Pepper Deals Scraper - mydealz, HotUKDeals & More

Scrape community deal listings from mydealz, HotUKDeals, Dealabs and Pepper.pl in bulk. Extract title, price, previous price, discount, temperature, merchant, voucher code, comments and link to CSV/JSON. No API key.

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What does the Pepper Scraper do?

This Actor turns any Pepper search into a structured dataset. You give it by site and feed or for another country, it walks the result pages one after another, and it writes one clean row per deal into your dataset — ready to export as JSON, CSV or Excel, or to pull straight from the Apify API.

The Pepper network powers mydealz, HotUKDeals, Dealabs and Pepper.pl from one platform, and every listing card carries its full record as JSON inside a data-vue3 attribute. The Actor reads that object rather than the rendered card, so prices, discounts and merchant details arrive already typed and one input switches country. Pagination is followed automatically until it runs out of results, hits your page limit or hits your Max items cap, whichever comes first. Every row is de-duplicated across the whole run, so you are never billed twice for the same deal.

There is no API key, no login and no browser involved. That keeps runs fast and cheap, and it means you can schedule the Actor without worrying about credentials expiring.

Who is it for?

• Price-intelligence teams tracking where products are genuinely discounted.
• Retail and brand managers watching how their products are promoted by the community.
• Affiliate and deal publishers sourcing live offers automatically.
• Market analysts comparing discount behaviour across Germany, the UK, France and Poland.
• Competitive-intelligence analysts monitoring rival merchants and voucher campaigns.

Use cases

• Export the hottest deals and rank them by community temperature.
• Track how often a merchant appears and at what discount depth.
• Compare the same product's promoted price across four national communities.
• Collect live voucher codes for a coupon feed.
• Build a discount-history dataset by scheduling repeated runs.

Why use this Pepper Scraper?

• 🔑 Keyless — no account, no API token, no cookies to paste.
• 📦 23 fields per deal — everything the result page exposes, already typed.
• 📄 Real pagination — it walks page after page instead of returning the first screen.
• 🎯 Precise caps — Max items stops the run exactly where you want it, so the bill is predictable.
• 📊 Export anywhere — JSON, CSV, Excel or HTML, plus the Apify API and integrations.
• 💸 Pay per result — you pay for rows you actually receive, with no platform fees to calculate.

What data can you extract?

Every run produces one row per deal, with these fields:

Field	Type	Description
dealId	string	Pepper thread ID
title	string	Deal title
dealUrl	string	Shareable link to the deal page
price	number	Deal price
previousPrice	number	Price the deal is compared against
discountPercent	number	Discount percentage when published
temperature	number	Community vote temperature
temperatureLevel	string	Heat label, e.g. Hot3
commentCount	number	Number of comments
merchantName	string	Merchant offering the deal
merchantId	string	Pepper merchant ID
voucherCode	string	Voucher code when the deal has one
shipping	string	Shipping cost or note as published
linkHost	string	Hostname the deal links out to
isExpired	boolean	Whether the deal has expired
isHot	boolean	Whether the community flagged it as hot
isNew	boolean	Whether the deal is newly posted
submittedBy	string	Username that posted the deal
imageUrl	string	Main deal image URL
publishedAt	string	ISO timestamp the deal was posted
site	string	Pepper site the row came from
page	number	Result page the deal appeared on
scrapedAt	string	ISO timestamp of extraction

Output example

{
  "commentCount": 81,
  "dealId": "2838825",
  "dealUrl": "https://www.mydealz.de/share-deal/2838825",
  "discountPercent": 0,
  "imageUrl": "threads/raw/giiGz",
  "isExpired": false,
  "isHot": true,
  "isNew": false,
  "linkHost": "www.aliexpress.com",
  "merchantId": "483",
  "merchantName": "AliExpress",
  "page": 1,
  "previousPrice": 55.47,
  "price": 45.78,
  "publishedAt": "2026-09-13T19:34:47.000Z",
  "scrapedAt": "2026-09-15T07:44:27.806Z",
  "shipping": "0",
  "site": "mydealz.de",
  "submittedBy": "JoergDampf",
  "temperature": 988.53,
  "temperatureLevel": "Hot3",
  "title": "(Aliexpress Elite) WLtoys / SUCHIYU SCY-16101PRO / SCY-16102 PRO, RC Auto 1/16, 2s brushless, 4WD 75 km/h",
  "voucherCode": "DEFS06"
}

How to use

Option A — by site and feed

{
  "maxItems": 300,
  "maxPagesPerSearch": 10,
  "section": "hot",
  "site": "mydealz.de"
}

1. Open the Actor and fill in the site and feed field.
2. Set Max pages per search and Max items to bound the run.
3. Click Start, then export from the Output tab.

Option B — for another country

{
  "maxItems": 300,
  "maxPagesPerSearch": 10,
  "section": "",
  "site": "hotukdeals.com"
}

Paste one or more Pepper URLs into Start URLs and the Actor paginates each of them independently.

