Galaxy vs DataGrip, TablePlus, DBeaver & More: The Ultimate 2025 SQL Editor Buyer’s Guide | Galaxy

In the world of data-driven development, having the right SQL editor can make or break your workflow. From legacy desktop IDEs to new AI-powered cloud tools, the landscape in 2025 offers more choices than ever for writing and running SQL. This buyer’s guide compares Galaxy – a modern, AI-enabled SQL IDE – against popular alternatives like JetBrains DataGrip, TablePlus, Postico, pgAdmin, DBeaver, Hex, Seek AI, Nao Labs, and others. We’ll break down how Galaxy stacks up in performance, collaboration, and intelligence, and help you find the best SQL editor for your team’s needs.

Developers and data engineers today are looking for the fastest SQL IDE with robust features: lightning-fast execution, rich autocomplete, integrated AI assistance, and team-friendly sharing. Legacy SQL tools (e.g. DataGrip, DBeaver) often feel clunky or siloed, while newer data notebook platforms (Hex, Briefer) and AI-based query tools (Seek AI, Nao Labs) take different approaches. Let’s explore each category and see how Galaxy – “the Cursor for SQL” – addresses common pain points like fragmented queries, recurring ad-hoc requests, untrusted SQL snippets, and the risks of LLM-generated queries.

Galaxy at a Glance: A Modern SQL Editor for Developers

Galaxy is a developer-first SQL IDE built for speed, collaboration, and AI assistance. It combines the familiarity of a desktop code editor with the convenience of a cloud workspace. In short, Galaxy is designed to be the best SQL editor in 2025 for teams that demand high performance, security, and modern workflows.io. Here’s what makes Galaxy stand out:

• Blazing-Fast SQL Editor – Galaxy’s core editor is optimized for lightning-fast performance with minimal memory usage. It executes queries quickly without the bloat of Java-based IDEs, and won’t hog your RAM or battery. (No more waiting or crashes due to heavy clients!) The UI is elegant and responsive, with smart autocomplete and syntax highlighting for a smooth developer experience.
• Context-Aware AI Copilot – Galaxy comes with a built-in AI for SQL that acts as a context-aware copilot. It can understand your database schema and query history to auto-generate SQL, optimize queries, and even suggest fixes when the data model changes. Unlike simplistic plugins, Galaxy’s AI is deeply integrated – essentially like having “Cursor for SQL” as your pair programmer. The AI assists you without replacing you – meaning you retain control, reducing the risk of AI mistakes while still speeding up your workflow.
• Real-Time SQL Sharing & Collaboration – Galaxy is built for teams. It enables SQL sharing and collaboration through Collections – shared folders of queries where team members can organize SQL by project or topic. You can co-edit queries, comment, and even endorse certain queries as the single source of truth. This eliminates the version chaos of emailing snippets or pasting SQL in Slack, so everyone stays aligned.
• Access Control and Security – With role-based permissions, edit history, and fine-grained access control, Galaxy ensures that collaboration doesn’t come at the expense of security. Team leads can grant read or write access per Collection and track changes easily. All data connections and queries are handled with enterprise-grade encryption and compliance (critical for sensitive data).
• Unified Desktop + Cloud Experience – Galaxy offers a beautiful native desktop application (for macOS, Windows, Linux) as well as a web-based cloud IDE. This hybrid approach lets developers work in their preferred environment while still benefiting from cloud syncing and collaboration. It feels like a familiar SQL IDE but with the convenience of the cloud when you need it.
• Developer-Centric Design – From intuitive keyboard shortcuts and theming to integrations with tools like Slack, Notion, and Git, Galaxy is tailored to software engineers’ workflows. It supports any SQL database (Postgres, MySQL, Snowflake, etc.) and is extensible for future needs. In short, it’s built “for the devs of the future”, not stuck in the past.

Galaxy’s value proposition: Write SQL faster, stay aligned on data, and cut down on busywork with a modern editor, AI SQL collaboration, and built-in sharing. Now, let’s see how this compares to other types of SQL tools in the market.

