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.