Who it is built for
- Researchers working with files in S3, Google Cloud Storage, or Azure
- AI teams seeking to reuse expensive LLM and embedding results
- Data engineers building Python pipelines over unstructured files
Data context layer for unstructured cloud storage
DataChain is a Python-based data context layer for unstructured files in S3, Google Cloud Storage, and Azure. It captures metadata, schemas, LLM outputs, lineage, and versioned datasets so researchers and AI agents can discover, reuse, and reproduce work without repeatedly processing raw files.
The decision
Start with the job, the team, and the constraints. Product fit becomes much clearer when those three line up.
Inside the product
The core product capabilities, grouped around the work they enable.
DataChain captures metadata, LLM summaries, statistics, code, and lineage for files that remain in object storage.
The SDK reads, transforms, and saves data at scale with Python functions, asynchronous I/O, and automatic checkpoints.
Each saved result records source code, inputs, author, and time to support reproducible data work.
Install the DataChain SDK and point a Python pipeline at files in S3, Google Cloud Storage, or Azure. Read and transform files, optionally run LLM or classifier passes, then save versioned results with their metadata and lineage for later reuse.
Plans and official links
See the entry price, free access options, company details, and direct vendor destinations in one place.
Choosing for a real workflow?
We map the workflow, connect existing systems, choose what to buy, and build what is missing.