The latest articles on DEV Community by Sunil Yadav (@pricedropy). https://dev.to/pricedropy https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4244958%2F858bc815-4fc5-45ca-9e4e-0c4763071fa4.png https://dev.to/pricedropy en Sunil Yadav Sat, 14 Nov 2026 18:54:11 +0000 https://dev.to/pricedropy/i-built-a-price-history-tracker-for-100-indian-online-stores-heres-what-i-learned-28ai https://dev.to/pricedropy/i-built-a-price-history-tracker-for-100-indian-online-stores-heres-what-i-learned-28ai <p>Every Indian sale season, the same thing happens: product pages light up with "70% OFF", and most shoppers have no way to tell if that's real. Often the discount is worked out from an inflated MRP, and the price you pay is the same as last month.</p> <p>So I built <strong>Price Dropy</strong> (<a href="https://pricedropy.com" rel="noopener noreferrer">pricedropy.com</a>): paste a product link from Amazon, Flipkart, Myntra, Nykaa or 100+ other Indian stores, and you see its full price history, with the all-time low, the usual price and today's price on one chart.</p> <h2> What it does </h2> <ul> <li>Price history charts for products across 100+ stores</li> <li>A "deal check" that compares today's price with the product's own history, not with the MRP</li> <li>Free price-drop alerts on email and Telegram</li> <li>A Chrome and Edge extension that shows the same history right on the store's product page</li> </ul> <h2> The stack </h2> <ul> <li> <strong>Next.js (App Router)</strong> for the site, rendered on the server so product pages are fast and crawlable</li> <li> <strong>PostgreSQL with TimescaleDB</strong> for the price time series, which keeps "lowest price in 90 days" style queries cheap</li> <li> <strong>Background workers</strong> that re-check prices on a schedule, with more frequent checks for products people actually watch</li> <li>A small <strong>Manifest V3 extension</strong> that talks to the same API</li> </ul> <h2> Lessons so far </h2> <ol> <li> <strong>The data is the product.</strong> A chart with three points is useless. Most of the work is keeping prices fresh: checking popular products often, and the long tail less often.</li> <li> <strong>MRP is not a baseline.</strong> Comparing with the product's own recent history (usual price, 90-day low) is far more honest than "X% off MRP", and it's the number people actually care about.</li> <li> <strong>Same product, many listings.</strong> One phone can have dozens of listings across stores and sellers. Matching them reliably is harder than scraping them.</li> <li> <strong>Don't let Google index everything.</strong> Early on we let millions of thin, near-identical product pages get indexed, and our search traffic collapsed in a single day. We now only let a curated set of genuinely useful pages be indexed, and keep the rest as <code>noindex</code>. Quality beats quantity.</li> <li> <strong>Festival weeks are the real test.</strong> Traffic and price changes both spike during sales, so caching and queueing matter much more than they do on a normal day.</li> </ol> <h2> What's next </h2> <p>Better cross-store matching ("same product, cheaper elsewhere"), an Android app, and more stores.</p> <p>If you shop online in India, I'd love feedback: <a href="https://pricedropy.com" rel="noopener noreferrer">pricedropy.com</a></p> webdev nextjs postgres sideprojects