Stay ahead of every competitor by mastering Dolt and DoltHub — the AI-native, version-controlled database changing how the next generation of developers build, collaborate, and ship.
Imagine combining the power of Git — the tool developers use to version control their code — with a fully functional SQL database. That's exactly what Dolt is: a revolutionary, open-source, version-controlled SQL database that lets you track, branch, merge, and collaborate on your data just like you do with code.
Dolt is MySQL-compatible, meaning you can plug it in as a drop-in replacement for MySQL and use your existing SQL skills and tools immediately — but with the added superpower of Git-style versioning built right in. There's also Doltgres, the Postgres-compatible variant, for teams already on that stack.
DoltHub is the hosted collaboration platform for Dolt — think "GitHub for databases." It supports forks, clones, pull requests, and public or private databases so teams can share, review, and propose changes to data with the same workflows they already use for code. There's also DoltLab for self-hosted deployments and a fully managed cloud option for production workloads.
Dolt was first released in 2019 and has rapidly evolved since then. Its unique approach to data versioning has attracted a growing community of developers, data scientists, AI researchers, and organizations who need better, more transparent ways to manage their data workflows. As of 2026, it's one of the most talked-about tools in the AI infrastructure space.
What makes Dolt genuinely different — not just another database with a marketing angle.
Create branches for experiments, new feature engineering, or labeling tasks. Merge successful branches back into main. Conflicts are resolved — just like code.
Every change to your schema or data is a commit with author, timestamp, and message. You have a complete, immutable history of your database forever.
Compare any two versions of your database — row by row, column by column. Know exactly what changed, who changed it, and why.
Bad data migration? Corrupt import? Roll back to any previous commit in seconds. No restoring backups, no guessing. Just revert.
No new query language. No migration headaches. Dolt speaks MySQL-compatible SQL — your existing drivers, ORMs, and tools work out of the box.
Fork databases on DoltHub, propose changes via pull requests, discuss and review them as a team, then merge. GitHub-style collaboration — for data.
The Dolt CLI is fully open source. Download it, use it locally for free, forever. Paid options exist for hosted production workloads when you scale.
Dolt markets itself as "The Database for AI." Agents need versioned, auditable data to be trustworthy. Dolt is purpose-built for that world.
Clone a database from DoltHub like you'd clone a git repo. Pull to get updates. Push to share. Synchronization across teams and environments becomes trivial.
Dolt isn't a solution looking for a problem — it solves real, painful problems that data teams hit every single day.
ML models are only as good as the data they train on. Dolt lets you version control your training datasets, feature engineering steps, and label changes — so every experiment is reproducible and every result is auditable. When a model performs differently, you know exactly which data version caused it.
Multiple analysts, engineers, and data scientists can safely propose, review, and merge changes to the same datasets simultaneously — without overwriting each other's work or creating conflicting versions in spreadsheets. Pull requests make data changes transparent and reviewable.
In regulated industries — finance, healthcare, government — tracking who changed what data and when is not optional, it's mandatory. Dolt's commit history and diff tools provide a built-in audit trail that simplifies compliance, governance, and legal discovery without any extra tooling.
Just like developers create feature branches for new code, data teams can create branches to test new transformations, enrichments, or hypotheses without risking the main dataset. If the experiment fails, discard the branch. If it succeeds, merge it in. Zero risk to production data.
Research institutions and open-source communities can publish datasets on DoltHub with full version history — enabling anyone to see exactly how a dataset evolved, reproduce any past state, and contribute improvements via pull requests. Science becomes more transparent and collaborative.
As AI agents increasingly read, write, and modify databases autonomously, auditability becomes critical. Dolt lets you see every change an agent made, roll back agent mistakes, branch before running agents on production data, and verify agent behavior — making AI systems safer and more trustworthy.
The trends shaping the next decade of software all point in one direction — and Dolt is sitting right at the intersection.
How does Dolt stack up against what you're already using?
| Feature | Dolt | MySQL / Postgres | Git + CSVs |
|---|---|---|---|
| SQL Interface | ✓ Full MySQL-compatible SQL | ✓ Yes | ✗ No |
| Branch & Merge | ✓ Native, built-in | ✗ Not supported | ~ Clunky workarounds |
| Commit History | ✓ Full immutable log | ✗ Only with audit plugins | ✓ Yes (file-level) |
| Row-Level Diffs | ✓ Built-in diff tables | ✗ No | ✗ No |
| Collaboration (PRs) | ✓ DoltHub pull requests | ✗ No native support | ~ GitHub PRs (file only) |
| Instant Rollback | ✓ One command | ✗ Restore from backup | ✓ git revert |
| Clone from Remote | ✓ dolt clone | ✗ Manual dump/restore | ✓ git clone |
| AI / Agent Ready | ✓ Designed for it | ✗ Requires external tooling | ✗ Not practical |
| Open Source | ✓ Apache 2.0 | ✓ Yes | ✓ Yes (git) |
This isn't just about your career. Learning Dolt puts you in a position to change how the world manages, shares, and trusts data.
Help researchers version control the datasets behind published findings — making scientific results verifiable, reproducible, and harder to fake.
Build AI pipelines where every training run is traceable to a specific data version. Help the world understand why an AI made a decision — and roll it back if needed.
Give compliance teams, auditors, and regulators the audit trails they need without building custom tooling. Make accountability a default — not an afterthought.
Publish datasets with full version history on DoltHub — enabling communities to contribute improvements the same way they contribute to open source software.
Safely branch, experiment, and fail fast on data. Teams that aren't afraid to experiment with data move faster. Dolt eliminates the fear of irreversible data changes.
Give every team member — not just DBAs — the ability to propose and review data changes transparently. Democratize data ownership through familiar PR workflows.
No fluff. Just the steps to go from zero to a running version-controlled database in under 10 minutes.
Download and install the Dolt binary for your OS. Mac: brew install dolt. Linux: use the install script. Windows: grab the MSI installer from GitHub releases. Verify with dolt version.
Create a new directory, enter it, and run dolt init. This creates a .dolt/ folder — just like .git/ — to store all your version history. Congratulations, you have a version-controlled database.
Run dolt sql-server to start a MySQL-compatible server on port 3306. Connect with any MySQL client: mysql -u root -h 127.0.0.1, DBeaver, TablePlus, or your ORM of choice.
Use standard SQL: CREATE TABLE, INSERT INTO, etc. Or import a CSV: dolt table import -c mytable data.csv. All the SQL you already know works exactly the same.
Stage your changes with dolt add . and commit them with dolt commit -m "initial schema and data". You've created your first data commit. Check your history with dolt log.
Run dolt checkout -b my-experiment. Make changes, update data, alter the schema. Your main branch is completely untouched. Run dolt diff main to see what changed.
Happy with the experiment? dolt checkout main && dolt merge my-experiment. Not happy? dolt checkout main and ignore the branch — your main data is pristine.
Create a free account at dolthub.com. Create a new database, add it as remote: dolt remote add origin https://doltremoteapi.dolthub.com/your-org/your-db. Then dolt push origin main. Share the URL with your team.
The best places to continue learning — direct links, no fluff.
The fastest way to learn Dolt is to clone an existing public dataset from DoltHub and practice branching, diffing, and merging on real data. Go to dolthub.com/explore, find a dataset that interests you, clone it locally with dolt clone <org/repo>, and start experimenting. You can't break anything — that's the whole point.
You now know what Dolt is, why it matters, and how to get started. The developers who master version-controlled data infrastructure today will architect the systems everyone else depends on tomorrow.