# Needle hosted fine-tuning pricing

These plans cover hosted fine-tuning in the Cactus console (https://cactuscompute.com/dashboard): data generation, training on our GPUs and evaluation. Downloading the base model and running any model locally is not billed.

Fine-tune locally for free, or use the platform for hosted GPUs, full-model training, a trained confidence head and 2-bit exports. Run your finished models on your own devices.

Free: $0 for local LoRA fine-tuning with the open-source package. Bring your own hardware and data; no hosted compute, generation or platform storage is included.

Start locally: https://github.com/cactus-compute/needle#customisation

Starter: $19 one-time for 3 fine-tunes, 3,000 generated examples, and 10 GB storage. Use within 30 days. Once per account before subscribing; no automatic renewal.

Pro: $99 per month for 10 fine-tunes, 10,000 generated examples, and 10 GB storage. Run and generation allowances reset each billing period. Cancel anytime.

Custom: contact us for higher limits.

Starter uses API plan ID `starter`; Pro uses API plan ID `standard`. Existing purchases and checkout eligibility are unchanged.

## Local vs. platform fine-tuning

What trains — Free: LoRA adapters on attention projections. Base frozen; adapters merged at export. Platform (Starter, Pro, Custom): The full model, at every supported depth from 2 layers up.

Original capabilities — Free: Your training data only. Platform (Starter, Pro, Custom): Your data augmented with Needle’s original dataset to help retain base capabilities.

Confidence — Free: Confidence head untouched; confidence is not available on the fine-tuned model. Platform (Starter, Pro, Custom): Confidence head fine-tuned with the model and calibrated on your tools.

Export precision — Free: 4-bit. Platform (Starter, Pro, Custom): 2-bit, with the same post-training process as the shipped model.

Training data — Free: Bring your JSONL in query / answers or chat format. Platform (Starter, Pro, Custom): Upload your data or generate synthetic examples from your tool definitions, within your plan’s allowance.

Evaluation — Free: Validation loss. Platform (Starter, Pro, Custom): Validation and test accuracy for every depth, so you can compare model sizes.

Compute & storage — Free: Your machine and disk. JAX on CPU, CUDA or Metal. Platform (Starter, Pro, Custom): Cactus GPUs and platform storage for datasets and model exports.

Run from — Free: The CLI: needle finetune. Platform (Starter, Pro, Custom): The dashboard, CLI, Python, or a coding agent with your API key.

Choose Free for local LoRA training on your own hardware. Choose Starter to test the full hosted pipeline before committing; choose Pro for ongoing development. If the free base model already meets your requirements, you do not need to fine-tune.

The human purchases at https://cactuscompute.com/dashboard/billing and creates an API key at https://cactuscompute.com/dashboard/api-keys; agents do not purchase or manage subscriptions.

Starter is a one-time purchase for one account before subscribing. Access and unused allowances expire after 30 days; there is no automatic renewal.

Pro is a monthly subscription. Upgrading starts fresh Pro allowances and preserves files and models. Unused Starter allowances do not carry over, its payment is not credited, and it cannot be purchased again.

Starter and Pro include the same model sizes, full-model training on hosted GPUs, 2-bit exports, confidence-head training, original-dataset reinforcement, synthetic example generation, evaluation, and playground access. Training and generation spend separate allowances; generated datasets can be reused across runs.

Storage includes uploaded files, generated datasets, model exports, and reserved output space. Jobs reserve up to 512 MiB for output. Failed jobs return their allowance; cancelled jobs do not.

After access ends, files and models remain available to download for another 30 days before they may be deleted. An active paid plan preserves them. Downloaded models remain usable locally.

Generation creates synthetic examples from tool definitions and optional product context. Original-dataset augmentation happens during training; it does not rewrite or clean your uploaded files. Review generated data and evaluate on independent examples from your use case.

There is no hosted inference endpoint. Run downloaded models locally with the open-source runtime. Accuracy improvements depend on your data and task.