[Blog](https://scrapfly.io/blog) / [ecommerce](https://scrapfly.io/blog/tag/ecommerce) / [How to Build a Grocery Price Comparison Tool with Python](https://scrapfly.io/blog/posts/how-to-build-a-grocery-price-comparison-tool-with-python) # How to Build a Grocery Price Comparison Tool with Python by [Hisham Medhat](https://scrapfly.io/blog/author/hisham) Jun 23, 2026 19 min read [\#ecommerce](https://scrapfly.io/blog/tag/ecommerce) [\#project](https://scrapfly.io/blog/tag/project) [\#python](https://scrapfly.io/blog/tag/python) [ ](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fscrapfly.io%2Fblog%2Fposts%2Fhow-to-build-a-grocery-price-comparison-tool-with-python "Share on LinkedIn") **MCP Cloud**Model Context Protocol cloud integration for AI-powered workflows. [ Learn More ](https://scrapfly.io/products/mcp-cloud) [ Docs ](https://scrapfly.io/docs/mcp/getting-started) Grocery price comparison is harder than typical ecommerce scraping. Prices change by zip code, every major grocery retailer runs enterprise-grade anti-bot systems, and the same product has different names across stores. A gallon of organic milk might cost $4.29 at one Walmart, $5.19 on Instacart, and $3.98 at Kroger. You won't know that without a tool that handles location-aware scraping, product matching, and unit price normalization. In this guide, we'll build a grocery price comparison tool in Python. It scrapes prices from Walmart, Instacart, and Kroger for a given zip code. We'll cover location-based pricing, cross-store product matching, and unit price comparison. The same pattern extends to European stores like Tesco and Carrefour. [How to Track Competitor Prices Using Web ScrapingIn this web scraping guide, we'll explain how to create a tool for tracking competitor prices using Python. It will scrape specific products from different providers, compare their prices and generate insights.](https://scrapfly.io/blog/posts/how-to-track-competitor-pricing-using-web-scraping) ## Key Takeaways Build a grocery price comparison tool with Python that scrapes location-aware prices from multiple stores and finds the cheapest option per product: - Scrape Walmart grocery prices by setting store location cookies that control all visible prices and stock levels - Handle Instacart's JS-rendered, delivery-zone pricing with browser automation and residential proxies - Use Kroger's free public API for structured price data (no scraping needed) - Match products across stores using a tiered strategy: UPC barcodes first, brand+name+size rules second, fuzzy matching as a fallback - Normalize unit prices ($/oz, $/lb) so you're comparing equal quantities, not raw shelf prices - Extend the same architecture to European stores like Tesco (UK) and Carrefour (France) by swapping postcodes, currencies, and metric units **Get web scraping tips in your inbox**Trusted by 100K+ developers and 30K+ enterprises. Unsubscribe anytime. ## Why Is Grocery Price Comparison Harder Than Regular Ecommerce Scraping? Grocery price comparison is harder than regular ecommerce scraping. It combines three challenges that generic scraping rarely faces: location-specific pricing, aggressive anti-bot protection, and cross-store product identity ambiguity. **Location-based pricing** makes grocery sites unique. A gallon of milk can cost $3.98 at one Walmart and $4.29 at another Walmart 20 miles away. Without setting a store location in your scraper's session, you get default or incomplete price data. **Anti-bot protection** on grocery sites is some of the toughest in ecommerce. Walmart runs [Akamai](https://scrapfly.io/blog/posts/how-to-bypass-akamai-anti-scraping) plus [PerimeterX (HUMAN)](https://scrapfly.io/blog/posts/how-to-bypass-perimeterx-human-anti-scraping), a rare dual-firewall setup. Instacart is fully JavaScript-rendered with session-based pricing. Each store needs a different strategy. **Product identity** is the messiest problem. "Organic Whole Milk 1 Gallon" at Walmart might appear as "Whole Milk Organic Gal" at Kroger and "Organic Milk, Whole, 128 fl oz" on Instacart. Without matching logic, you can't compare prices across stores at all. A Consumer Reports investigation