ModelDB vs Trackio

ModelDB has no write-up on Specifiction yet; its figures are above.

Trackio is a free Python library for recording and comparing machine-learning experiments, built on Hugging Face Buckets and Spaces. Its API follows the wandb.init, wandb.log, and wandb.finish patterns and is documented as a drop-in replacement for compatible workflows. A dashboard runs locally by default, while logs can be stored locally, in a Hugging Face Bucket, or on a self-hosted server. Runs record metrics and configuration, and the dashboard groups them for comparison. Logging can include tables, Markdown reports, images, video, audio, 3D objects, HTML, and Matplotlib or Plotly figures. Optional packages enable NVIDIA GPU or Apple Silicon monitoring. Trackio documents integrations with Transformers and TRL, plus RapidFire AI for fine-tuning and RAG experiments. Alerts can be printed, stored, queried, shown in the dashboard, or sent through Slack and Discord webhooks. The dashboard can also run as an HTTP API and MCP server; mutation tools are gated by a write token. For self-hosted servers outside Hugging Face Spaces, ingestion and uploads require a write token that the documentation says to keep secret on untrusted networks. Trackio has no first-class hyperparameter sweep API; its docs describe using Python loops with one run per configuration.