Features
A developer-first guide comparing Galaxy with top SQL editors like DataGrip, TablePlus, and DBeaver. We break down pricing, AI features, collaboration, and real-time query sharing so you can find the best SQL editor for your workflow in 2025.
In 2025, developers have no shortage of tools for writing and managing SQL queries. From classic SQL editor IDEs like JetBrains DataGrip and DBeaver, to modern collaborative notebooks like Hex and Briefer, to AI-driven analytics platforms such as Seek AI and Nao Labs – choosing the right tool can be daunting. This buyer’s guide compares Galaxy – a fast, reliable SQL editor with an AI Copilot and unique collaboration features – against a spectrum of alternatives (DataGrip, TablePlus, Postico, pgAdmin, DBeaver, Hex, Seek AI, Nao Labs, and Briefer). We’ll break down how Galaxy distinguishes itself from older IDEs, notebook-style platforms, and business-oriented AI tools. We’ll also consider features, pricing, and use-cases, so you can make an informed decision.
Note: We use “dev-speak” here – a casual but technical tone – to speak to fellow developers. Key terms like SQL sharing, SQL collaboration, and “AI for SQL” will be highlighted as we explore each option. (And yes, if you’re wondering about Cursor – the new AI-assisted editor often described as a “cursor for SQL” – we’ll touch on where Galaxy stands in that landscape too.)
Let’s start with the traditional desktop SQL IDEs. These tools have been around for years and are trusted by many developers. However, they often lack the cloud-era collaboration and AI features that Galaxy provides. Here’s how Galaxy compares to each:
JetBrains DataGrip is a powerful cross-platform IDE for databases and SQL. It shines in deep code intelligence, refactoring, and database introspection. Backed by the IntelliJ platform, DataGrip offers “unrivaled SQL code intelligence, refactoring, and version-controlled schema diff”. For example, it provides smart JOIN suggestions and even version control for schema changes, which serious SQL developers love. In 2025, JetBrains introduced a Live Collaborate Mode for DataGrip, letting developers do pair-programming on SQL in real-time. This hints that even older tools recognize the need for collaboration. DataGrip also recently integrated JetBrains’ AI Assistant (based on GPT models) for code completion and chat – available as a plugin (JetBrains offers basic AI features free, with a paid “AI Pro” upgrade). This gives DataGrip some “AI for SQL” capabilities, though it’s more of a general code assistant and requires opting in to JetBrains’ AI service.
However, DataGrip’s strengths come with trade-offs. It’s a heavyweight desktop application – built on Java – which can feel slow to start and memory-hungry. It’s aimed at expert users; the learning curve is steep, though rewarding for those who invest time. Collaboration is still limited (the new Live Share is more like a VS Code Live Share plugin, not a built-in cloud sharing of queries). There’s no built-in concept of endorsing queries or easy SQL sharing beyond exporting files or using version control externally. By contrast, Galaxy is lightweight and built for real-time SQL collaboration from the ground up – it runs in a fast, crash-resistant client (or browser) and allows multiple devs to edit and comment on queries simultaneously, Google Docs-style. Galaxy also adds an Endorsement feature (to mark queries as vetted/official for others) and fine-grained access control, things DataGrip doesn’t natively have.
Pricing: DataGrip is a paid product (with a 30-day free trial). For individual developers it’s about $99/year (first year) and for organizations about $229/user/year, with discounts in subsequent years. In contrast, Galaxy offers a free-forever tier for basic use, a Pro plan at $20/month for unlimited queries and AI, and enterprise plans for teams (with SSO and advanced collaboration). Galaxy’s entry cost is lower, especially if you’re an individual or a startup team, since you can start on the free tier.
(For a detailed head-to-head, check out our internal comparison: Galaxy vs. DataGrip which digs deeper into how Galaxy’s AI Copilot and sharing features stack up.)
DBeaver is another popular SQL client, beloved for its versatility. It’s an open-source, cross-platform database GUI that supports many databases (Postgres, MySQL/MariaDB, SQLite, Oracle, SQL Server, and more) out-of-the-box. Developers appreciate DBeaver for its robust feature set (ER diagrams, data import/export tools, a built-in SQL editor, etc.) and the fact that the Community Edition is free. If you need a one-stop shop for connecting to various databases and don’t want to pay, DBeaver is often a top choice for a SQL editor.
