Context Layer · For unstructured data

Your researchers and AI agents
are flying blind in S3

For agentsClaude Code·Cursor·Codex

DataChain gives them sight - versioned datasets, Pydantic schemas, lineage, and summaries over S3, GCS, and Azure.

How they finally see

Bytes stay in storage. DataChain captures context from every pipeline.
AI Agents
Claude Code·Cursor·Codex
Humans
Pipelines·Notebooks·UI
DataChain
Knowledge Base
LLM summaries · stats · lineage · code
Dataset DB
Pydantic schemas · versioning · file refs
Compute Engine
distributed · async I/O · checkpoints
Object Storage
S3s3://
GCSgs://
AZaz://
Sensor Data · Videos · Images · Logs · Docs

Empowering companies

Aicon logo
Billie logo
Cyclica logo
Degould logo
Huggingface logo
Inlab Digital logo
UBS logo
Mantis logo
Papercup logo
Pieces logo
Sicara logo
UKHO logo
XP Inc logo
Kibsi logo
Summer Sports logo
Motorway logo
Aicon logo
Billie logo
Cyclica logo
Degould logo
Huggingface logo
Inlab Digital logo
UBS logo
Mantis logo
Papercup logo
Pieces logo
Sicara logo
UKHO logo
XP Inc logo
Kibsi logo
Summer Sports logo
Motorway logo
Aicon logo
Billie logo
Cyclica logo
Degould logo
Huggingface logo
Inlab Digital logo
UBS logo
Mantis logo
Papercup logo
Pieces logo
Sicara logo
UKHO logo
XP Inc logo
Kibsi logo
Summer Sports logo
Motorway logo
Aicon logo
Billie logo
Cyclica logo
Degould logo
Huggingface logo
Inlab Digital logo
UBS logo
Mantis logo
Papercup logo
Pieces logo
Sicara logo
UKHO logo
XP Inc logo
Kibsi logo
Summer Sports logo
Motorway logo
Aicon logo
Billie logo
Cyclica logo
Degould logo
Huggingface logo
Inlab Digital logo
UBS logo
Mantis logo
Papercup logo
Pieces logo
Sicara logo
UKHO logo
XP Inc logo
Kibsi logo
Summer Sports logo
Motorway logo

What our customers say

We realized we were solving a problem we shouldn't be solving. With DataChain, what used to require data engineers is now handled seamlessly by researchers - and the whole team moved to the next level.

Yoni Svechinsky

Director of Research | brain.space

What surprised me was how easily researchers adopted DataChain - data tools are usually hard for non-engineers. What surprised me more was when hardware and QA started asking for access too.

Sharon Kohen

Principal Data Engineering | brain.space

DataChain added real value to our workflows - versioned datasets, automated ETL, and MLOps, all in Python. If you need a data management layer on top of cloud storage, give it a try.

Nikhilesh Saggere

Lead Engineer | Alps Alpine Europe

Distributed Python over your files

Read, transform, and save data at scale. In your own cloud (BYOC).

pipeline.py
import datachain as dc

(
 dc.read_storage("s3://acme-robots/runs/**/*.mp4")
 .filter(dc.C("file.size") > 1000)
 .settings(parallel=8, prefetch=5, workers=700)
 .map(obstacles=detect_obstacles)
 .save("obstacle_detections")
)
Scale
laptop 700 workers
Parallelism
Python functions, async I/O for S3/GS/AZ
Resilience
automatic checkpoints, incremental update
Files in storage
No file copies, pointers to files

What they can finally do.

Impossible with raw bytes. Automatic with Data Context Layer.

Researchers find work, not files.

Search by schema, statistics, or LLM summary. Last quarter's labeled dataset is one prompt away — not days of Slack archeology to find who built it.

Agents reuse, not regenerate.

Claude Code, Cursor, and Codex read schemas, previews, and lineage before generating code, turning hours of recompute into a single read.

Recall replaces recompute.

Read a summary: $0.0001. Run a query: $0.20. Both instant. Recompute from raw files: $100 and three hours of wall-clock — a day gone.

Every result is reproducible.

Each .save() records source code, inputs, author, and time. Re-running a six-month-old experiment is one line of Python — not weeks of forensics.

Open source to start. Studio to scale.

Same SDK. Same datasets. New delivery model.
Open Source
Teams
Enterprise
Delivery
Skill
MCP
MCP
Storage
Your S3, GCS, or Azure — never copied, never moved
Dataset DB
In local files
Centralized
Centralized (BYOC)
Compute Engine
Local machine
Local machine
CPU/GPU clusters (BYOC)
Access control
Single developer
Up to 5 users
Teams + access control
Scale
Millions of records
Billions of records
Billions of records + distributed compute
Price
Free
$70 / team (coming soon)

Trusted partners with global industry leaders

NVIDIA logo
GitHub logo
Databricks logo
Nebius logo
Hashicorp logo

Your data never leaves your cloud.

Your Cloud

  • Data stays in your S3/GCS/Azure bucket
  • Compute runs in your VPC (BYOC)
  • No data copying or egress
  • You control access and encryption

DataChain

  • Metadata and lineage
  • Control plane, not data plane
  • Role-based access and audit logs
  • SSO & SAML integration

Compliance

  • SOC 2 Type II certified
  • GDPR-ready data processing
  • On-prem deployment available
  • Enterprise security reviews