Galaxy vs Traditional SQL Editors (DataGrip, DBeaver, TablePlus, Postico, pgAdmin)

Legacy SQL desktop applications have been the go-to for developers for years. Tools like JetBrains DataGrip, DBeaver, TablePlus, Postico, and pgAdmin are installed clients that connect to your databases. Developers appreciate their robust feature sets and familiarity – but these tools also show their age in 2025. Here’s how they compare to Galaxy:

DataGrip: Powerful but Heavy and Isolated

JetBrains DataGrip is a popular IDE for SQL that supports many databases. It offers intelligent code completion and is part of the JetBrains suite, which many developers knowi. However, DataGrip is also known to be resource-intensive and lacks collaboration:

• Performance: DataGrip runs on Java (IntelliJ platform) and can be heavy on memory/CPU. JetBrains even documents tips for increasing memory if you experience slowdowns. Many users find it “heavy and clunky” in daily use. In contrast, Galaxy’s native engine is lightweight and fast, avoiding those pauses and crashes.
• AI Integration: DataGrip does not have built-in AI. It requires a third-party plugin (like a ChatGPT extension), which often feels buggy and lacks context‍. Galaxy’s AI copilot is built-in and context-aware, so it can use your schema and past queries to generate correct SQL and even catch schema changes.
• Collaboration: DataGrip is a single-user desktop app – it has no real-time collaboration or sharing features‍. Teams end up sharing SQL via copy-paste or version control outside the tool. Galaxy wins here with one-click sharing of queries and collections that serve as a version-controlled library for your team.
• Pricing: DataGrip is a paid product (approximately $229/year for a JetBrains subscription). Galaxy, on the other hand, offers a free tier for individuals with generous features (including AI), and affordable team plans starting around $20/month.

Bottom line: DataGrip is a powerful all-in-one IDE if you’re already in the JetBrains ecosystem, but it feels outdated in a collaborative, AI-driven era. Galaxy provides a snappier, team-friendly alternative without the bloat.

DBeaver: Open-Source Workhorse with No Team Features

DBeaver is an open-source, cross-platform database GUI beloved for supporting dozens of databases. It’s a solid choice for many developers and DBAs, especially since the Community Edition is free. However, DBeaver shows similar shortcomings when compared to Galaxy:

• Performance: DBeaver (built on Java/Eclipse) can handle basic tasks well, but it may become slow on very large data sets or require tweaking settings for optimal performance. Galaxy’s architecture is designed for efficiency out-of-the-box, so even complex queries or large results feel smooth.
• UI/UX: DBeaver’s interface is powerful but complex, with a steeper learning curve. Galaxy emphasizes a clean, intuitive UI – for example, a modern dark/light theme, simplified menus, and context-sensitive help – making it more approachable for newcomers without sacrificing power for experts.
• Collaboration: Like other traditional IDEs, DBeaver is single-user. It does not support real-time collaboration or shared query repositories. Any sharing of queries is manual. Galaxy’s real-time sharing (multiple people can view/edit a query simultaneously) and Collections with versioning are a big step up for team workflows.
• AI Assistance: DBeaver has no built-in AI features. One might script external AI or use plugins, but it’s not a native capability. Galaxy’s integrated AI copilot is a unique advantage for those who want AI for SQL tasks (query generation, completion, fixes) in their day-to-day editor.

For solo developers on a tight budget, DBeaver is a capable tool (and Galaxy’s free mode can actually complement that). But for 2025’s data teams who need speed and collaboration, Galaxy offers a more innovative environment.

