Dataloop

The AI-ready Data Stack for unstructured data, multimodal pipelines, and the full AI data lifecycle

Enterprise·All audiences·API available

Key strengths

End-to-end AI data lifecycle management for unstructured dataVisual drag-and-drop pipeline builder with Python SDK supportBuilt-in human-in-the-loop / RLHF feedback integrationMarketplace of 100s of pre-built models, pipelines, and nodesEnterprise-grade security: SOC 2 Type II, ISO 27001, GDPR compliant

Enterprise pricing

Tel Aviv

Founded 2017

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Dataloop Developer Documentation

SDK & API Access

Dataloop provides a Python SDK and a REST API for programmatic control over the full platform:

Install the SDK via pip: pip install dtlpy
Authenticate and connect: import dtlpy as dl; dl.login()
Access datasets, pipelines, models, and annotations entirely in code

Key Platform Components

Data Management API: Upload, version, filter, and route unstructured data items (images, video, audio, text, LiDAR point clouds) across projects and datasets.
Pipeline SDK: Build, deploy, and manage DAG-based pipelines using Python or the visual UI. Nodes include data sources, model inference, human review tasks, and custom functions.
Model Management: Deploy off-the-shelf or custom models; run fine-tuning experiments; version and compare model outputs programmatically.
FaaS (Function-as-a-Service): Write serverless Python functions that operate on data and models without managing infrastructure.

Key Parameters & Concepts

Concept	Description
`Dataset`	Versioned collection of unstructured data items
`Pipeline`	DAG of connected nodes (data, model, app, human)
`Item`	A single data artifact (image, video, audio, etc.)
`Annotation`	Labels or feedback attached to an item
`Model Adapter`	Wrapper interface for integrating custom or third-party models

NVIDIA NIM Integration

The platform is embedded with NVIDIA's NIM architecture, enabling 128x faster NIM adoption for GenAI and Agentic workflows with reduced infrastructure overhead.

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