---
title: Glossary
url: https://docs.schemalabs.ai/glossary
description: The product vocabulary, one entry per term, identical across the API and the platform.
---

# Glossary

> The product vocabulary, one entry per term, identical across the API and the platform.

## Product objects

**Data Language Model (DLM)**: a foundation model that understands tables natively, from raw cell values, and returns understanding, relationships, missing values, and predictions in one pass. Schema models are DLMs.

**Base**: a Schema model generation, named `schema-{n}` (`s{n}` in report names). Runs and new endpoints use the latest base unless you name one. [Models](https://docs.schemalabs.ai/models).

**Quick run**: `POST /v2/run`. A stateless pass over the tables in the request that returns the full bundle and persists nothing. Its prediction is labeled `tier: "in_context"`. [Quick run](https://docs.schemalabs.ai/api-reference/run).

**Endpoint**: your persistent object: a base, pinned dataset snapshots, a configuration (target, task, thresholds), a library of system prompts, and a held-out report, served at a stable URL. [Endpoints](https://docs.schemalabs.ai/endpoints).

**Serve**: `POST /v2/serve/:id`. Live inference over the request tables, scored against the endpoint's pinned data, returning the bundle. Needs the `serve` scope.

**Refresh**: `POST /v2/endpoints/:id/refresh`. Re-syncs the pinned snapshot, scores held-out on a fresh split, and mints a new report. The only operation that makes an endpoint stop reflecting removed rows.

**Upgrade**: `POST /v2/endpoints/:id/upgrade`. Re-runs the same pinned data on a newer base and mints a new report.

**System prompt**: a named instruction fragment (`sp_...`) on an endpoint that steers an LLM. Executed in the platform's Chat; exposed on the endpoint object so your own LLM can apply it.

**Job**: an async unit of work (`job_...`) for endpoint creation, refresh, upgrade, synthetic generation, and large or batch runs. Poll `GET /v2/jobs/:id`. [Jobs](https://docs.schemalabs.ai/api-reference/jobs).

**Batch**: a service tier elected per job with `"options": { "processing": "batch" }`. Runs with a completion window and bills the batch rate ($1.50 / Mcell).

**Scope**: what an API key may do: `read`, `run`, `serve`, `manage`, `delete`. [Authentication](https://docs.schemalabs.ai/authentication).

## Data

**Connection and dataset**: a connection (`conn_...`) is where data comes from; a dataset (`ds_...`) is an ingested or registered table. Both live under `/v2/data`.

**Snapshot**: an immutable copy of a dataset minted by each ingest or sync, id `dsv_...`. Endpoints pin snapshots and held-out reports stamp them.

**Pinned**: a dataset state. Pinned data serves repeat calls at the cached rate indefinitely and counts toward plan storage. Endpoint creation pins its datasets automatically. [Data](https://docs.schemalabs.ai/data#pinning).

## Outputs

**Bundle**: the one output shape every run, creation, and serve returns: sector, column profile, cross-table map, missing-value imputation, and predictions, each with its own confidence. [Outputs](https://docs.schemalabs.ai/outputs).

**Pass**: one inference over a set of tables. Every pass emits the full bundle; there is one rate per pass, with no per-output charge.

**In-context**: how every prediction is made: from the labeled rows present (the request's, or the endpoint's pinned data). Nothing trains.

**Target and task**: the column to predict and the prediction type (`classification`, `regression`, `anomaly`). Both default to `auto`; the response states what was chosen and why.

**Sector**: the industry or domain a table belongs to, returned as `top1` and `top5` with a confidence each. Detection is sector-agnostic: it understands the values, not the column names.

**Column profile**: the per-column understanding: `role`, `role_confidence`, `type`, `missing_pct`, `pii`.

**Cross-table map**: how two or more tables relate without shared keys: column alignments, entity matches, and a unified schema, each with a confidence.

**Column alignment**: a pair of columns from two tables that carry the same attribute, with its `confidence` and a verbatim `example` from each side.

**Entity match**: a pair of rows from two tables that describe the same real-world entity, with the attributes it was matched on.

**Unified rows**: the cross-table map materialized into merged records, one per matched entity, with provenance per field and the raw variants of shared values.

**Missing-value imputation**: filled values for empty cells, numeric and categorical, with a row-level confidence. Original cells are never overwritten.

## Trust

**Held-out**: the score on rows whose target values the model did not see when predicting them, measured by an in-context split at creation, refresh, and upgrade, stated with its split, seed, baseline, and snapshot. [Held-out reports](https://docs.schemalabs.ai/held-out).

**Report**: the immutable trust artifact minted by each create, refresh, and upgrade. Id `r_...`, display name `{endpoint}.{base}.{op}.{YYYY-MM-DD}`. [Reports](https://docs.schemalabs.ai/api-reference/reports).

## Metering

**Cell**: one value, one row by one column. The metering unit. 1 Mcell = 1,000,000 cells. Missing cells count like any other cell. [Usage and billing](https://docs.schemalabs.ai/billing).

**Fresh and cached**: fresh cells are understood for the first time and bill the fresh rate ($3.00 / Mcell); cached cells are unchanged pinned data re-used on a later pass and bill the cached rate ($0.30 / Mcell).