TablePlus & Postico: Lightweight GUI Tools vs Galaxy

TablePlus and Postico are lightweight GUI clients (primarily for macOS, though TablePlus is cross-platform now). They are praised for being simple, fast, and user-friendly for browsing databases and running queries:

• Speed & UI: TablePlus, in particular, is known for its native speed and sleek UI. It’s a small install and launches quickly. Galaxy is similarly focused on performance – it’s built from scratch to be blazing-fast and memory-efficient, so neither tool will slow you down. Galaxy’s interface is equally polished (and arguably more modern in styling).
• Features: TablePlus and Postico cover basics like query editing, table data viewing, and simple charting. But they lack advanced features – e.g., no AI assistance, limited extensibility, and minimal scripting or automation. Galaxy’s feature set is far richer: from AI-assisted query writing to parameterization, saved query templates, and forthcoming additions like built-in visualizations.
• Collaboration: These tools are strictly single-player; there’s no built-in query sharing or multi-user mode. If you want to share a query from TablePlus, you’re copying it into Slack or saving it to a file. This can lead to the usual fragmentation and version confusion. Galaxy eliminates that by letting you share queries natively and even “endorse” the official versions for others to reuse (solving the *“is this the right SQL?” doubt).
• Database Support: Postico is Postgres-only. TablePlus supports many databases but with varying depth. Galaxy connects to any SQL database with a JDBC or native driver – from MySQL and Postgres to Snowflake, BigQuery, and more – just like the heavy IDEs do. So you get broad compatibility in a modern package.
• Price: TablePlus has a free tier but requires a paid license (~$99) for full usage; Postico is a paid app (around $50-$100). Galaxy’s core offering is free for personal use, with premium plans for team features. Cost-wise, Galaxy can be more economical for teams when you factor in its all-in-one capabilities (versus buying multiple licenses and extra tools).

In summary, TablePlus and Postico are great SQL editors for Mac users who need a quick query tool, but they operate in a silo. Galaxy provides a similarly snappy experience while adding the collaboration and AI features missing in those tools.

pgAdmin: Open-Source PostgreSQL Tool vs Galaxy

pgAdmin deserves a mention as the long-standing open-source GUI for PostgreSQL. It’s free and widely used for basic Postgres administration. However, user feedback on pgAdmin highlights several pain points that Galaxy addresses:

• User Experience: pgAdmin has a reputation for a slow, non-intuitive UI, and it can be clunky particularly in its web app form. Many developers find it dated. Galaxy, by contrast, offers a modern IDE UX with smooth interactions and a more coherent design.
• Performance: Users often complain that pgAdmin uses too many resources and can be sluggish, even hanging on heavy operations. Galaxy’s efficient engine means even large queries or results won’t freeze your interface.
• Features: As a specialized Postgres tool, pgAdmin includes things like graphical explain plans and monitoring dashboards for Postgres – but it lacks advanced coding aids (no AI, limited autocomplete intelligence) and is Postgres-specific. Galaxy provides intelligent coding assistance and works across different databases, which is useful if your environment is heterogeneous.
• Collaboration: pgAdmin is single-user; there is no concept of team collaboration. Just like other traditional tools, any sharing is done via external means (exporting scripts, etc.). Galaxy’s team features fill this gap by letting you share queries and results in-app, endorsing canonical queries for others to trust.
• Use Case: pgAdmin is often used by DBAs or those needing to manage Postgres configurations, whereas Galaxy is aimed at developers and data analysts writing queries for insights. If your goal is to speed up querying and analysis (and not to tweak Postgres server settings), Galaxy provides a far better user experience.

In essence, pgAdmin shows the limitations of older-generation tools – it’s free but not very developer-friendly by modern standards. Galaxy brings the convenience of a polished commercial tool with none of the cost for its basic usage.

Galaxy vs Modern Data Notebook Platforms (Hex, Briefer, etc.)

Moving beyond classic IDEs, there’s a category of cloud-based data notebooks and workspaces exemplified by tools like Hex and Briefer (and others like Mode or Deepnote). These platforms blend SQL, Python, and visualizations in a notebook interface, often with collaborative features. How does Galaxy compare to these when it comes to SQL work?