found that Instacart runs AI pricing experiments that inflate prices for some users. That kind of price opacity is exactly why a comparison tool that checks multiple sources matters. ## What Will This Grocery Comparison Tool Build? This tool scrapes grocery prices from Walmart, Instacart, and Kroger for a given zip code. It normalizes products across stores and outputs a comparison showing the cheapest option per item. The architecture has three layers: store-specific scrapers, a normalization pipeline that matches products, and a comparison engine. Each store needs a different data access strategy. Here's how they compare: ### Tier 1: Clean data, production-ready fast | Store | Data Access | Why | |---|---|---| | Kroger (US) | Best | Free public API with OAuth2. Returns structured JSON: prices, availability, store locations. No scraping needed. | ### Tier 2: Structured data behind anti-bot walls | Store | Data Access | Why not Tier 1? | |---|---|---| | Walmart (US) | Good | Hidden JSON in `__NEXT_DATA__` gives structured product data, but dual Akamai + PerimeterX firewall and per-store location cookies add real complexity. | | Tesco (UK) | Good | Similar structured JSON approach, Cloudflare-protected. Postcode-based location system instead of zip codes. | ### Tier 3: Heavy lifting, highest payoff | Store | Data Access | Why not higher? | |---|---|---| | Instacart (US) | Hard | Fully JS-rendered, delivery-zone pricing, AI pricing experiments. Needs browser automation + residential proxies. But it's a marketplace, so one scrape gives you prices from Kroger, Costco, ALDI, and more. | | Carrefour (FR/ES) | Hard | DataDome anti-bot, multi-country sites, French/Spanish product names. Language-aware matching adds a normalization layer US stores don't need. | > **Worth noting:** Instacart is the hardest target but also the highest-value. As a marketplace aggregator, cracking it gives you cross-retailer pricing data (Kroger, Costco, ALDI, Safeway) for a single zip code. The data flows from scrapers through normalization (product matching + unit price conversion) into the comparison engine, which outputs the best price per product per store. The key design decision: Kroger has a public API, Walmart and Instacart need scraping. Build the system to handle both patterns with a common output schema. ### Project Setup and Dependencies Install the dependencies: shell```shell pip install httpx parsel scrapfly-sdk pandas thefuzz ``` We use httpx for HTTP requests, parsel for HTML parsing, and scrapfly-sdk for anti-bot bypass. Pandas handles comparison logic and thefuzz handles fuzzy product matching. ## How Do You Scrape Walmart Grocery Prices by Location? You scrape Walmart grocery prices by first setting a store location through session cookies, then extracting price data from Walmart's `__NEXT_DATA__` JSON. The technique is the same as [general Walmart scraping](https://scrapfly.io/blog/posts/how-to-scrape-walmartcom), but grocery adds location handling that most scrapers skip. ### How Does Walmart's Location-Based Pricing Work? Walmart ties all grocery prices and stock levels to a specific store. When you pick a store on Walmart's site, the browser sets cookies (`ACID`, `locDataV3`, `locGuestData`) that tell the server which store's prices to show. Without these cookies, product pages return default data that may not match any real store's prices. Prices and availability can differ between stores in the same city. A box of cereal might be $3.48 at one store and $3.98 at another 10 miles away. Your scraper needs a valid store ID to get accurate grocery prices. To find a store ID, visit the Walmart store finder page and search by zip code. The URL for each store contains its numeric ID (e.g., `/store/1234`). ### How to Set Up Location-Aware Walmart Scraping with Scrapfly Scrapfly's ASP handles Walmart's dual Akamai + PerimeterX firewall automatically. Pass your target zip code and store context through custom cookies: python```python from scrapfly import ScrapflyClient, ScrapeConfig scrapfly = ScrapflyClient(key="YOUR_SCRAPFLY_KEY") async def scrape_walmart_grocery(zip_code: str, store_id: str, search_term: str): """Scrape