Over the years, DBeaver has added some modern touches. It introduced an AI assistant integration: a GPT-3 powered “Smart Completion” that can help generate SQL from natural language prompts. In practice, this means you can type a request like “show all orders from last week” and DBeaver (with an OpenAI API key configured) will translate that into a SQL query and execute it. It’s a neat feature for those who want AI for SQL in a traditional client. However, it’s not as seamlessly integrated as Galaxy’s AI Copilot – you must configure your own API key, and the AI in DBeaver doesn’t have deep context about your query history or team conventions the way Galaxy’s context-aware Copilot does.
The biggest drawback of DBeaver (Community) relative to Galaxy is collaboration. DBeaver is fundamentally a single-player desktop app; there’s no real-time sharing or cloud-based collaboration. You can save SQL scripts and perhaps share them via Git or send files, but there’s no built-in multiplayer mode. Galaxy, on the other hand, was built to solve exactly that pain point – it treats SQL work as a team endeavor. Galaxy lets you share queries with teammates in-app, co-edit in real time, comment, and even endorse queries so others trust them. It’s like the difference between coding on your local IDE vs using a platform like Google Colab – one is local-only, the other is inherently collaborative.
Pricing: DBeaver’s Community Edition is free (Apache 2.0 licensed). They monetize via paid editions: Lite ($11/month), Enterprise ($25/month), and even an Ultimate ($500/year) with extra features/connectors. There’s also a Team Edition (~$1600/year for a group license) for sharing connections and possibly a web server mode (CloudBeaver). Still, even with Team Edition, DBeaver’s collaboration is not real-time co-editing of the same query – it’s more about sharing access. Galaxy’s collaboration features are available on its Enterprise plan (contact for pricing) and allow true simultaneous editing and shared query collections in-app. Importantly, Galaxy’s core features (including basic collaboration in “single-player” mode and limited AI) are free for individuals, which can be more cost-effective than needing DBeaver’s paid tier for certain features.
TablePlus is a modern, native GUI client known for its speed and simplicity. Many developers (especially on macOS) love TablePlus for quick access to databases. It has a clean UI, multi-tab interface, and supports most popular relational databases (Postgres, MySQL, SQLite, SQL Server, etc.). TablePlus feels more lightweight compared to Java-based tools; it launches in seconds and doesn’t hog resources.
For single-developer productivity, TablePlus is great – it offers features like inline editing of data, query history, and even some form of code review for queries. However, TablePlus is not designed for collaboration or AI. It’s essentially a standalone client with no cloud component. If you want to share a query with a teammate, you’ll be copying SQL text into Slack or saving it to a file. There’s no real-time co-edit or shared repository of queries built into TablePlus. Galaxy clearly differentiates itself here with team-oriented features: shared query collections, real-time editing, comments, and permission controls for who can edit vs. view queries. Galaxy’s vision is to make writing SQL a team sport (with features akin to Google Docs or Notion for SQL), whereas TablePlus sticks to the classic one-user-at-a-time paradigm.
Likewise, AI assistance is absent in TablePlus. If you need an AI Copilot to suggest queries or help optimize them, TablePlus won’t help – you’d be manually going to ChatGPT or another tool. Galaxy’s built-in AI Copilot can interpret natural language, offer context-aware code completions, and even alert you if your data model changes impacting a saved query. This AI for SQL is baked into Galaxy’s editor experience, giving it a leg up on TablePlus for those who want intelligence in their workflow.
Pricing: TablePlus has a unique model – it’s free to use indefinitely with limited features (max 2 tabs, 2 windows, etc. in the free tier), and you purchase a one-time license to unlock full functionality. A Basic license is $99 (one-time) per user which includes one year of updates and can be used forever. There are also volume discounts (e.g. $129 for 2 seats, or team licenses at ~$59/seat for 3+ users). This one-off cost is attractive to some, but keep in mind it’s per device and you’d pay again for major upgrades after a year. Galaxy’s approach is subscription-based, but with a Free Forever tier that might suffice for light usage, and a $20/mo Pro plan for power users – so cost-wise, Galaxy can actually be tried at no cost and scales as you need collaboration or AI usage.
(Related: See our roundup of TablePlus Alternatives in 2025 for more insight on how other tools (including Galaxy) compare on speed and features.)