Hex & Briefer: Collaborative Notebooks vs Developer IDE

Hex and Briefer are recent entrants that offer a Google-Docs-style collaboration on data analysis. Teams can work in a shared environment, mixing code and charts, with some AI assistance sprinkled in. Here’s how they differ from Galaxy:

• Interface & Workflow: Hex and Briefer use a notebook paradigm – think cells that can run SQL or Python, results that can be visualized inline, and the ability to create interactive reports. This is great for exploratory analysis and presentations. However, for a developer who wants a pure SQL editing experience, notebooks can feel cumbersome. Galaxy sticks to an IDE-style interface for writing and running SQL, which many engineers prefer for rapid querying (no need to manage notebook state or outputs).
• Collaboration: Both Hex and Briefer are multiplayer by nature – multiple users can edit notebooks, add comments, etc. Galaxy matches this with real-time collaboration on queries. The difference is granularity: in Galaxy you share individual queries or collections of queries, whereas in Hex/Briefer you typically share an entire notebook or project. Galaxy’s approach can be more lightweight – e.g. sharing one SQL snippet result with a teammate is simpler than navigating a whole notebook. It also encourages modular reuse (each query can be documented and endorsed).
• AI Features: Hex has introduced “Hex Magic” (an AI assistant to generate SQL or code), and Briefer touts a built-in AI analyst. These AI features are geared toward making it easier to produce queries or insights for less technical users. Galaxy’s AI SQL copilot is similarly there to help generate and improve queries, but with a key difference: context-awareness. Galaxy’s AI understands your schema deeply and integrates with the editing workflow (not just a chat on the side). This can lead to more accurate suggestions and the ability to chat with your database in a focused way. For example, Galaxy’s AI can fill in a complex join or recommend an index, things a generic AI might miss.
• Use Cases: Notebook tools shine when you need to blend SQL with further analysis (Python, ML, rich plotting) in one environment. They often target data scientists or analytics teams. Galaxy is laser-focused on SQL querying and data exploration for developers. If your primary need is to quickly get SQL results and share them or integrate them into apps, an IDE like Galaxy will be more direct. On the other hand, if you need a full analysis narrative or dashboard, a notebook might be the choice – although Galaxy is evolving with features like lightweight visualizations and scheduled queries on its roadmap.
• Integration and Deployment: Hex and Briefer are fully cloud SaaS – you run them in the browser, and data must be accessible from their cloud (which can raise security considerations). Galaxy offers a hybrid: you can run the desktop app locally for direct database access (no data leaves your network except results you share), or use the cloud version if you prefer. This flexibility can be important for enterprise scenarios where data access is restricted.
• Cost: These notebook platforms are typically paid SaaS per user. For example, Hex’s professional plan is around $36 per editor/month (with enterprise tiers higher). Briefer is also a paid platform (often justified by the AI and notebook features). Galaxy’s free tier allows a lot of functionality for a single user, and upgrading for team features is often cheaper on a per-user basis. If you have a small dev team that mainly needs an SQL IDE, Galaxy could be more cost-effective than paying for heavy notebook software that you might not fully utilize.

In summary, notebook tools vs Galaxy comes down to your workflow: if you need an interactive notebook for mixed data analysis, something like Hex is powerful. But if your developers crave a fast SQL editor with code-like precision and real-time sharing, Galaxy is a better fit. It brings many of the collaboration benefits of notebooks (shared workspaces, comments, etc.) without forcing you into a notebook interface.

Galaxy vs AI-Powered SQL Assistants (Seek AI, Nao Labs, etc.)

A new category in 2025 is the rise of AI-driven SQL query tools. These include conversational interfaces and agent-based systems like Seek AI and Nao Labs, which aim to let you get answers from your data by simply asking natural language questions or having an AI generate code for you. How does Galaxy compare to these AI-first approaches?

