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First, credit where it’s due: Hex is a genuinely lovely product — collaborative notebooks, polished AI features, great for teams that want analytics in the cloud. If that’s what you need, use Hex.
DataCharter sits in a different spot, and for a lot of individual data work it’s the spot that matters: everything runs on your laptop, nothing leaves it, and it’s free, open source (Apache-2.0), and yours.
When DataCharter is the better fit
- Your data can’t go to a SaaS. Client data, regulated data, or just data you’d rather keep local. DataCharter’s engine, UI, and agent all run as one local process — there is no cloud side.
- You want SQL-first exploration, not notebooks. A Monaco SQL editor with live-as-you-type results, one-click charts, and a profiling panel — no cells, no kernel, no publish step.
- You query files and databases together. DuckDB federation joins a local CSV to a Snowflake table to Parquet in S3 in one statement — see Sources.
- You want AI help without handing over the keys. Connect Claude Code, a local Ollama model, or any OpenAI-compatible agent — with access declared per source, table, and column in a data contract, PII masked by default, an audit trail of every access, and clean-room-style policies like “aggregates only” and “groups of at least 10”.
- Zero procurement.
pip install datacharter(oruvx datacharter) and you’re exploring in under a minute — no accounts, no seats, no trial clock.
When Hex is the better fit
Honesty corner: if you need real-time multiplayer collaboration, published interactive apps for stakeholders, scheduled runs in the cloud, or Python/R notebooks as the primary medium — that’s Hex’s home turf, and DataCharter doesn’t try to be that.
Try the local-first version of the idea
uvx datacharter serve
# -> a governed local workspace with demo data on http://127.0.0.1:8321
Then point it at your own data. It’s open source — if you take it for a spin and something feels missing, tell us; the roadmap is built from exactly that.