FluentDB

How are you thinking about prioritizing cross-platform vs. deepening the guardrails/safety features first? Curious whether early feedback like Brandon's is shifting your roadmap more than requests for new platforms at this stage.

The schema-only default with per-query approval is the right call — most AI database tools send sample rows to 'prove context' and that's the actual privacy leak, not the query. Two things I want to understand for local-first use: when talking to Ollama, does the schema introspection include column-level comments and check constraints, or just names and types? And for SQLite, is query execution going against the live file or does FluentDB open it in WAL mode with any read isolation to protect against a half-formed AI-generated write corrupting the journal?

Downloaded it and used it today really nice tool ! Feels fast and the UI is cleanBtw are you planning to support OLAP databases at some point (ClickHouse, DuckDB, BigQuery), or is the focus staying on OLTP for now?

Very interesting. I think the choice of native Swift is a big plus, especially compared to Electron-based solutions.

@rahul_ladumor good question, that's actually the scenario I'd worry about most - toggling auto-run for writes is exactly when you want the safety net most, not less. would be a strange design if convenience mode quietly removed the one check that matters for destructive queries.

the approval-by-default on write queries plus the read-only connection lock covers most of what I'd worry about. one thing I didn't see addressed - is there any kind of audit log of what got approved, so if something did go wrong (or you just want to know what the AI actually did to the db last week) you can look back at a history of generated queries and whether they were approved, edited, or rejected? feels like the kind of thing a team lead would want even if a solo user never touches it

Hi Kevin, Hoe does it gather the actual context for what the data means ? That has been the most diffucult thing for me, and would like to know about any plans for any lakehouse integrations ?

Nice! I am already using it everyday

Congrats team! I have been burned before by an AI tool that wanted way too much access to my data, so the approval by default thing is really reassuring. Curious about the Ollama support, does the experience feel as smooth with local models or is it noticeably better with the bigger providers?

Every AI generated SQL query requiring approval by default is a genuinely good default, but curious how far the approval flow actually helps in practice, since a query can be technically correct and still catastrophic in effect. An UPDATE without a WHERE clause or a query that touches ten thousand rows instead of the intended one can look completely reasonable to someone glancing at the SQL text if they aren't reading it carefully in the moment. Does FluentDB do anything beyond just showing the raw query, like flagging when a generated query lacks a WHERE clause on an UPDATE or DELETE, or surfacing an estimated row count before you approve, since that's usually the gap between reviewing SQL and actually catching the dangerous ones.

Also, models seeing your schema by default is a reasonable middle ground, but schema alone can still leak a fair amount, table and column names often describe exactly what kind of sensitive data lives there even without seeing a single row. Is there any way to selectively hide or rename specific tables and columns from what gets sent to the AI, for cases where even the schema shape is something a team wouldn't want leaving the machine.

a native Mac db client that isn't electron is already a plus in my book. curious how the AI part handles write queries specifically - does it always show you the generated SQL before running anything against the actual database, or can it execute directly if you ask it to "clean up duplicate rows" type stuff without a confirmation step?

Hey Product Hunt! 👋

I'm Kevin, the maker of FluentDB.

After years of working with databases every day, I kept running into the same frustrations: database clients felt slow, cluttered, and AI integrations either required sending data to third-party services or lacked proper safeguards.

So I built FluentDB: a native macOS database client that is fast, lightweight, and AI-first from the ground up.

A few things I'm particularly proud of:
• ⚡Native Swift app designed for speed.
• 🤖 Bring your own AI: OpenAI, Anthropic, Claude Code, Codex, or local models with Ollama.
• 🔒 Privacy by default: AI only sees your schema unless you explicitly approve sending data. Every AI-generated SQL query also requires approval by default.
• 🗄️ Support for PostgreSQL, MySQL, SQLite, SQL Server, with more databases coming soon.

FluentDB is still in its early days, and I'd genuinely love your feedback. Whether you're a DBA, backend engineer, or just curious about AI-powered developer tools, let me know what you think and what features you'd like to see next.

Thanks for checking it out! 🙌

PS: if you are looking for FluentDB on Linux or Windows, feel free to raise your voice, it will encourage us to accelerate the cross-platform port :)