{"id":31796,"date":"2026-02-12T05:11:07","date_gmt":"2026-02-12T13:11:07","guid":{"rendered":"https:\/\/www.pingcap.com\/?p=31796"},"modified":"2026-03-10T02:02:58","modified_gmt":"2026-03-10T09:02:58","slug":"when-to-use-llm-reasoning-vs-vector-search","status":"publish","type":"post","link":"https:\/\/www.pingcap.com\/ko\/blog\/when-to-use-llm-reasoning-vs-vector-search\/","title":{"rendered":"LLM Reasoning vs. Vector Search: Lessons from Building CrowdSnap\u2019s Sentiment Analytics"},"content":{"rendered":"\n

CrowdSnap<\/em><\/a>, a TiDB Cloud user, builds an AI analytics platform where teams upload datasets\u2014like reviews or surveys\u2014and pull natural language insights: sentiment trends, correlations, turning messy data into sharp decisions.<\/em><\/p>\n\n\n\n

In shaping their Prompt Analysis feature, the team hit a familiar AI crossroads: Lean on direct LLM calls (GPT via Azure OpenAI) for on-demand sentiment reasoning, or go with a vector database for quick, repeatable retrieval?<\/em><\/p>\n\n\n\n

They put both to the test on real datasets (200-1,000 rows), measuring speed, costs, and accuracy. GPT edged out for single-shot runs (2-5s latency), but TiDB Vector Search dominated repeats (400x faster at 0.3s\/query, $0.11 vs. $10-15 per 1,000 analyses), crossing break-even at just 51 queries.<\/em><\/p>\n\n\n\n

Game-changer: A hybrid flow\u2014GPT for sharp, context-rich generation; TiDB for embedding storage (Cosine distance matching). For AI devs scaling up, it’s a smart playbook: Prototype with LLMs, then stack TiDB for cost-smart, high-replay performance.<\/em><\/p>\n\n\n\n

Enjoy the deep dive into the benchmarks, code snippets, and decision matrix\u2014discover how to choose (or combine) for your next AI build.<\/em><\/p>\n\n\n\n

<\/span>The Decision Every AI Builder Faces<\/span><\/h2>\n\n\n\n

When I started building CrowdSnap’s Prompt Analysis feature<\/a>, I hit a fork in the road that many AI developers encounter: should I use direct LLM calls for sentiment analysis, or invest in a vector database architecture?<\/p>\n\n\n\n

Instead of going with gut feeling, I built both approaches and ran them head-to-head with real datasets. What I discovered challenged some of my assumptions about performance, cost, and when each approach makes sense.<\/p>\n\n\n\n

This post shares the benchmarks, trade-offs, and lessons learned from implementing GPT-based direct analysis versus TiDB Vector Search for production sentiment analysis at scale.<\/p>\n\n\n\n

Spoiler: The answer wasn’t A or B, but how to combine them.<\/strong><\/p>\n\n\n\n

What We’re Building<\/h3>\n\n\n\n

CrowdSnap’s Prompt Analysis lets users upload any dataset (surveys, reviews, feedback) and ask questions in natural language to uncover insights, sentiment patterns, and correlations. Think of it as “ChatGPT for your data,” but with the architectural challenge of doing this efficiently for hundreds or thousands of data points.<\/p>\n\n\n\n

<\/span>Why This Comparison Matters<\/span><\/h2>\n\n\n\n

The AI landscape is full of “best practices” that aren’t always backed by real data. You’ll hear:<\/p>\n\n\n\n