Alder uses LLM-powered Agents to automatically optimize complex data warehouse queries. It builds a virtual runtime, finds bottlenecks, rewrites queries, evaluates improvements, and delivers the best plan—cutting manual tuning costs to zero.
Optimizing Data Warehouse Query Performance via AI Agents
Alder uses LLM-powered Agents to automatically optimize complex data warehouse queries. It builds a virtual runtime, finds bottlenecks, rewrites queries, evaluates improvements, and delivers the best plan—cutting manual tuning costs to zero.
Super impressive — love how you're using AI agents to tackle query optimization 🔍 Here to support your launch today!
Just spent the hours testing Alder and I'm genuinely impressed. As someone who regularly battles with slow data warehouse queries, this is a game-changer. The AI agent caught optimization opportunities I completely missed and rewrote my most problematic query, cutting execution time nearly in half! What I appreciate most is that it doesn't just hand you optimized code - it walks you through the reasoning behind each change, which has actually improved my SQL skills. The setup was surprisingly pa
⋆✦* We’re live! Autonomous query performance optimization — powered by AI Agents. 👋 Hey Product Hunt! We’re the team behind Alder , and we’re thrilled to launch a new kind of performance tuning platform — one built specifically for complex data warehouse workloads , and powered entirely by LLM Agents . ⋆✦* With Alder, you get: ✅ Fully autonomous query optimization—no manual tuning required ✅ AI Agent–built runtime simulates real query execution ✅ Bottleneck detection and root cause analysis ✅ Sm
Great product, it's very helpful and easy to use for query optimization.
It's easy to use, upload the minirepo and wait one minute. Then the query is optimized! Here is what I got: Optimization Summary: The query was optimized by applying a Common Table Expression (CTE) to calculate the average quantity only once, instead of executing a subquery multiple times. This change significantly reduced the execution cost and improved performance. Expected Performance Improved Ratio: 1543.99X Original Plan: The original query plan involved a Hash Join with a subquery that was
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