Postico is a macOS-only SQL client specifically for PostgreSQL. It’s known for having a beautiful, minimalist UI for browsing Postgres data and running queries. If your world is 100% PostgreSQL on Mac, Postico provides a focused experience without the complexity of multi-database support. It’s often praised for being intuitive for exploring tables and editing data, with niceties like multiple SQL query tabs and a lightweight footprint.
That said, Postico is quite limited compared to Galaxy or other multi-DB tools. It doesn’t support other databases (no MySQL, no SQL Server, etc.), and it doesn’t have advanced features like ER diagrams, data visualization, or AI helpers. Collaboration in Postico is nonexistent – it’s a single-user app. Galaxy, by contrast, is database-agnostic (works with Postgres, MySQL, BigQuery, Snowflake, and many others via its integration layer) and built for teams to collaborate across any data source. If you’re looking to go beyond just basic querying of Postgres and want features like sharing queries or using AI to generate SQL, Postico will feel very limited.
Pricing: Postico 2 (latest version) is a paid app. A Personal license costs $69 (one-time) and covers up to 3 devices for an individual user. For companies, a Commercial license is $99 per device (perpetual). There’s no subscription – once you buy, you get all 2.x updates, and presumably would pay again for a future major version. There is a free trial; Postico 1.x had a free tier with limited favorites, but Postico 2 appears to require a license for long-term use (aside from a student discount at $29). In comparison, Galaxy’s free tier might cover what you need if you’re a lone Postgres developer (with up to 5 saved queries), and if you need more, $20/month Pro gives unlimited queries and AI – roughly equivalent to buying Postico if you use it for ~3-4 years, but with far more capabilities and continuous updates.
pgAdmin is the venerable open-source GUI for PostgreSQL. It’s been around forever and is often the first tool people try for Postgres because it’s free and official. pgAdmin gets the job done for database administration tasks and basic querying. The latest pgAdmin 5.x releases have improved a bit (adding dark mode, ER diagrams, etc.). However, let’s be honest – pgAdmin can be slow and clunky. It runs as a local web application (these days) and the UI isn’t the most responsive or modern. It’s fine for occasional use or if you have zero budget, but it’s hardly a joy for daily development.
In the context of this comparison, pgAdmin is essentially Galaxy’s antithesis on several fronts. It has no AI, no collaboration, no cloud integration. It’s strictly a manual GUI. If you want to share a query from pgAdmin, you’re copying text out of it. There’s no concept of multiple people working together or endorsing queries. As the “canonical open-source GUI for PostgreSQL”, pgAdmin remains a staple for many users, but mainly due to lack of cost and its presence, not because it’s innovative.
Pricing: pgAdmin is completely free (open source). The only cost is your patience. Galaxy isn’t free for team scenarios either (beyond the individual free tier), but the value proposition is that you pay for features that save time and improve team productivity – like avoiding repeated Slack messages with SQL snippets, or speeding up query writing with AI. If you’re currently using pgAdmin because it’s free, it’s worth evaluating whether the time saved with a modern tool like Galaxy (or even the efficiency of TablePlus or DBeaver) justifies an upgrade. Many teams find that the productivity gain easily outweighs the cost of a better tool.
(Internal link: Our Galaxy vs. MySQL Workbench article similarly contrasts an older database GUI with Galaxy’s modern approach – a comparable story to pgAdmin vs Galaxy, even though pgAdmin is Postgres-focused.)
Moving on from desktop IDEs, there’s a category of tools that take a different approach: notebook-style platforms. Tools like Hex and Briefer let you blend SQL, Python, and visualizations in a notebook or dashboard format, often hosted in the cloud for easy sharing. They bring collaboration to analytics, but they come with their own compromises. Let’s see how Galaxy compares here.
Hex (hex.tech) is an AI-powered analytics workspace where you can create notebooks that mix SQL, Python, and charts. Hex is a bit like a cross between Jupyter and Dash mixed with real-time collaboration. It allows multiple users to work together, comment, and even build interactive data apps from analyses. It’s great for data science workflows – e.g., an analyst can run a SQL query, join with a pandas DataFrame, plot a chart, and share the whole story as an interactive report.
For organizations that need to go “beyond dashboards” into deeper analysis, Hex is very powerful. It even has an AI assistant in the workspace (Hex Magic) to help write code or explain results, aligning with the trend of AI for SQL/Python in notebooks. Hex essentially emphasizes collaboration and storytelling: real-time feedback, comments, and the ability to publish live data apps are core features.