Seek AI & Similar Tools: Natural Language to SQL vs Galaxy’s Copilot

Seek AI (recently acquired by IBM) is known for its generative AI that allows non-technical users to ask questions in plain English and get SQL answers. It essentially acts as an AI SQL query generator for your database. This can be powerful for business users, but there are considerations:

• Target User: Seek AI is largely built to empower business analysts or non-SQL folks to get data without writing code. Galaxy, on the other hand, is built for developers and data engineers who do write SQL, but want to do it faster and more safely with AI help. If you have a technical team, Galaxy’s copilot will feel like an enhancement to your workflow, whereas a tool like Seek AI might feel like ceding control to an opaque AI agent.
• Accuracy and Trust: One major issue with fully automated NL->SQL tools is LLM-generated query risk. Large language models can produce SQL that looks correct but is subtly wrong or inefficient – and a non-technical user might not catch the mistake. In fact, even fine-tuned models can make mistakes that lead to incorrect numbers or confusing results, and those errors are “often not obvious”, potentially leading to decisions based on wrong data. Seek AI and similar tools have tried to mitigate this by involving data teams as approvers of queries, effectively adding humans back into the loop. Galaxy’s philosophy is to keep the developer in control from the start: the AI suggests or generates queries, but a human reviews, tests, and tweaks them in the familiar editor. This copilot model significantly reduces the chance of an unnoticed hallucination slipping through.
• Context & Schema Knowledge: Seek AI can connect to popular databases like Snowflake, Redshift, etc., and it uses the database metadata to some extent. However, Galaxy’s AI is truly schema-aware in context – because it lives in your IDE, it knows your exact table structures, relationships, and even the names of previously saved queries or common subqueries in your workspace. This context means Galaxy’s AI can produce more precise SQL and even warn you if, say, a table or column has changed (something a generic chat interface wouldn’t know in real time). One external analysis noted that “context is everything” in getting accurate SQL from AI – Galaxy’s design embraces that by embedding the AI where context is richest.
• Interface & Workflow: Seek AI typically provides a chat or Q&A interface – a business user asks a question, the AI returns an answer or chart. This is great for quick fact-finding but not as much for building complex data pipelines or iterative queries that a developer might do. Galaxy keeps the workflow in a code editor format, which is better suited for iterative development of SQL (and debugging, optimization, etc.). It still allows natural language prompts (you can ask Galaxy’s AI to “show total sales by region last month” and get a query) – but always with the SQL visible and editable by you. This fosters trust and transparency in how results are obtained, something critical for data quality.
• Collaboration & Reuse: Another limitation of chat-based tools is that question-answer pairs may not be easily reusable or visible to others. Galaxy, with its Collections and endorsements, ensures that useful queries (even AI-generated ones that you’ve verified) can be saved and shared for future use. In effect, Galaxy can turn one person’s successful query into a team asset, whereas an AI Q&A tool might answer the same question repeatedly for different people if they don’t share the results.
• Examples of other AI tools: There are other AI+SQL tools beyond Seek AI – for example, Nao Labs (a Y Combinator startup) offers an AI-powered data development environment with an agent that can generate SQL and even dbt models. It’s sometimes referred to as “Cursor for data teams”, indicating a focus on code generation similar to how Cursor (an AI code editor) works. Outerbase is another platform that lets you chat with your database or use AI to build queries, aiming to cater to non-technical users with a more GUI approach‍. These tools, while innovative, often target a slightly different user base (analysts or data ops) or they emphasize no-code interactions. Galaxy differentiates itself by augmenting real developers instead of abstracting them away. Think of it this way: Galaxy is not trying to replace SQL developers with AI – it’s trying to make SQL developers dramatically more productive and aligned with their teams.

Bottom line: If you need a system where anyone can ask something of the data in plain English, an AI agent like Seek AI might be worth exploring (keeping in mind the need for oversight). But for engineering teams that need to build reliable data queries and pipelines, Galaxy’s integrated AI copilot is a safer, more controlled approach. You get the benefits of AI-generated SQL without the blind trust – every query can be verified and tuned by a human, with the AI doing the heavy lifting on the tedious parts. In practice, this means faster development with less risk of major errors.

