What your team can finally do.
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
AI compute spend
10,000× cheaper
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.
Time to result
weeks → minutes
for questions a teammate already answered. Researchers find the dataset by schema, stats, or LLM summary instead of asking around.
Reproducibility risk
zero
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
In the tradition of Codd (1970), Kimball (1996), Iceberg (2017) — applied to data they never saw.
Empowering startups to Fortune 500 companies
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
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
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
How DataChain captures context
Distributed Python over your files
Read, transform, and save data at scale. In your own cloud (BYOC).
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")
)Open source to start. Studio to scale.
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