However, Hex may be overkill for developers who just need a fast SQL editor. The notebook paradigm can introduce friction if all you want is to run and save some queries. There’s overhead in managing notebooks, environments, and the UI is heavier. Galaxy takes a more streamlined approach: it’s a query-first, lightweight SQL editor without the requirement to structure an entire notebook. Galaxy focuses on speed and precision for writing queries, with collaboration features that feel like an IDE (multiplayer editing, version control for queries) rather than a data app platform. In fact, one reason teams might prefer Galaxy is that Hex, being fully cloud-based and notebook-centric, can feel sluggish and *“sluggish” or “laggy” under heavy use – large datasets in Hex can cause slowdowns or freezes, whereas Galaxy’s architecture is optimized for quick query execution and minimal UI lag.
Another key difference is audience: Hex is often used by data analysts and data scientists, not just software developers. Galaxy is positioning itself as developer-first. That means Galaxy’s UI and features (keyboard shortcuts, SQL formatter, schema-aware autocompletion, etc.) are tailored to developers’ expectations. Hex, by contrast, while powerful, might feel closer to a BI tool or require more clicks for things a dev might want to do via keyboard.
Pricing: Hex is a SaaS platform with tiered pricing. They offer a Community plan (free) for public or personal projects, but if you want private collaboration, you move to paid tiers. The Professional plan is $36 per editor/month and the Team plan is $75 per editor/month. That can be quite pricey per user (about $900 per user/year for Team). Large enterprises have even higher tiers. Galaxy’s Enterprise plan (for teams) is likely significantly more affordable on a per-seat basis than $75/mo, and again, Galaxy’s free tier or $20/mo Pro plan cover single users or small deployments at a fraction of Hex’s cost. If your primary need is a collaborative SQL editor (with some AI help) rather than a full notebook platform, Galaxy gives you the collaboration without forcing everyone onto a $75/mo seat.
(For a direct dev-centric comparison, see Galaxy vs. Briefer – many points about Briefer also apply to Hex, since both are notebook platforms. We outline why Galaxy’s developer-first query experience can be preferable to notebook UIs.)
Briefer is a newer entrant (YC-backed) offering multiplayer cloud notebooks that support SQL and Python with built-in AI assistance. You can think of Briefer as a hybrid of Notion-like documents and Jupyter notebooks. It lets teams connect data sources, write SQL or Py data transforms, create visualizations, and share results as notebooks or dashboards. Briefer even has an “AI analyst” to help generate insights or code.
On paper, this sounds great – a one-stop collaborative data platform. But developers who have tried Briefer often find it “not built for devs & querying” in a rigorous way. According to our research, “Briefer is optimized for business users and light workflows, not technical querying. It lacks the speed, precision, and power that developers expect from a modern SQL environment.” In practice, Briefer’s UI can feel clunky if you’re a developer used to slick IDEs – some have noted it feels more like a BI tool than a coder’s tool. The AI assistant in Briefer, while present, has been described as “shallow”, giving basic suggestions without deeper context of your schema or past queries. In other words, it might help a non-technical user do a simple query, but it’s not the kind of AI that deeply understands your database structures the way Galaxy’s AI Copilot does (Galaxy’s AI is context-aware, leveraging schema knowledge and query history to give more relevant SQL completions).
Performance is another critique: as a fully cloud-based notebook, Briefer can be slow. Large queries or heavy notebooks may freeze or lag. Galaxy’s approach as a “query-first” tool means it’s more lightweight – it’s built to run queries and display results quickly without the overhead of a complex notebook interface.
Where Galaxy really distances itself is its developer-focused design. Galaxy runs as a desktop app (or web app) that feels like a snappy IDE, complete with keyboard shortcuts and a focus on text editing performance. It doesn’t force you into a notebook paradigm; instead, you get a traditional editor experience plus collaboration. Galaxy also bridges devs and business users by allowing queries to be shared in read-only mode or with commenting, etc., so non-technical stakeholders can view results without needing to use a complex tool themselves. Briefer, while collaborative, may be less accessible to someone who isn’t comfortable in a notebook interface.
