Terracotta

Terracotta is a web-based workspace for teams that fine-tune models and need a clearer way to compare the results. Rather than treating fine-tuning as a one-off experiment, it organizes multiple tuned models in one place so you can review ouTPUts, run evaluations, and make decisions about which veRSIon is worth keeping.

What it does

The core IDEa is to bring data upload, Model Management, and eValuation together. You upload the data you plan to use, keep track of the different fine-tuned variants you produce, and then assess them with both qualitative review and quantitative scoring. This helps teams move past gut-level impressions and see which model version ACTually perfORMs better on their use case.

Key capabilities

• Central model management: keep several fine-tuned models grouped and organized so Comparisons stay manageable.
• SECure data upload: the platform emphASIzes safe upload of training and evaluation data, a practical concern for teams working with sensitive material.
• Qualitative evaluation: inspect model responses and judge tone, relevance, and output quality by reading them.
• Quantitative evaluation: use scoring and measurement features to compare performance across models in a more structured way.
• Workspace-style review: review and iterate on fine-tuning results without juggling separate tools for each step.

Who it is for

Terracotta is AImed at developers, ML engineers, and product teams that already have data and want to fine-tune models but struggle to evaluate the results consistently. It also suits research and APPlied-AI groups that need to document why one tuned model was chosen over another. Because it focuses on evaluation and model management rather than end-user chat, it is a better fit for technical teams than for casual users.

How to get started

1. Open the official website and create an account if registration is required.
2. Upload the dataset you intend to use for fine-tuning or evaluation.
3. Connect or start a fine-tuning run and let the platform track the resulting model.
4. Run qualitative and quantitative evaluations and compare outputs side by side.
5. Select the strongest version and continue iterating as needed.

Pricing and access caveat

Terracotta appears to be offered with free access, though the exact limits on data volume, model count, or evaluation runs may vary. Pricing, supported model providers, and feature limits can change, so confirm current terms on the official site before relying on the platform for production work.