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GoogleSQL
AI SQL workflows for BigQuery — with cost guardrails
Generate, validate, and govern GoogleSQL with schema-aware SQL, BigQuery dry-runs, admin review, and multilingual learning flows.
👋 Hey Product Hunt!
I'm the founder of GoogleSQL — and yes, the domain has a story.
I registered googlesql.com back in 2008, long before "GoogleSQL" was ever a product name. For years it ran as an enterprise SaaS marketplace. Then two things happened: Google unified its SQL dialect under the same name, and AI turned SQL into the interface between LLMs and enterprise data. Suddenly the domain I'd held for 17 years was sitting at the center of one of the fastest-moving spaces in data.
So I built the tool I always wanted as a BigQuery user.
Here's the problem: Text-to-SQL demos look magical. Then you put them in production and three things break:
🔸 The query is syntactically perfect but business-wrong
🔸 It touches tables it shouldn't
🔸 It quietly scans 4TB and your bill explodes
GoogleSQL fixes the boring-but-critical parts:
⚡ Schema-aware generation — it knows your tables, fields, and nested STRUCT/ARRAY models, so queries are grounded in your actual data
💰 Dry-run first, always — see estimated bytes, cost, and runtime before anything executes
✅ Governance built in — approval flows, audit logs, rollback, role-based access
🌍 13 languages — your team in Tokyo, Berlin, and São Paulo all get native UX
Who it's for: data teams on BigQuery who are tired of choosing between "move fast" and "don't torch the budget."
What I'd love from you:
Try breaking it — ask it something gnarly with nested arrays and window functions
Tell me what's missing for your workflow — governance features are prioritized by real requests
If cost control resonates, tell me your worst surprise-bill story in the comments 😅 (I'll share mine)
I'll be here all day answering every comment. Fire away! 🙌
About GoogleSQL on Product Hunt
“AI SQL workflows for BigQuery — with cost guardrails”
GoogleSQL was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #119 on the daily leaderboard. Generate, validate, and govern GoogleSQL with schema-aware SQL, BigQuery dry-runs, admin review, and multilingual learning flows.
On the analytics side, GoogleSQL competes within Developer Tools, Artificial Intelligence and Data & Analytics — topics that collectively have 994.2k followers on Product Hunt. The dashboard above tracks how GoogleSQL performed against the three products that launched closest to it on the same day.
Who hunted GoogleSQL?
GoogleSQL was hunted by Charles. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of GoogleSQL including community comment highlights and product details, visit the product overview.
Charles