Summary
Galaxy is the Cursor for SQL, purpose built for software engineers, data practitioners, and more. The Galaxy experience starts with a next-generation SQL editor: lightning-fast, memory-light, and packed with practical features - AI copilot, collaborative sharing & permissions, and rock-solid security. To start, we’re focused on perfecting the daily workflow of writing, running, and collaborating on SQL.
We built Galaxy after drowning in the same set of headaches:
- Queries everywhere – SQL lives in Slack, BI folders, dusty Git repos, and copy-pasted Notion pages.
- Never-ending data requests – because no one can find (or trust) the right query, engineers and analysts still field endless one-offs.
- LLM Roulette - Generic ChatGPT answers miss schema nuance, and letting non-technical users paste SQL from a chatbot is a compliance nightmare.
The collaborative editor is just the beginning. From it we’re building a unified data platform that will:
- Version & Govern every query, dataset, and dashboard in one searchable hub.
- Serve Data Anywhere by turning approved queries into live APIs, webhooks, and notebooks.
- Empower Everyone with trusted building blocks—so business users can self-serve without derailing engineers.
Think of it as “data-platform-in-a-box”: start with the editor you already love, grow into the full stack when you’re ready.
Features
Galaxy’s base product - the SQL editor, has been reimagined from the ground up to be modern, lightning fast, and not eat your computer’s memory / battery. It is a beautiful desktop app (with a cloud offering for those who like web). We’ve reworked parameterization, auto-complete, and even table metadata to make data discovery and analysis much more reliable and convenient for developers.
Galaxy’s AI copilot is context-aware - meaning you can write complex SQL queries accurately, optimize queries, change queries when the underlying data model changes, and even chat directly with your database to learn about it.
Galaxy Collections - allow users within the same Workspace to create an organized view through which they can share and collaborate on queries of the same type, project and more. Collections also enable teams to “Endorse” queries so that a team know when a query has been endorsed as correct by the relevant stakeholders. This removes the needs for teams to share SQL queries in Slack, Notion, GSheets, etc.
Galaxy also has modern access controls and security - meaning things like permissioning and user types, run / edit history, read / write access and more are all accessible and within reach.
Roadmap
Down the line, Galaxy will enable teams to run lightweight visualization out of the box, run recurring workflows, and even serve as a data cataloguing tool.
Value Prop
Galaxy helps engineering teams write SQL faster, stay aligned on data, and cut down on busywork with a blazing-fast editor, context-aware AI copilot, and built-in collaboration tools. Instead of pasting queries into Slack or Notion, teams can endorse and reuse trusted SQL in one place—speeding up development and reducing errors.
ICP
Galaxy is best fit for technical founders, software developers, data engineers, and data scientists that like using IDEs or sql editors to write SQL (not necessarily jupyter notebooks). These individuals like trying new tools with AI in them, and want to write SQL faster and more efficiently, be on the same page about data, and more. The majority of developers use a SQL editor, but the best fit B2B customers are at late Seed to Series B software companies whos platform involves showing KPIs, metrics or dashboards in-app. This is because they want to optimize their SQL writing / efficiency by leveraging a context aware AI copilot, and are excited by sharing SQL seamlessly, to be on the same page (without pasting raw SQL into Slack and Notion) and more.
Galaxy is free to use in single player mode with limited AI, but there are paid plans for premium AI usage and multiplayer / sharing.
Competition
- Old-Age SQL editors like Datagrip, Tableplus, Postico, pgAdmin, DBeaver, and more. These are predominantly developer oriented since it is an IDE and comes in a desktop app.
- Data Science Tools that have notebook or Jupyter notebook-like interfaces like Hex, Mode, Briefer. These are more notebook oriented and tend to be delivered as a cloud offering
- Business Intelligence Tools (which include AI SQL tools) like Outerbase, Tableau, Looker, Basedash, Index (index.app), Vanna, Julius, Chat2DB, Seek AI, and many more
We differentiate by creating a modern experience built intentionally for developers (not for non technical folks at first) - meaning a desktop app, IDE interface (not a notebook and not a chat UI) AI as a copilot (not a replacement), and more.
Head to Heads
Versus Old-Age SQL Editors
Galaxy outpaces SQL editors like X because it is fast, reliable, doesn’t crash, and has modern SQL capabilities - such as AI and sharing. What’s more it’s a beautiful and feels like new tools that devs use and expect.
Versus Data Science Tools
Galaxy outpaces data science tools like X because it is an actual IDE and not delivered in a notebook format. Additionally, many data science tools are delivered via cloud UIs and require python, but developers prefer to use desktop IDEs and write in SQL.
Versus proper BI tools
Galaxy outpaces BI tools like X because developers don’t use BI tools - they use SQL editors and things that have a dev-tool feel. As a result, devs typically send queries to business teams in Slack or Notion rather than use the tools themselves. Eventually, Galaxy will have visualization out of the box, so we plan to serve this use case soon.
Versus AI Agent Tools -
Galaxy outpaces AI tools like X because these agentic tools don’t have the preferred UI for developers, notably the IDE interface. They tend to be built for non-technical business analysts, not the developers and technical talent that intimately know their data models. Consequently these platforms do not support deep SQL and data work, with the required accuracy and precision necessary. Galaxy will offer agentic workflows and text to visualization eventually, so we are excited to serve this use case soon.
Traction
Currently, we have a waitlist of ~1500 software developers at companies around the world. We have spoken to over 150 of these individuals and have spent north of 75 hours doing so. We will be launching over the coming weeks in sets of hand selected groups and expect to release a publicly available beta within the next 90 days.
Team
Mitch and Leon originally met in college at Rutgers as roommates. From there Leon and Mitch began their software and data careers separately, then joining forces once again at Flock Safety - where they built the data team together from a small company to Pre-IPO. They together and individually have worked with data extensively over the course of their careers. Today, Leon manages an entire data team.
Meanwhile, Mitch jumped back into early stage building at Unify where he joined as employee #14 and met Garrett Wolfe, who was Unify’s first Growth and Business hire (employee #9). Garrett has finance and venture experience (graduating from Duke) and helped build out many of the teams at Unify that now have mature teams. He was a founding member of Unify’s growth team and the work Garrett did at Unify powered north of $15M in pipeline in less than 12 months.
Garrett’s work at Unify positioned him not only as a growth leader, but also as a functional head of product - pushing Unify to its absolute limits. There, he needed to be deeply data oriented and interface with Mitch and team directly in order to prioritize the company’s roadmap. The problems of data sprawl, data literacy, and communication about data continued to pop up again and again.