Data Storage | Label Studio

Label Studio can connect to local and cloud storage through a standard storage connection architecture. You can combine multiple buckets, containers, databases, or directories as a project's source or target data store. By using secure connections to cloud storage, data annotators can guarantee that sensitive data is never transmitted to Label Studio servers and is only shared over encrypted connections with data annotators.

Storage Integrations

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Other Integrations

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Azure Blob Storage
Azure cloud storage for data labeling
Azure Blob with Managed Identity
Secure, flexible authentication for Azure.
Databricks
Unify your data and AI
GCS with Workload Identity Federation
Keyless, short-lived access to storage
Google Cloud Storage
Google cloud storage for data labeling
Redis
High-speed cache for annotation data
S3
AWS cloud storage for data labeling
S3 with IAM roles
A secure way to move data in and out of projects
Anthropic
Building safe, reliable, and steerable artificial intelligence
Azure OpenAI
Cohere
Foundational models and AI solutions
Gemini
A family of advanced AI models that power a suite of AI features across Google products.
Hugging Face LLM
Text generation with Hugging Face language models
IBM watsonx.ai
Enterprise AI studio for foundation model inference
Meta Llama
Open-weight foundation models for text and vision
Mistral AI
High-performance open-weight language models.
OpenAI
Provides powerful language and vision models through hosted APIs.
Vertex AI
Enterprise ready, fully-managed, unified AI development platform
Grounding DINO
Text-Driven Object Detection Model
OpenMMLab
Bounding box image labeling
Segment Anything Model
Image Segmentation Model
Segment Anything Model 2
Interactive image segmentation with Meta's latest model
Ultralytics YOLO
Computer vision models
Flair
Named entity recognition
GLiNER
Generalist NER with zero-shot entity extraction
Hugging Face NER Pipeline
Named entity recognition with transformer models
spaCy
Open-source software library for advanced natural language processing
Docling
Vision-language model for end-to-end document conversion
EasyOCR
Optical Character Recognition (OCR) engine
Tesseract
Automated bounding box OCR
Unstructured.io
Unstructured data ingestion and preprocessing
Deepgram
Voice AI
Nvidia NeMo
Automated audio transcription
Braintrust
Debug and evaluate AI agents with observability traces
Chainlit
Evaluate multi-turn AI conversations with automatic sync
LangChain
Evaluate LLM Output Quality
LangSmith
Debug and evaluate AI agents with observability traces
PyTorch
Open source machine learning framework
Scikit Learn
Machine learning toolkit
TensorFlow
Open source deep learning framework
ZenML
Extensible MLOps framework
Amazon SageMaker
Integrate Label Studio with AWS SageMaker
Docker
Container installation of Label Studio
Kubernetes
Infrastructure to deploy and scale Label Studio
Terraform
Automated installation
PostgreSQL
Advanced relational storage for data annotations
SQLite
Default embedded database in Label Studio