standard .safetensors format and host them anywhere, whether that's your own infrastructure or another model deployment platform. Every fine-tuned model also carries full lineage back to the exact dataset, configuration, and eval that produced it, so a run stays reproducible and auditable wherever you choose to run it.
Your fine-tuned models belong to you, not Crusoe. Crusoe operates with zero data sharing, so your data trains your models only and is never used to improve shared or third-party models. Training and storage run in a tenant-isolated environment, meaning your datasets and resulting weights stay private to your account rather than being pooled across customers. From there you decide what happens: deploy the model via Self-Serve Deployments or download the weights to host elsewhere. For specific data-retention and deletion terms, visit our privacy documentation.
Crusoe brings the entire fine-tuning journey under one roof, from picking a base model to serving a live endpoint, so you skip the usual patchwork of separate tools for training, hosting, and deployment. Smart defaults and built-in best practices mean your team can ship a working model quickly without deep ML-ops expertise. Underpinning all of it is Crusoe Cloud's AI-optimized infrastructure, which delivers scalability and automatic recovery without you having to do cluster management. The result is the ease of a managed service paired with the reliability, security, and cost efficiency of infrastructure designed specifically for AI.
Are you ready to build something amazing?