Input parameters

Parameter	Type	Default	Description
site	string	mydealz.de	Which national Pepper community to read.
section	string	``	Which feed to walk.
maxPagesPerSearch	integer	4	How many result pages to walk for each search.
maxItems	integer	150	Stop after this many deals.
maxConcurrency	integer	2	Parallel requests.
proxyConfiguration	object	{"useApifyProxy": true}	Proxy used to fetch pages.

Tips for best results

• Pick the site that matches your market: mydealz.de, hotukdeals.com, dealabs.com or pepper.pl.
• Each page carries about 30 deals, so a 10-page run yields roughly 300 rows.
• temperature is the community's own ranking signal — sort by it rather than by date.
• voucherCode is empty for most deals; a value means a code is attached.
• previousPrice is zero when the poster did not supply a comparison price.
• Deals expire fast, so schedule a run every few hours if you are building a feed.
• Filter on merchantName after export to isolate one retailer's promotions.
• Currency follows the site: EUR for mydealz and Dealabs, GBP for HotUKDeals, PLN for Pepper.pl.
• Keep Max concurrency low — the platform rate-limits bursts.
• Pair with the Idealo or Amazon scrapers to check a promoted price against list price.

Integrations

Send results straight into the tools you already use: Google Sheets, Slack, Zapier, Make, Airtable or any Webhook. You can also schedule the Actor to run hourly, daily or weekly and have each run append to the same dataset, which is how you build a price or availability history rather than a one-off snapshot.

API usage

Run the Actor and collect results from any language. Replace <YOUR_TOKEN> with your Apify API token.

cURL

curl -X POST "https://api.apify.com/v2/acts/logiover~pepper-deals-scraper/run-sync-get-dataset-items?token=<YOUR_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"maxItems": 300, "maxPagesPerSearch": 10, "section": "hot", "site": "mydealz.de"}'

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_TOKEN>' });
const run = await client.actor('logiover/pepper-deals-scraper').call({"maxItems": 300, "maxPagesPerSearch": 10, "section": "hot", "site": "mydealz.de"});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

from apify_client import ApifyClient

client = ApifyClient('<YOUR_TOKEN>')
run = client.actor('logiover/pepper-deals-scraper').call(run_input={"maxItems": 300, "maxPagesPerSearch": 10, "section": "hot", "site": "mydealz.de"})
for item in client.dataset(run['defaultDatasetId']).iterate_items():
    print(item)

Use with AI agents (MCP)

This Actor is available through the Apify MCP server, so an AI agent can call it as a tool. Point your agent at https://mcp.apify.com and it can run the Pepper Scraper on demand — for example: "Pull the first 500 deals from Pepper and summarise the price distribution." The agent receives the same structured rows you would get from the UI.

FAQ

Do I need a Pepper account or API key?

No. The Actor reads publicly available pages only. There is nothing to authenticate and no credentials to rotate.

How many deals can I get in one run?

As many as the search exposes. Raise Max pages per search and Max items together; the run stops at whichever limit it reaches first.

Why did I get fewer rows than I asked for?

The search ran out of deals. That is normal for narrow queries — broaden the search or add more searches to one run.

Are results de-duplicated?

Yes. Each deal is emitted once per run, even when it appears on several pages, so you are never billed twice for the same record.

Why are some fields empty?

Pepper does not publish every attribute for every deal. Empty means the source did not show it, not that extraction failed.

What export formats are supported?

JSON, CSV, Excel, HTML and RSS from the Output tab, plus the Apify API and any integration you connect.

How fast is it?

It is pure HTTP with no browser, so a page of results typically takes a second or two. Raise Max concurrency carefully — the source rate-limits aggressive crawling.

Can I schedule it?

Yes. Use the Apify scheduler to run it on any interval and append each run to the same dataset for time-series analysis.

Does it work behind a proxy?

It uses Apify Proxy automatically. You can switch groups or supply your own proxies in Proxy configuration.

How often does the data change?

Pepper updates continuously. Re-run whenever you need current data; the Actor always reads the live pages, never a cache.

Is the output schema stable?

Yes. Field names and types are fixed, so downstream pipelines will not break between runs.

What if the site changes its layout?

Open an issue on the Issues tab and it gets fixed. The Actor is actively maintained.

Is it legal?

This Actor reads only publicly available pages on Pepper — the same content any visitor sees without logging in. It does not bypass authentication, does not collect private data and does not attempt to defeat access controls. You are responsible for how you use the output: respect the source's terms of service, applicable copyright, and data-protection law such as GDPR where personal data is involved. Scraping public data is generally lawful in the EU and the US, but the responsibility for the downstream use of that data sits with you.

Related scrapers

• Idealo Scraper — German price comparison
• Amazon Product Scraper — Retail pricing at scale
• Game Deals Scraper — Cross-store game pricing
• Kleinanzeigen Scraper — German classifieds