Use Case

Work With Raw Multimodal Data at Any Scale

Video, 3D scans, audio, documents. Filter, transform, and reason over millions of files in one Python session. No ETL, no preprocessing pipeline, no waiting.

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Trusted by startups and Fortune 500 companies

Skip the prep. Start the research

Copying files, changing formats, and building pipelines should not come before research. DataChain turns files in S3, GCS, and Azure into typed datasets with one Python call. Its context layer brings persistent data and accumulated knowledge into Claude Code, Cursor, and Codex. Two 2026 studies evaluate it under Claude Code on 1,500 SEC 10-K documents and 1,500 MS-COCO images

From Raw Files to Reusable Research

Direct access

Raw files to typed datasets in one call

Dataset DB keeps file pointers in Pydantic-typed rows and bytes in your buckets. Query millions of records at warehouse speed—without ETL, reorganization, or waiting.

Start with dc.read_storage("s3://bucket/..."). Filter, sort, sample, group, join, and search by similarity.
Run LLM calls, model inference, and multimodal extraction per row with parallel compute, async I/O, and checkpoint recovery.
Read only what you need: a video clip, an MRI slice, or one Parquet column—not the whole file.

Reuse economics

Lower the cost of every answer

An LLM pass over 500K documents takes hours and thousands of dollars. Reuse saved results in under a second for fractions of a cent, with roughly 100× lower costs at each tier.

Raw files (~$100 per recall): Compute Engine answers questions that have never been asked before.
Typed datasets (~$1): Dataset DB handles filters, joins, similarity search, and column reads.
Summaries (~$0.01): Knowledge Base serves Markdown on schemas, lineage, and prior conclusions.
Reported: 100,000,000× cheaper recall. End-to-end enrichment of 3,000 multimodal files with LLMs, VLMs, and embeddings cost $249; queries on saved results cost under one cent.

Compounding speed

Every session starts further ahead

Every chain saves a typed dataset for the next session. Research speeds up through accumulated results instead of repeated work on raw files.

8.4× faster, 3.4× cheaper follow-ups: seconds and cents instead of minutes and dollars.
5× compounding savings across five sessions as each saved dataset feeds the next.
Up to +40 percentage points in pass rate on reuse-rich tasks when agents read prior typed datasets instead of raw files.

Skip the prep. Start the research

From Raw Files to Reusable Research

Raw files to typed datasets in one call

Lower the cost of every answer

Every session starts further ahead

Add the missing data context layer to your object storage