Context Layer for Unstructured Data

Your researchers and AI agents are flying blind in S3

Every LLM, embedding, or classifier pass over your files runs once. The next read costs cents. Your data stays in your cloud; compute runs in your VPC. SOC 2 Type II.

Trusted by startups and Fortune 500 companies

What your team 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 — instead of three engineer-days hunting through Slack for who built it.
Agents reuse, not regenerate.
Claude Code, Cursor, and Codex read schemas, previews, and lineage before they write code. Six hours of recompute become a six-cent read.
Recall replaces recompute.
Read a summary: $0.0001. Run a query: $0.20. Recompute from raw files: $100 and three hours of wall-clock. Same answer, four orders of magnitude apart.
Every result is reproducible.
Each .save() records source code, inputs, author, and time. Audit-ready by construction. A six-month-old experiment re-runs in one line of Python.

Three numbers that move when you adopt DataChain

No headcount changes. No new platform. The math comes from making compute reusable.

10,000× cheaper
AI compute spend. Recall vs recompute. Sense work (LLM annotations, embeddings, classifier passes) is the dominant line item in most AI budgets. CAST persists it once; every later question reads it.
weeks → minutes
Time to result. Find answers a teammate already produced. Researchers locate datasets by schema, stats, or LLM summary instead of asking around.
zero
Reproducibility risk. Avoid lost experiments. Every dataset carries source code, inputs, author, timestamp, and lineage. Six months later, the result re-runs with one line of Python.

Why DataChain works

CAST · Container / Asset / Sense / Task

Four layers of unstructured data that researchers
and AI agents read instead of rebuild.

Storage
Sensor data · Videos · Images · Logs · Docs
S3 · GCS · Azure
Your cloud
Container
h5/jpg/mp4 file headers · JSON sidecars · joint metadata
cheaper to recall
20×
Asset
audio tracks · frames · clips · np.array · dataset mixtures
cheaper to recall
400×
Sense
ML scoring · LLM responses · embeddings
cheaper to recall
10,000×
Task
insights · curated datasets · data analytics
cached forever
Instant

In the tradition of Codd (1970), Kimball (1996), Iceberg (2017) — applied to data they never saw

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

How DataChain captures context

Bytes stay in storage. DataChain captures context from every pipeline.

AI Agents

Claude Code · Cursor · Codex

Skill · MCP

Humans

Pipelines · Notebooks · UI

Python SDK

DataChain

Knowledge Base

LLM summaries · stats · lineage · code

Dataset DB

Pydantic schemas · versioning · file refs

Compute Engine

distributed · async I/O · checkpoints

Raw files

Object Storage

Amazon S3

s3://

Google Cloud Storage

gs://

Azure Blob Storage

az://

Sensor Data · Videos · Images · Logs · Docs

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 Run the same Python pipeline on your laptop or across 700 workers.
Parallelism Combine parallel Python functions with async I/O across S3, GCS, and Azure.
Resilience Resume from automatic checkpoints and process incremental updates without starting over.
Files in storage Keep original files in your cloud and work with pointers, without copying data.

Open source to start — Studio to scale

Same SDK. Same datasets. Choose the setup that fits your team.

Book a Demo

Open Source

For individual developers building reusable data workflows on their own machine.

Free

Get started

Storage: Your S3, GCS, or Azure
Delivery: Skill
Dataset DB: Dataset DB in local files
Compute: Local compute
Access: Single developer
Scale: Millions of records

Teams

For small teams sharing centralized datasets while keeping compute on local machines.

$70

Storage: Your S3, GCS, or Azure
Delivery: MCP
Dataset DB: Centralized Dataset DB
Compute: Local compute
Access: Up to 5 users
Scale: Billions of records

Enterprise

For organizations running distributed workloads with team access controls in their own cloud.

Custom

Storage: Your S3, GCS, or Azure
Delivery: MCP
Dataset DB: Centralized Dataset DB (BYOC)
Compute: CPU/GPU clusters (BYOC)
Access: Teams + access control
Scale: Billions of records + distributed compute

Trusted partners with global industry leaders

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

Researchers find work, not files.

Agents reuse, not regenerate.

Recall replaces recompute.

Every result is reproducible.

10,000× cheaper

weeks → minutes

zero

Storage

Container

Asset

Sense

Task

What our customers say

AI Agents

Humans

DataChain

Knowledge Base

Dataset DB

Compute Engine

Object Storage

Amazon S3

Google Cloud Storage

Azure Blob Storage

Open Source

Teams

Enterprise

Trusted partners with global industry leaders

Your Cloud

DataChain

Compliance

Add the missing data context layer to your object storage