Solving Key Data Team Pain Points with Galaxy

Throughout these comparisons, several common pain points for data teams have emerged. Galaxy was explicitly designed to solve many of these problems that plague users of older SQL tools. Let’s address each briefly:

• Fragmented Queries and Knowledge Silos: In many organizations, SQL queries are scattered across different engineers’ machines, Slack messages, Notion pages, or at best a Git repo. This makes it hard to find past work or trust that you have the latest version. In fact, analysts often become “human routers” for the same questions repeatedly because work isn’t centralized. Galaxy tackles this by providing a central hub for SQL – with Collections, all your important queries can live in one shared space, organized and searchable. Team members can discover existing queries instead of rewriting them from scratch, and because queries can be endorsed (marked as verified by a domain expert), there’s a higher level of trust. No more hunting through Slack threads for that one script you remember someone shared – if it’s important, it’s in Galaxy.
• Recurring Ad-Hoc Data Requests: Hand-in-hand with fragmentation is the issue of repeated ad-hoc requests. How many times have you been asked to “pull these numbers again” or explain an analysis you did months ago? As one data scientist noted, the majority of ad-hoc requests “felt avoidable” if only people had access to the previous work and its context. Galaxy helps reduce these requests by enabling self-service (with guardrails). Business users or less technical team members can run queries from Collections (with proper permissions) or use Galaxy’s AI Agent to ask questions with guidance from the data team’s curated queries and schema. Because Galaxy emphasizes transparency (everyone can see the query behind results), stakeholders can get answers with context rather than pinging analysts repeatedly. Over time, this can drastically cut down the Slack pings and meeting requests for “that report from last week.”
• Untrusted Shared SQL (Lack of Single Source of Truth): Without a proper system, even when queries are shared, people might not trust them. Was this SQL reviewed? Is it the latest version? This lack of trust leads to duplicative work or, worse, faulty data in decision-making. Galaxy’s solution is query versioning and endorsement. When a data lead endorses a query in Galaxy, it signals to everyone that “this is the canonical SQL for answering X.” Team members can reuse it confidently, and if it’s updated, they’ll see the change history. It’s akin to having version control for queries with an official stamp of approval. Compare this to emailing around SQL files – there’s no comparison in terms of establishing trust and accountability.
• LLM-Generated Query Risk and Safety: We touched on this in the AI tools section, but it’s worth reiterating as a pain point. Relying on AI to generate SQL (or code) can introduce errors and even security issues (like unintended cross-joins or even SQL injection if prompts are poorly handled). The risk is amplified when non-experts directly use these tools and take results at face value. Galaxy’s context-aware AI copilot is designed to mitigate these risks. First, by operating within the Galaxy IDE, the AI has the context to generate more accurate queries (reducing hallucinations). Second, Galaxy encourages a human-in-the-loop approach: the AI suggestions appear where the developer can review and edit them. The AI might do 90% of the work, but that last 10% – a developer’s eye verifying logic and adjusting edge cases – ensures the query is correct and safe. As a result, you get the productivity boost of AI without the “make mistakes quietly” downside that pure automated systems suffer from. Moreover, Galaxy’s team settings can require review or approval for AI-generated queries in sensitive environments, adding an extra layer of governance if needed.

By addressing these pain points, Galaxy aims to not just be another SQL editor, but a unified solution for writing, collaborating on, and trustfully deploying SQL in a team or organization.

Conclusion: Choosing the Right SQL Editor in 2025

As we’ve seen, the SQL tooling landscape in 2025 ranges from tried-and-true IDEs to cutting-edge AI assistants. Your choice will depend on your team’s priorities:

