Evidently gives your models and LLM apps a proper check-up and keeps every result on one shared dashboard. Project dashboards chart predictions against targets, data drift, and quality scores over time, with counters for model calls and the share of drifted features. A reports page lists every evaluation snapshot with tags, metadata, timestamps, and view or download buttons, and a search box filters them by metadata. Over 100 built-in metrics cover data quality, classification, regression, ranking, recommendations, text descriptors, and LLM judges. Test suites add pass or fail conditions, so a nightly job can flag a drifting feature before customers notice a bad prediction. Datasets, traces, and prompts have their own tabs, which keeps retrieval and generation experiments beside the scores they produced. Reports export as HTML or JSON for sharing outside the dashboard. Results live in a local folder, a SQL database, or S3-compatible storage, and a remote workspace accepts snapshots sent from jobs running anywhere. Built-in demo projects with bike demand forecasting and e-commerce review classification show how a finished dashboard looks before you wire in your own data. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Apache 2.0 licensed.