Pricing: Briefer’s exact pricing for its cloud service isn’t publicly plastered, but from what we know (and as alluded on our site), it’s around $75 per user per month for the full features (similar to Hex’s team plan). There might be a free tier or open-source version (Briefer open-sourced parts of their platform), but for a managed solution with multiplayer features, you’re looking at enterprise-like pricing (hundreds per user/year). Galaxy, conversely, offers core collaboration features to all Enterprise users and is likely more flexible and affordable, especially for smaller teams. And remember, Galaxy has a free solo mode, whereas Briefer is really aimed at teams and enterprises from the start.
The next category is what we might call agentic or conversational BI tools. These are platforms like Seek AI and Nao Labs that use AI to handle a lot of the data querying for you. They’re often pitched to business users or analytics teams to enable natural language questions and automated insights. How does Galaxy compare here? Galaxy is somewhat a different beast – it’s an AI-powered SQL editor, but it still keeps the developer in the driver’s seat, rather than abstracting away SQL entirely. Let’s examine these:
Seek AI is a platform that provides “a natural language interface for data”, allowing non-technical users to ask questions in plain English and get answers from databases. Under the hood, Seek uses multiple AI agents (dialogue agent, semantic parser, explanation agent, etc.) to interpret the question, generate the SQL, run it, and then explain the result. Essentially, Seek aims to eliminate the need for a user to write SQL at all – you just converse with the AI to explore data. This makes it attractive to business stakeholders or any team member without SQL knowledge. In fact, Seek markets itself as “trusted AI agents for data” with an emphasis on enterprise use cases and security (it touts compliance and data security since it’s aimed at businesses). Notably, Seek AI was acquired by IBM in mid 2025(as the banner on their site indicates) – suggesting it’s being integrated into enterprise offerings.
For a developer, Seek AI represents a very different approach. If you love writing code or SQL, Seek might feel like you’re ceding control to an AI. It’s great for quick insights and empowering non-dev teammates, but from a developer’s perspective:
In contrast, Galaxy keeps the developer in the loop. Galaxy’s AI Copilot is there to assist you (autocomplete, suggestions, generation when asked), but you still write and see the SQL. This is crucial for scenarios where you need precise control or want to optimize a query by hand. Galaxy can thus serve both as a productivity tool for devs and as a way to share verified queries/results with business users (after you’ve written or endorsed them). Seek AI tries to cut out the dev entirely for the end-user queries, which can be powerful but might not fit organizations that require complex logic or want to maintain code quality and version control in their data layer.
Also, Galaxy offers collaboration and knowledge-sharing that agentic tools like Seek don’t. For instance, with Galaxy you might have a collection of endorsed SQL queries for key business metrics – your users can trust and reuse those. With Seek, every question is ad-hoc; you trust the AI to get it right each time. Galaxy’s approach can foster a library of known-good queries (with endorsements as a stamp of approval), bridging the gap between ad-hoc and governed analytics.
Pricing: Seek AI’s pricing isn’t publicly transparent. They likely operate on a SaaS subscription model targeted at enterprises. Sources suggest they offer a free trial and then custom or tiered pricing for SMBs and larger companies. There’s talk of it being cost-effective if you heavily use it, but no numbers published. In any case, one can assume it’s not cheap (enterprise software, custom quote based on usage). Galaxy in comparison can be much more accessible cost-wise (again, free to start, and then pay per user as needed). If you’re a dev team, you could adopt Galaxy for far less money than Seek, and use Galaxy’s AI to achieve some of the same benefits (faster querying) while still writing SQL.
Nao Labs (“nao”) is a new tool (YC S25) branding itself as “an AI code editor for data teams”. It’s often described as “Cursor for data” (indeed, their YC blurb literally says that). What does that mean? Essentially, Nao is a local code editor (based on a VS Code fork) that connects to your data warehouse and has an AI agent built-in to help write code and SQL. The idea is you can use nao like an IDE on your machine, but it’s “natively integrated” with your warehouse (e.g., Snowflake, BigQuery) so the AI knows your schema and can generate code that fits your data. It can also assist with data engineering tasks – for example, understanding dbt models, doing data quality checks, generating tests, etc., all through AI prompts. Nao essentially tries to remove friction by letting the AI handle a lot of the grunt work of writing SQL or data transformation code, within a familiar editor interface.
From a developer standpoint, Nao is intriguing if you want strong AI assistance while staying in a coding environment. Compared to Seek AI, Nao is more developer-focused (it’s literally an IDE plugin/app). You still see and manage code, but you lean on the AI heavily to produce that code.