Walmart grocery prices for a specific store location.""" url = f"https://www.walmart.com/search?q={search_term}&cat_id=976759" # cat_id=976759 is the grocery department result = await scrapfly.async_scrape(ScrapeConfig( url=url, asp=True, # bypass Akamai + PerimeterX country="US", cookies={ "ACID": store_id, # store ID sets location "locGuestData": f'{{"zipCode":"{zip_code}"}}', }, )) return result ``` The `ACID` cookie ties the session to a specific Walmart store. The `locGuestData` cookie passes the zip code. Together, they make sure the returned prices match what a shopper at that store sees. Once you have the response, extract grocery-specific fields from the `__NEXT_DATA__` JSON. For the full parsing approach, see the Walmart scraping guide linked above. The grocery-specific fields you'll want are: python```python import json from parsel import Selector def parse_walmart_grocery(html: str) -> list[dict]: """Extract grocery product data from Walmart's __NEXT_DATA__ JSON.""" sel = Selector(html) # Walmart embeds structured data in a script tag next_data = sel.css("script#__NEXT_DATA__::text").get() if not next_data: return [] data = json.loads(next_data) # navigate to search results in the JSON structure items = data.get("props", {}).get("pageProps", {}).get("initialData", {}).get("searchResult", {}).get("itemStacks", [{}])[0].get("items", []) products = [] for item in items: if not item.get("name"): continue products.append({ "store": "walmart", "name": item.get("name", ""), "price": item.get("price", 0), "unit_price": item.get("pricePerUnit", {}).get("price", ""), "unit": item.get("pricePerUnit", {}).get("unit", ""), "brand": item.get("brand", ""), "size": item.get("shortDescription", ""), "upc": item.get("upc", ""), "available": item.get("availabilityStatusV2", {}).get("value", "") == "IN_STOCK", "image": item.get("image", ""), }) return products ``` The `pricePerUnit` field is grocery-specific. This field gives you the $/oz or $/lb price that you'll need for fair cross-store comparisons later. [How to Scrape Walmart.com Product Data (2026 Update)Tutorial on how to scrape walmart.com product and review data using Python. How to avoid blocking to web scrape data at scale and other tips.](https://scrapfly.io/blog/posts/how-to-scrape-walmartcom) Scrapfly #### Scale your web scraping effortlessly Scrapfly handles proxies, browsers, and anti-bot bypass — so you can focus on data. [Try Free →](https://scrapfly.io/register)## How Do You Scrape Instacart Prices Across Stores and Zip Codes? Instacart needs browser automation because JavaScript renders all its prices and they vary by delivery zone. You must simulate a real user session with a specific zip code to see accurate store-level pricing. ### Why Is Instacart the Hardest Grocery Site to Scrape? Instacart is the hardest grocery site to scrape for four reasons. First, the page is fully JS-rendered, so raw HTML contains no useful product data. Second, prices are delivery-area driven: entering a zip code loads prices specific to that zone. Third, the same product can show $0.40–$0.80 price differences between adjacent zip codes. Fourth, Instacart runs AI pricing experiments that can inflate prices for some users in the same delivery zone. The upside is that Instacart is a marketplace. One scrape gives you prices from multiple retailers (Kroger, Costco, ALDI, Safeway, and others) for a single zip code. That cross-retailer view is something no individual store scraper can provide. ### How to Handle Instacart's Variable Pricing with Browser Automation Scrapfly's browser rendering plus ASP handles Instacart's JavaScript and anti-bot protection in one API call: python```python async def scrape_instacart(zip_code: str, search_term: str): """Scrape Instacart prices with browser rendering for JS content.""" url = f"https://www.instacart.com/store/search/{search_term}" result = await scrapfly.async_scrape(ScrapeConfig( url=url, asp=True, render_js=True, # run a real browser to load JS content country="US", # set delivery zip code through the session cookies={"zipCode": zip_code}, rendering_wait=3000, # wait 3s for prices to load )) return result ``` The `render_js=True` flag tells Scrapfly to use a real browser. The `rendering_wait` gives the page time to load prices after the JavaScript runs. Without both, you get empty product cards. Since Instacart's layout changes often, avoid hardcoding CSS selectors. Instead, extract the key data points conceptually: - **Product name**: the main heading inside each product card - **Price**: the dollar amount tied to each product - **Store attribution**: which retailer (Kroger, Costco, etc.) is selling this item - **Unit size**: package size or weight listed below the product name Capture timestamps with every data point. Because Instacart adjusts prices algorithmically, the same product can show different prices between scraping sessions. The timestamp lets you track when a price was valid. For more on browser-based scraping, see our guide on [web scraping with Playwright](https://scrapfly.io/blog/posts/web-scraping-with-playwright-and-python). [Web Scraping with Playwright and PythonPlaywright is the new, big browser automation toolkit - can it be used for web scraping? In this introduction article, we'll take a look how can we use Playwright and Python to scrape dynamic websites.](https://scrapfly.io/blog/posts/web-scraping-with-playwright-and-python) ## How Do You Get Kroger Prices Using Their Public API? Unlike Walmart and Instacart, Kroger provides a free public API that returns structured product and price data. No scraping needed. You register for API credentials, authenticate with OAuth2, and query product prices by store location. ### How to Authenticate and Query the Kroger Product API Register at the [Kroger Developer Portal](https://developer.kroger.com) to get a client ID and secret. Then use the OAuth2 client credentials flow to get an access token: python```python import httpx import base64 KROGER_CLIENT_ID = "your_client_id" KROGER_CLIENT_SECRET = "your_client_secret" async def get_kroger_token() -> str: """Get an OAuth2 access token from the Kroger API.""" credentials = base64.b64encode( f"{KROGER_CLIENT_ID}:{KROGER_CLIENT_SECRET}".encode() ).decode() async with httpx.AsyncClient() as client: resp = await client.post( "https://api.kroger.com/v1/connect/oauth2/token", headers={"Authorization": f"Basic {credentials}"}, data={"grant_type": "client_credentials", "scope": "product.compact"}, ) return resp.json()["access_token"] async def find_kroger_stores(token: str, zip_code: str) -> list[dict]: """Find Kroger stores near a zip code.""" async with httpx.AsyncClient() as client: resp = await client.get( "https://api.kroger.com/v1/locations", headers={"Authorization": f"Bearer {token}"}, params={"filter.zipCode.near": zip_code, "filter.limit": 5}, ) return resp.json().get("data", []) async def search_kroger_products( token: str, store_id: str, search_term: str ) -> list[dict]: """Search for products at a specific Kroger store.""" async with httpx.AsyncClient() as client: resp = await client.get( "https://api.kroger.com/v1/products", headers={"Authorization": f"Bearer {token}"}, params={ "filter.term": search_term, "filter.locationId": store_id, "filter.limit": 20, }, ) products = [] for item in resp.json().get("data", []): price_info = item.get("items", [{}])[0].get("price", {}) size_info = item.get("items", [{}])[0].get("size", "") products.append({ "store": "kroger", "name": item.get("description", ""), "brand": item.get("brand", ""), "price": price_info.get("regular", 0), "sale_price": price_info.get("promo", 0), "upc": item.get("upc", ""), "size": size_info, "available": item.get("items", [{}])[0].get("fulfillment", {}).get("inStore", False), }) return products ``` The flow is: authenticate, find the nearest store by zip code, then search for products at that store. The API returns structured JSON with prices, UPC codes, and availability, so you don't need parsing or anti-bot bypass. Kroger's API is rate-limited, so keep requests reasonable. The `product.compact` scope gives you prices and basic product info without needing user authentication. Unlike Kroger, [Walmart's API](https://scrapfly.io/blog/posts/guide-to-walmart-api) doesn't return grocery prices, which is why we scrape Walmart instead. ## How Do You