> ## Documentation Index
> Fetch the complete documentation index at: https://docs.platform.toloka.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# How the platform works

> Choose Data Labeling or Train, then work with shared projects, datasets, and a team balance.

Toloka Platform has two product workflows: **Data Labeling** produces labeled data, and **Train** produces a Model Version you can download. They share projects, datasets, team access, and a team balance.

## Choose your starting point

| If you have… | Start with… | You get… |
| - | - | - |
| Data that needs labels, judgments, or transformations | [Data Labeling](https://docs.platform.toloka.ai/docs/data/get-started/first-labeled-dataset.md) | Labeled results in your dataset and a downloadable export |
| Labeled conversations to teach a model a task | [Supervised fine-tuning](https://docs.platform.toloka.ai/docs/train/guides/fine-tune.md) | A LoRA adapter for its Base model |
| Example conversations and a prompt to compress | [Gisting](https://docs.platform.toloka.ai/docs/train/guides/gist.md) | A full model with a learned prompt representation |

You do not need to train a model to label data. You do not need a labeling pipeline to train from an existing dataset.

## Shared resources

A **team** owns projects and their balance. Access follows your team membership. Use [Teams, access, and balance](https://docs.platform.toloka.ai/docs/core/teams-and-balance.md) to bring in a colleague or add funds.

A **project** holds the datasets and work for a task. Its pipelines belong to Data Labeling; its Trainings, Training Runs, Models, and Model Versions belong to Train. See [Projects](https://docs.platform.toloka.ai/docs/core/projects.md).

A **dataset** holds items and fields. Data Labeling reads and writes those fields. Train reads selected rows as conversation examples. The same project can use both workflows, but a labeling export is not automatically a training file: [prepare the labeled results](https://docs.platform.toloka.ai/docs/train/guides/prepare-training-data.md) in the required conversation format.

## Data Labeling: describe, review, run, export

Describe your task to the project agent. It builds a **pipeline**, a connected set of nodes that labels, transforms, or routes your items. You review the configuration, resolve its checks, and approve the cost before deployment.

A **pipeline run** processes the selected items. Labeling nodes use experts or, where supported, an LLM. Quality-control settings determine how labels are reviewed. You can inspect results and export them from the run.

A deployed pipeline is locked. To process another batch, clone it and deploy the copy. Read [How data labeling works](https://docs.platform.toloka.ai/docs/data/concepts/overview.md) for the division of work between you, the agent, and the pipeline.

## Train: configure, estimate, run, download

A **Training** is an editable draft: choose a method, a model source, dataset selections, and settings. A free cost estimate validates the inputs and gives you a maximum charge.

A **Training Run** is one execution of that draft. Starting it places a hold for the maximum charge on your team's balance. A completed Training Run publishes a **Model Version** inside a **Model**. You download the files and serve them in your own environment; the platform does not host inference.

Use the [CLI](https://docs.platform.toloka.ai/docs/cli.md) or [HTTP API](https://docs.platform.toloka.ai/docs/api-reference.md) for project Training. The [first-model tutorial](https://docs.platform.toloka.ai/docs/train/get-started/first-trained-model.md) walks through a CLI run. [How training works](https://docs.platform.toloka.ai/docs/train/concepts/how-training-works.md) explains its lifecycle and states.

> [!NOTE]
>
> In shared documentation, **pipeline run** means Data Labeling and **Training Run** means Train. A Train **Model** is a container for trained versions; a pipeline's **generation model** or **review model** is an LLM selected for labeling or quality control.

## Costs are product-specific

Both products use the team balance, but their charge rules differ. Data Labeling has a cost forecast and a frozen per-run contract. Train estimates a maximum charge and settles GPU usage against a hold. Read [Billing](https://docs.platform.toloka.ai/docs/core/billing.md), then the relevant product's [pipeline pricing](https://docs.platform.toloka.ai/docs/data/concepts/pricing.md) or [Training cost](https://docs.platform.toloka.ai/docs/train/concepts/cost.md) before launching paid work.

## Work from code

The [CLI](https://docs.platform.toloka.ai/docs/cli.md) covers Data Labeling and Train. The [TypeScript SDK](https://docs.platform.toloka.ai/docs/sdk/pipelines.md) authors and tests labeling pipelines. The [HTTP API](https://docs.platform.toloka.ai/docs/api-reference.md) exposes both product workflows.

When you need both products, follow [Label, train, and evaluate](https://docs.platform.toloka.ai/docs/workflows/label-train-evaluate.md). For an unfamiliar term, use the [glossary](https://docs.platform.toloka.ai/docs/start/glossary.md).


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