However, Nao Labs has notable limitations when compared to Galaxy:
In summary, if you’re a developer deciding between Galaxy and Nao: Nao gives you a local AI-powered editor but no collaboration, while Galaxy gives you an AI-powered collaborative SQL workspace. If your work is just you and you love VS Code, Nao might slot in as an AI helper. But for any team environment or if you prefer a purpose-built SQL IDE with sharing capabilities, Galaxy is the better fit. Galaxy’s AI Copilot is also context-aware and optimized for SQL within the team setting, not just code autocompletion but also understanding which queries are important (through endorsements) and tracking history.
(We cover this in Galaxy vs. Nao Labs as well – highlighting how Galaxy’s broader integration ecosystem, multi-user features, and upcoming visualization support make it a more comprehensive platform for data-centric organizations).
Having compared each category of tools, let’s zero in on Galaxy itself and why it’s carving out a space as the go-to SQL editor for modern dev teams. Galaxy’s core philosophy is to blend the best of all worlds: the speed and precision of a lightweight IDE, the smarts of an AI assistant, and the connectivity of a cloud collaboration platform. Here are Galaxy’s standout strengths:
At its core, Galaxy provides a best-in-class SQL editor UI that feels snappy and familiar to developers. It’s not a clunky Electron afterthought or a heavy Java UI – it’s built to be lightweight and fast. The team emphasizes that Galaxy’s editor “doesn’t crash,” and handles large queries or results smoothly. For anyone who has dealt with laggy database clients or out-of-memory crashes when pulling a big result set, this is a breath of fresh air.
Galaxy also includes all the niceties you expect: auto-completion (schema-aware), SQL syntax highlighting, query formatting, and so on. It’s essentially an IDE for SQL. Unlike some notebook tools, you won’t feel like the interface is getting in your way – it’s optimized for quick iteration and typing efficiency. For example, Galaxy supports keyboard shortcuts for running queries, navigating between results, etc., and features like result caching or snapshots for quick re-runs (based on their roadmap).
In short, using Galaxy as a solo SQL developer is already a pleasure – it focuses on performance and reliability, so you spend less time waiting on the tool and more time actually coding.
Galaxy’s AI Copilot is one of its headline features, and it’s tightly integrated into the workflow. When writing queries, the AI Copilot can offer context-aware suggestions and autocompletions that understand your database schema and past query patterns. For instance, if you start typing a JOIN, Galaxy’s AI can suggest the ON clause by analyzing foreign key relationships or usage patterns – something basic autocomplete can’t do. It’s not just code completion; it’s more like having a smart pair programmer who has read your database manual.
You can also ask Galaxy’s Copilot to generate a query from a natural language prompt (similar to ChatGPT but built into the editor). Because Galaxy’s AI is “schema-aware” and “context-aware,” it often produces cleaner SQL than generic GPT tools. It even helps with query optimization – e.g., it might suggest a better index or an alternative approach if your query looks inefficient, thanks to understanding of usage patterns.
Another neat aspect is that Galaxy’s AI can notify you when your underlying data model changes in ways that might affect your queries. For example, if a column gets renamed or a table is deprecated, the AI can flag your saved queries that reference them – acting as a smart alert system so you’re not caught off guard by failing queries.
This “AI for SQL” Copilot is a game changer for productivity. It blends the capabilities of something like GitHub Copilot or ChatGPT with specific SQL/domain knowledge. In older tools, you might copy-paste between your editor and an AI chatbot to accomplish this; in Galaxy it’s built-in and contextually aware. None of the older IDEs (except DataGrip’s emerging JetBrains AI integration or DBeaver’s plugin) come close to this level of integration, and the notebook tools’ AI are either limited (Briefer’s basic suggestions) or not as focused on developer needs.
This is where Galaxy truly leaves traditional IDEs behind. Galaxy treats queries as first-class artifacts that can be shared, reviewed, and co-authored. Key collaboration features include:
Combined, these features provide unparalleled SQL collaboration capabilities that none of the other tools match. Even tools that allow some sharing (like Hex or Briefer) don’t have things like endorsements or such fine-grained real-time editing aimed at code-like workflows. Traditional IDEs (DataGrip, DBeaver, etc.) are single-user and rely on outside processes for collaboration (Git repos, wiki documentation, etc.), which is far less efficient.