Match the Same Product Across Different Grocery Stores? Product matching across grocery stores uses a hierarchy of strategies. UPC/barcode lookup handles exact matches, brand+name+size rules cover most branded products, and fuzzy string matching acts as a fallback. Each match gets a confidence score so you can flag uncertain results. ### What Matching Strategy Works Best for Grocery Products? The best strategy is tiered. Try the most accurate method first, then fall back to fuzzier methods: - **Tier 1: UPC/GTIN barcode**: Same UPC means same product. The Kroger API returns UPC data. Walmart's `__NEXT_DATA__` sometimes includes GTIN values. When both stores expose a UPC for the same item, matching is trivial. - **Tier 2: Brand + product name + size**: Parse the brand, product type, and package size from each listing. If all three align, it's the same product. This handles most branded CPG items (Cheerios, Tide, Coca-Cola). - **Tier 3: Fuzzy string matching**: When Tier 1 and 2 fail, use token-based similarity scoring on the full product name. Set a minimum confidence threshold (85%) to avoid false matches. Tag each match with a confidence level. high (UPC), medium (brand+name+size), or low (fuzzy). Flag low-confidence matches for manual review. python```python from thefuzz import fuzz def match_products(source_products: list[dict], target_products: list[dict]) -> list[dict]: """Match products across two stores using a tiered strategy.""" matches = [] used_targets = set() for source in source_products: best_match = None best_confidence = "none" best_score = 0 for i, target in enumerate(target_products): if i in used_targets: continue # tier 1: exact UPC match if source.get("upc") and target.get("upc"): if source["upc"] == target["upc"]: best_match = target best_confidence = "high" best_score = 100 used_targets.add(i) break # tier 2: brand + name similarity + matching size if (source.get("brand") and target.get("brand") and source["brand"].lower() == target["brand"].lower()): name_score = fuzz.token_sort_ratio( source["name"].lower(), target["name"].lower() ) if name_score > 80 and _sizes_match(source.get("size", ""), target.get("size", "")): if name_score > best_score: best_match = target best_confidence = "medium" best_score = name_score # tier 3: fuzzy name match as fallback name_score = fuzz.token_sort_ratio( source["name"].lower(), target["name"].lower() ) if name_score >= 85 and name_score > best_score: best_match = target best_confidence = "low" best_score = name_score if best_match: matches.append({ "product": source["name"], "source_store": source["store"], "source_price": source["price"], "target_store": best_match["store"], "target_price": best_match["price"], "confidence": best_confidence, "score": best_score, }) return matches def _sizes_match(size_a: str, size_b: str) -> bool: """Check if two size strings refer to the same quantity.""" import re nums_a = re.findall(r"[\d.]+", size_a) nums_b = re.findall(r"[\d.]+", size_b) # if both have a number and the numbers match, sizes match if nums_a and nums_b: return nums_a[0] == nums_b[0] return False ``` The matching pipeline tries each tier in order for every source product. UPC matches short-circuit immediately, so there's no need to check further. Brand+name+size matches score well but need the size check to avoid matching a 32oz bottle against a 64oz bottle. Fuzzy matching catches products with different naming conventions. ### How to Normalize Unit Prices for Fair Comparison Shelf prices are misleading when package sizes differ. $3.99 for 64oz vs $5.49 for 128oz? The bigger bottle is cheaper per ounce, but the shelf price says otherwise. Always compare unit prices. python```python import re # conversion factors to normalize everything to a base unit UNIT_CONVERSIONS = { "oz": 1.0, "fl oz": 1.0, "lb": 16.0, # 1 lb = 16 oz "g": 0.03527, # 1 g = 0.035 oz "kg": 35.274, # 1 kg = 35.27 oz "ml": 0.03381, # 1 ml = 0.034 fl oz "l": 33.814, # 1 L = 33.81 fl oz "gal": 128.0, # 1 gallon = 128 fl oz "ct": 1.0, # count-based (per item) } def calculate_unit_price(price: float, size_str: str) -> dict: """ Parse a size string and calculate the unit price. Returns the price per oz (or per count for count-based items). """ if not size_str or price <= 0: return {"unit_price": None, "unit": None} # extract the number and unit from strings like "128 fl oz" or "1 gal" match = re.search(r"([\d.]