Petabytes stored. Nothing understood.
Your files have no schema, no lineage, no catalog. Here's what that costs you.
Same work, repeated.
Your team already extracted those features. Nobody can find them, so the next person starts from scratch.
Tribal knowledge.
The person who knows which bucket has the latest labels is on vacation. Work stops.
Agents with no context.
No catalog to search, no schema to inspect, no lineage to trace. Just hallucinations.
Empowering startups to Fortune 500 companies
Six lines. That's the whole pipeline.
No SQL. No ETL. No data movement. Just Python.
import datachain as dc
(
dc.read_storage("s3://hospital/scans/", type="image")
.filter(dc.C("file.size") > 1000)
.settings(parallel=8, prefetch=5, workers=150)
.map(diagnosis=classify_scan)
.save("ct_anomaly_candidates")
)Point at storage
Connect to any S3, GCS, or Azure bucket. No data copying, no ingestion step.
Transform with Python
Filter, map, and enrich using plain Python - LLMs, CV models, or any function.
Save as a dataset
Versioned, with full lineage, queryable from anywhere - UI, API, or MCP.
The same code that runs on your laptop runs on a 150-node cluster.
DataChain handles the parallelism, async download, checkpointing, and lineage.
What changes when your data has context.
Impossible without a data platform. Automatic with one.
Find any file. Without asking anyone.
No more Slack archeology. Anyone on the team can search, filter, and trace data to its source.
AI that knows your data.
Claude Code and other tools stop hallucinating pipelines - they reuse real datasets instead of creating duplicates.
Reproduce anything. Instantly.
Every file and transformation is versioned. Debugging goes from days to minutes.
One workspace. Everyone in sync.
Shared view of datasets, runs, and lineage. Standups become decisions, not status updates.
Open source to start. Studio to scale.
Same SDK. Same concepts. The difference is scale and collaboration.
Open Source
For individuals and small teams getting started with data pipelines.
- Python SDK for S3/GCS/Azure
- Pydantic-native schemas
- Dataset versioning & lineage
- Local parallel execution
- LLM & ML model integration
- Apache 2.0 license
Studio
For teams that need a shared registry, cloud compute, and collaboration.
- Everything in Open Source
- Web UI & dataset registry
- Team collaboration & access control
- Distributed cloud compute (BYOC)
- MCP server for IDE & agent access
- Enterprise support & SLAs
Both paths use the same DataChain SDK. Start locally, move to Studio when your team or data outgrows a single machine - no rewrite required.
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
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
- Only metadata and lineage are indexed
- Control plane - never 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