• If you value a fast, developer-centric workflow with modern luxuries like AI assistance and built-in collaboration, Galaxy is a compelling choice. It combines the strengths of an IDE (speed, control, rich editing) with the collaborative power of cloud platforms and the intelligence of AI. For many software teams, this balance makes Galaxy the ultimate SQL editor for 2025 and beyond.
• If your team is heavily invested in the JetBrains ecosystem or needs an all-purpose DB management tool, DataGrip might still serve you well – but be prepared for its resource demands and consider augmenting it with external tools for sharing (or give Galaxy a try alongside to offload those collaborative tasks).
• For open-source enthusiasts or occasional query needs, DBeaver or TablePlus/Postico can do the job for single-user work. Just recognize their limitations and perhaps use processes (like a git repo for SQL) to fill the collaboration gap – or simply upgrade to Galaxy when those limits start hurting productivity.
• If your use case extends to full-blown data analysis notebooks and interactive reporting, you might pair Galaxy with a tool like Hex – using Galaxy for the core SQL development and Hex for presenting results. However, note that Galaxy’s roadmap includes adding lightweight visualization and scheduling, which could cover a lot of those needs in the near future.
• For enabling non-technical users with AI, something like Seek AI can be complementary to Galaxy. You could allow business users to ask questions via Seek AI, but have the data team manage and curate the approved queries in Galaxy for ongoing use. In many cases, though, Galaxy’s AI copilot and upcoming features might bridge the gap such that even non-technical stakeholders can get what they need in a controlled way.

In the end, the goal is to empower your team to get insights from data quickly and safely. Legacy SQL editors brought us the power of GUI querying; notebook and AI tools brought new ways to broaden access to data. Galaxy is bringing these threads together – a tool that keeps developers in the driver’s seat, leverages AI where it helps most, and makes collaboration seamless rather than an afterthought.

Pro tip: If you’re curious about Galaxy, you can download Galaxy – the fastest AI-powered SQL editor for free and give it a spin. There’s no steep learning curve; if you know SQL, you’ll feel at home, and you might be amazed at how quickly you can produce results (with a little help from AI). Plus, with Galaxy’s free tier, there’s no barrier to trying it out alongside your current tools.

Finally, as you evaluate options, keep your team’s pain points in mind. The best tool is the one that eliminates friction in your daily work. For many in 2025, that means a tool that is fast, collaborative, and intelligent – and that’s exactly where Galaxy shines.

Frequently Asked Questions (FAQs)

Q: What is Galaxy and how is it different from other SQL editors?
A: Galaxy is a modern AI-powered SQL editor with a beautiful IDE interface, built-in context-aware AI copilot, real-time collaboration features, and robust security controls. Unlike traditional SQL editors that are single-player and lack intelligence, Galaxy combines lightning-fast SQL querying with AI-assisted generation and team-oriented features like shared query Collections and version control. Essentially, Galaxy serves as a unified platform for writing, sharing, and understanding SQL, whereas other editors often require plugins or external tools to offer a similar experience.

Q: Which SQL editors support AI assistance in 2025?
A: Galaxy is a front-runner with its integrated AI copilot for SQL. Other tools with AI features include Seek AI (natural language to SQL for business users) and Hex (which offers “Hex Magic” for AI suggestions in notebooks). Most legacy editors (like DBeaver, TablePlus, pgAdmin) do not have built-in AI – you would have to use external plugins or scripts, which are usually not as effective. Some newer IDEs and platforms (e.g., Chat2DB or Cursor) are also experimenting with AI. But as of 2025, Galaxy’s AI integration is one of the most advanced in the SQL editor category, especially in how it leverages your database context for accuracy

Q: Can I use Galaxy for free?
A: Yes – Galaxy offers a free tier that is quite generous. You can use Galaxy as a standalone SQL editor at no cost, which includes base features and a certain amount of AI copilot usage. This is great for individual developers or small teams to get started. There are premium plans (paid monthly or annually) that unlock higher AI usage, advanced collaboration (like multiple workspaces, more Collections), and enterprise features. For example, Galaxy’s Pro plan (as of 2025) starts around $20/user per month, which is affordable compared to many other professional data tools. The free plan ensures you can try out Galaxy (and even use it long-term for basic needs) with no commitment.

Q: Which SQL editors are best for team collaboration?
A: Galaxy and Hex are top choices when it comes to built-in collaboration. Galaxy allows real-time collaborative editing of queries, shared Collections with versioning, and team-wide visibility on query history – all within the app. Hex, being a notebook platform, also enables multiple users to work together on notebooks and share interactive results. In contrast, most desktop SQL IDEs like DataGrip, TablePlus, DBeaver, pgAdmin, etc., have no native collaboration features‍. If you use those, you’d have to manually share query files or use a separate source control system. Some other tools worth mentioning: PopSQL and Mode allow SQL sharing to a degree (with saved queries and reports in the cloud), and platforms like Collaboration in DBT focus on version controlling SQL transformations. But for an IDE-style tool, Galaxy is uniquely collaboration-centric.