+)\s*(fl oz|gal|oz|lb|kg|ml|ct|g|l)", size_str.lower()) if not match: return {"unit_price": None, "unit": None} quantity = float(match.group(1)) unit = match.group(2) # convert to oz (or count) using the conversion table oz_equivalent = quantity * UNIT_CONVERSIONS.get(unit, 1.0) if oz_equivalent <= 0: return {"unit_price": None, "unit": None} return { "unit_price": round(price / oz_equivalent, 4), "unit": "per_oz" if unit != "ct" else "per_ct", } ``` The function takes a price and a size string like "128 fl oz" or "1 gal" and returns the price per ounce. The conversion table handles the most common grocery units. For count-based items (a dozen eggs, a 6-pack), the function returns price per count. ## How Do You Compare Prices and Find the Best Deals? Once you've matched products and normalized prices, the comparison is the simplest part. Group by matched product, compare unit prices across stores, and output the cheapest option per item. python```python import pandas as pd def compare_grocery_prices(matched_products: list[dict]) -> pd.DataFrame: """Compare prices across stores and find the cheapest option per product.""" df = pd.DataFrame(matched_products) if df.empty: return df # find the cheapest store for each product cheapest = df.loc[df.groupby("product")["unit_price"].idxmin()] cheapest = cheapest.rename(columns={"store": "cheapest_store", "price": "cheapest_price"}) # build comparison table with all stores side by side comparison = df.pivot_table( index="product", columns="store", values=["price", "unit_price"], aggfunc="first", ) return comparison # example with sample data sample_data = [ {"product": "Organic Whole Milk 1 Gal", "store": "walmart", "price": 5.97, "unit_price": 0.0466}, {"product": "Organic Whole Milk 1 Gal", "store": "kroger", "price": 5.49, "unit_price": 0.0429}, {"product": "Organic Whole Milk 1 Gal", "store": "instacart", "price": 6.29, "unit_price": 0.0491}, {"product": "Cheerios 18oz", "store": "walmart", "price": 4.98, "unit_price": 0.2767}, {"product": "Cheerios 18oz", "store": "kroger", "price": 5.29, "unit_price": 0.2939}, {"product": "Cheerios 18oz", "store": "instacart", "price": 5.49, "unit_price": 0.3050}, {"product": "Dozen Large Eggs", "store": "walmart", "price": 3.12, "unit_price": 0.26}, {"product": "Dozen Large Eggs", "store": "kroger", "price": 2.99, "unit_price": 0.2492}, {"product": "Dozen Large Eggs", "store": "instacart", "price": 3.79, "unit_price": 0.3158}, ] comparison = compare_grocery_prices(sample_data) print(comparison) ``` The output shows every matched product with prices at each store and the cheapest option flagged. For the sample data above, Kroger wins on milk and eggs while Walmart wins on cereal. To track changes over time, run the comparison on a schedule. Our [price tracker guide](https://scrapfly.io/blog/posts/how-to-build-a-price-tracker-using-python-web-scraping) covers scheduling patterns and price change alerts. ## Can You Extend This Tool to European Grocery Stores? Yes. The same three-layer architecture (store scrapers, normalization, comparison) works for European grocery stores. You'll swap zip codes for postcodes, dollars for pounds or euros, and imperial for metric, but the pipeline design stays the same. Here's how the US and EU stores map: | Challenge | US (Walmart/Instacart/Kroger) | UK (Tesco) | France (Carrefour) | |---|---|---|---| | Location system | Zip code (5-digit) | Postcode (e.g., SW1A 1AA) | Code postal (5-digit) | | Currency | USD | GBP | EUR | | Unit system | Imperial (oz, lb) | Metric (g, kg, ml, L) with some imperial | Metric | | Dominant anti-bot | Akamai, PerimeterX | [Cloudflare](https://scrapfly.io/blog/posts/how-to-bypass-cloudflare-anti-scraping) | [DataDome](https://scrapfly.io/blog/posts/how-to-bypass-datadome-anti-scraping) | ### How Does Tesco Handle Location-Based Pricing in the UK? Tesco uses postcodes for delivery slot and pricing. The site is Cloudflare-protected and heavily