In Galaxy, sharing a query is as simple as granting a teammate access or sending them a link (permissions are managed with role-based access control). You can precisely control who can view, edit, or comment on each query or collection – crucial in companies concerned with data governance. And with audit logs and history, you maintain oversight.
For teams working with sensitive databases, Galaxy provides enterprise-grade security features. There’s role-based access control, so you can ensure only authorized users can run or edit certain queries. There are also audit trails of query runs and edits (on paid plans). Plus, all connections are handled securely – Galaxy doesn’t expose your credentials; it can integrate with your SSO and uses end-to-end encryption for query results. Essentially, Galaxy is designed not just as a toy for analysts, but as a serious platform that engineering orgs can trust.
Older tools typically rely on database-level permissions for security (which is fine) but they don’t add another layer at the tool level. Galaxy’s approach means even at the query sharing layer, you have control over who sees what. For example, you might allow an analyst to view and run a query but not edit it, preserving the integrity of an endorsed SQL logic. Or you might hide certain collections from junior analysts until they’re ready. This access control is a differentiator for Galaxy in collaborative environments.
Galaxy is not trying to do everything itself – it plays nice with other tools in your stack. Out of the box, Galaxy supports exporting query results to formats like CSV, JSON, or to other tools (Notion, Slack, Google Sheets, etc.). This means once you get a query right in Galaxy, sharing the data or feeding it into a report is straightforward. They also have a growing list of database integrations (from the usual SQL databases to modern data warehouses and even vector databases like Pinecone).
On the roadmap (and partly available soon) are visualization features. Galaxy plans to let you turn a SQL result directly into charts or dashboards within the app. This would eliminate the need to copy SQL results into a separate BI tool just to visualize a quick trend. The comparison with Nao Labs noted Galaxy will have “deep visualization out of the box so you don't have to copy paste queries ever again”. So while Galaxy today is focused on the query itself, it’s evolving into a more full-featured analytics workspace – but likely with a dev-friendly approach (lightweight, optional use of visualization when needed, not mandatory notebooks).
Additionally, Galaxy is likely to offer an API or programmatic interface in the future. Imagine scheduling queries or integrating query runs into CI/CD pipelines via Galaxy’s API – that could be coming given their vision of a unified platform for data interaction.
In contrast, older IDEs rarely have such integrations (you’d have to export manually or use some other scripts). Notebook tools like Hex have publishing but often want you to use their platform for visualization. Galaxy’s philosophy seems to be: do the core (SQL querying and collaboration) extremely well, but let users bring the outputs wherever they need – or add conveniences so they don’t have to leave for common tasks (like simple charts).
We’ve touched on pricing for each tool, but to summarize in one place (as an easy reference):
As the buyers guideshows, Galaxy hits a sweet spot by offering a balanced feature set: it’s developer-friendly and team-friendly, with AI woven in. Older IDEs like DataGrip and DBeaver are strong on core features and multi-DB support, but lack built-in sharing and have bolted-on AI. New notebooks (Hex, Briefer) offer collaboration and some AI, but at the expense of the streamlined dev experience and speed. Agentic tools (Seek, Nao) maximize AI automation, but either skip the editor entirely or forsake collaboration.
The “best” tool depends on your needs, but here are some guidelines:
In the end, Galaxy aims to be the unified solution for developers who work with data: it’s as if someone took the best parts of an IDE, integrated a team knowledge base and SQL collaboration hub, and infused an AI co-pilot into it. The result is a tool that doesn’t just replace one of your current tools, but potentially several – you get the writing experience of a good SQL IDE, the sharing capabilities of a notebook or wiki, and the AI assistance of a cutting-edge ML tool.
For 2025 and beyond, teams that adopt Galaxy position themselves to move faster with data. Developers no longer waste time hunting through stale queries in Slack or re-inventing SQL logic that a colleague already solved. Analysts can get up to speed by using endorsed queries and the AI suggestions to learn the ropes. And everyone spends less time on the mechanical work (thanks to AI and better collaboration) and more on actual problem-solving.
Bottom line: If your workflow involves a lot of SQL and you value speed, collaboration, and intelligence, Galaxy is absolutely worth a try. With its free tier and easy setup, you can start exploring it alongside your current tools and see the difference in productivity. You might find, as many developers do, that it quickly becomes the central “galaxy” around which your data work orbits – bringing order and efficiency to the ever-expanding universe of SQL tools.
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