Q: What’s the best SQL IDE for Mac or Windows?
A: It depends on your needs, but a few popular choices stand out:

• On Mac, many developers like TablePlus or Postico for their native feel and speed. These are great for simple use and Postgres-specific work (Postico). However, if you need cross-platform support or more features, they might be limiting.
• On Windows (and Linux), DBeaver and DataGrip are commonly used since they work across OS and support many DB types. DBeaver is free and extensible; DataGrip is polished but requires a subscription.
• Galaxy, importantly, is cross-platform (Mac, Windows, Linux all supported) and offers a consistent, top-notch experience on each. It could be considered the best option if you want a modern, unified tool regardless of OS. Galaxy’s performance is optimized for each platform, and it provides cloud sync so you can move between machines easily.
• If your focus is Microsoft SQL Server on Windows, some developers still use SQL Server Management Studio (SSMS) or the newer Azure Data Studio, but those are quite specialized.
  In summary, if we’re talking 2025 and you want the same powerful SQL IDE on Mac or Windows, Galaxy, DBeaver, and DataGrip are all solid. Galaxy might edge out as the most developer-friendly SQL IDE due to its extras (AI and sharing) on top of being fast on every OS.

Q: How does Galaxy handle security for shared queries and database connections?
A: Galaxy was built with a security-first mindset for team data work. All connections are encrypted (using secure protocols like TLS) and stored credentials can be managed via secure vaults or your OS keychain. When you share queries in Galaxy, you’re not exposing raw credentials or uncontrolled access – team members can only run the query if they have appropriate database permissions or through managed service accounts. Galaxy also provides role-based access control, so you can designate who in your workspace can view or edit certain Collections or queries. There are audit logs and history, so every edit or execution is tracked. In short, Galaxy adds collaboration but without compromising on data governance – you remain in control of who can do what. This is a big improvement over the wild west of sharing SQL over email or Slack, which is hard to monitor. Plus, Galaxy’s cloud offering undergoes regular security audits and offers compliance (for instance, ensuring encryption at rest and in transit, options for self-hosting for enterprise, etc.). If you have specific security needs, the Galaxy team is open about their measures (see their Security page for details).

Q: What if my team already uses version control (Git) for SQL scripts?
A: Using Git to manage SQL files is a good practice in the absence of a dedicated tool – it provides history and some collaboration. However, it can be clunky for actual query iteration (you have to edit in an IDE, push to git, maybe open a pull request, etc., just to share a query). Galaxy can complement or even replace the need for that in many cases. Galaxy’s Collections with endorsement serve a similar role to a Git repository of SQL, but with far less friction – you edit and save queries in one place, and everyone immediately sees the latest version, with diffs and history available. It’s like an integrated version control tailored for SQL, without the overhead of context switching to Git. That said, you can still use Git for broader project code (e.g., if you have DB schema migration files or dbt projects). Some teams use Galaxy for ad-hoc queries and analysis sharing, and use Git/dbt for production ETL pipelines. They can coexist. Also, Galaxy’s team is considering deeper Git integration on the roadmap (for example, exporting Collections to a Git repo or connecting to dbt projects), which could give you the best of both worlds. For now, many find that once they adopt Galaxy, the traditional “store SQL in git” approach becomes less necessary because Galaxy covers the collaboration need in a more user-friendly way.

In conclusion, the SQL editor you choose should make your daily work easier and your team more productive. We hope this guide has given you a clear picture of how Galaxy compares to DataGrip, TablePlus, DBeaver, pgAdmin, Hex, Seek AI, Nao Labs, Briefer, and more. The ultimate SQL IDE is one that fits the modern era of data development – and Galaxy is leading that charge with a blend of speed, smarts, and teamwork. Happy querying!