JavaScript-rendered, similar to Instacart in that respect. Product data follows a `__NEXT_DATA__`-style JSON pattern, but with pence/GBP pricing and metric weights (grams, kilograms, millilitres). The adapter pattern makes adding Tesco straightforward. Write a new scraper config that sends a UK postcode instead of a zip code and parses GBP prices. Feed the results into the same normalization pipeline. The matching and comparison layers don't need to change. ### What Makes Scraping Carrefour Different from US Grocery Sites? Carrefour operates across France, Spain, Italy, and more. Each country runs a separate site with different anti-bot protection. French Carrefour uses DataDome, which is a different bypass strategy from Walmart's Akamai or Tesco's Cloudflare. The biggest new challenge isn't the scraping, it's the normalization. Product names are in French or Spanish. "Lait entier biologique 1L" is "Organic Whole Milk 1L" in your comparison table. Fuzzy matching needs language-aware tokenization, or you can add a translation layer before matching. Carrefour's click-and-collect service has store-specific pricing similar to Walmart's location model. ## How Does Scrapfly Help with Grocery Scraping at Scale? ScrapFly provides web scraping, screenshot, and extraction APIs for data collection at scale. For grocery price comparison, three features matter most: - **Anti-bot bypass ([ASP](https://scrapfly.io/docs/scrape-api/anti-scraping-protection))**: One API call handles Walmart's dual Akamai + PerimeterX firewall, Instacart's bot detection, and Tesco's Cloudflare, with no per-store bypass config needed. - **Browser rendering**: Built-in headless browsers for JS-heavy sites like Instacart. No need to run your own Playwright or Selenium farm. - **Rotating proxies**: Residential and datacenter proxy pools across 100+ countries. Scrapfly picks the right proxy type per request and handles rotation automatically. - **[Python SDK](https://scrapfly.io/docs/sdk/python)**: Drop-in client with async support. The same SDK call works for Walmart, Instacart, and Tesco (only the URL and country parameter change). Here's the same scraper pattern for two different stores: python```python # walmart grocery. US, anti-bot bypass walmart = await scrapfly.async_scrape(ScrapeConfig( url="https://www.walmart.com/search?q=organic+milk&cat_id=976759", asp=True, country="US", )) # tesco grocery. UK, anti-bot bypass + JS rendering tesco = await scrapfly.async_scrape(ScrapeConfig( url="https://www.tesco.com/groceries/en-GB/search?query=organic+milk", asp=True, render_js=True, country="GB", )) ``` The SDK call is the same. The ASP figures out which anti-bot system the target uses and handles the bypass. Kroger's public API doesn't need Scrapfly. Self-hosted browser automation also works for teams with DevOps capacity. Scrapfly is most valuable for the scraping layer. The product matching, normalization, and comparison logic run the same regardless of how you fetch the data. ### Power your scraping with Scrapfly Forget about getting blocked. Scrapfly handles anti-bot bypasses, browser rendering, and proxy rotation so you can focus on the data. [Try for FREE!](https://scrapfly.io/register) ## FAQ Are grocery store prices higher on Instacart than in-store?Yes, Instacart prices are typically higher than in-store prices. Retailers set separate pricing for Instacart's platform. A Consumer Reports investigation also found that Instacart runs AI pricing experiments that can inflate prices for some users. Is there a public API for grocery store prices?Kroger offers a free public API that returns product prices by store location. Walmart and Instacart don't provide public price APIs, so you need to scrape their platforms. Is Walmart cheaper than Kroger for groceries?It depends on the product and your location. Prices vary by zip code, and neither store is cheaper across all categories. That's exactly the question this comparison tool answers. How often do grocery store prices change online?Grocery prices can change daily, especially for sale items and promotions. Run your scraper at least once per day for usable comparisons. Instacart prices may change even more often due to algorithmic pricing adjustments. Can you scrape grocery store prices without getting blocked?Yes, but grocery sites run enterprise-grade anti-bot systems. Walmart uses Akamai plus PerimeterX, and Instacart detects automation through JS challenges. You need proper session handling, proxy rotation, and browser fingerprints, or a managed scraping API that handles this automatically. ## Summary The real challenge in grocery price comparison isn't the comparison itself, it's the data pipeline. Location-aware scraping, product matching, and unit price normalization are where the engineering effort goes. The three-layer architecture (store scrapers, normalization, comparison) works for any grocery market. Each store needs a different strategy: Walmart uses hidden JSON with location cookies, Instacart needs browser automation, and Kroger has a free public API. The pattern extends to Tesco, Carrefour, and any other grocery site by swapping the scraper config. For teams that want to skip the anti-bot complexity, Scrapfly's ASP handles Walmart's and Instacart's firewalls automatically through a single API. The [Python SDK](https://scrapfly.io) works for US and EU stores with the same code. Kroger's API doesn't need a scraping service, and self-hosted approaches work for teams with an existing setup. Table of Contents Table of Contents- [Key Takeaways](#key-takeaways) - [Why Is Grocery Price Comparison Harder Than Regular Ecommerce Scraping?](#why-is-grocery-price-comparison-harder-than-regular-ecommerce-scraping) - [What Will This Grocery Comparison Tool Build?](#what-will-this-grocery-comparison-tool-build) - [Tier 1: Clean data, production-ready fast](#tier-1-clean-data-production-ready-fast) - [Tier 2: Structured data behind anti-bot walls](#tier-2-structured-data-behind-anti-bot-walls) - [Tier 3: Heavy lifting, highest payoff](#tier-3-heavy-lifting-highest-payoff) - [Project Setup and Dependencies](#project-setup-and-dependencies) - [How Do You Scrape Walmart Grocery Prices by Location?](#how-do-you-scrape-walmart-grocery-prices-by-location) - [How Does Walmart's Location-Based Pricing Work?](#how-does-walmart-s-location-based-pricing-work) - [How to Set Up Location-Aware Walmart Scraping with Scrapfly](#how-to-set-up-location-aware-walmart-scraping-with-scrapfly) - [How Do You Scrape Instacart Prices Across Stores and Zip Codes?](#how-do-you-scrape-instacart-prices-across-stores-and-zip-codes) - [Why Is Instacart the Hardest Grocery Site to Scrape?](#why-is-instacart-the-hardest-grocery-site-to-scrape) - [How to Handle Instacart's Variable Pricing with Browser Automation](#how-to-handle-instacart-s-variable-pricing-with-browser-automation) - [How Do You Get Kroger Prices Using Their Public API?](#how-do-you-get-kroger-prices-using-their-public-api) - [How to Authenticate and Query the Kroger Product API](#how-to-authenticate-and-query-the-kroger-product-api) - [How Do You Match the Same Product Across Different Grocery Stores?](#how-do-you-match-the-same-product-across-different-grocery-stores) - [What Matching Strategy Works Best for Grocery Products?](#what-matching-strategy-works-best-for-grocery-products) - [How to Normalize Unit Prices for Fair Comparison](#how-to-normalize-unit-prices-for-fair-comparison) - [How Do You Compare Prices and Find the Best Deals?](#how-do-you-compare-prices-and-find-the-best-deals) - [Can You Extend This Tool to European Grocery Stores?](#can-you-extend-this-tool-to-european-grocery-stores) - [How Does Tesco Handle Location-Based Pricing in the UK?](#how-does-tesco-handle-location-based-pricing-in-the-uk) - [What Makes Scraping Carrefour Different from US Grocery Sites?](#what-makes-scraping-carrefour-different-from-us-grocery-sites) - [How Does Scrapfly Help with Grocery Scraping at Scale?](#how-does-scrapfly-help-with-grocery-scraping-at-scale) - [Power your scraping with Scrapfly](#power-your-scraping-with-scrapfly) - [FAQ](#faq) - [Summary](#summary) Join the Newsletter Get monthly web scraping insights Scale Your Web Scraping Anti-bot bypass, browser rendering, and rotating proxies, all in one API. 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