Price scraping API. Track every SKU. Block-free.
The API behind your own pricing-intelligence stack. Headless Chrome rendering, residential proxies, AI extraction for clean SKU / price / availability JSON — no per-domain scrapers, no per-GB billing, one credit pool.
Real free tier · failed requests cost 0 · credits depend on request settings
# Fetch a product page with JS rendering + rotating proxies.
$ curl 'https://api.scrapingant.com/v2/general' \
--data-urlencode 'x-api-key=YOUR_KEY' \
--data-urlencode 'url=https://www.amazon.com/dp/B0EXAMPLE' \
--data-urlencode 'browser=true'# Wrap inside your price-tracking pipeline.
import requests
r = requests.get("https://api.scrapingant.com/v2/general", params={
"x-api-key": "YOUR_KEY",
"url": PRODUCT_URL,
"browser": "true",
"proxy_country": "US",
})# Skip the parser — pass a JSON schema, get clean price data.
$ curl 'https://api.scrapingant.com/v2/extract' \
--data-urlencode 'x-api-key=YOUR_KEY' \
--data-urlencode 'url=https://store.example.com/sku/1234' \
--data-urlencode 'extract_properties=price,currency,availability,sku'
# → { "price": 24.99, "currency": "USD", "availability": "in_stock", "sku": "1234" }Why pricing teams pick us.
The infrastructure layer, not a finished repricer. Bring your own SKU list — we handle the messy parts.
Parser-free price JSON
AI extraction handles every storefront. Pass price,currency,sku — get JSON back.
/v2/extract → Built-in proxy pool
Fetch, JS rendering and proxy rotation share one API credit pool. Credits per request depend on rendering and proxy type.
How proxies work →Plan for your request mix
Business ($249/mo) includes 3M credits: a fetch-only budget for 10K rendered datacenter requests/day over 30 days, before retries, multiple fetches or AI extraction.
See pricing →One schema. Every storefront.
Bright Data ships 437 pre-built scrapers because each storefront needs its own template. ScrapingAnt's /v2/extract endpoint takes a property list — price,currency,availability,sku — and returns clean JSON from any e-commerce page. No template-per-domain maintenance; when a storefront ships new HTML the extraction layer adapts.
- Same call shape works on Amazon, Walmart, eBay, Shopify stores, niche marketplaces
- Returns numeric prices, not strings — ready for your diff/alert logic
- Falls through to
/v2/generalraw HTML when you want your own parser
Built-in proxy pool. No per-GB billing.
Fetch, browser rendering and proxy rotation draw from one API credit pool, with no separate bandwidth bill for API requests. A non-browser datacenter fetch costs 1 credit; JS rendering costs 10 credits with datacenter proxies or 125 with residential proxies. Start on datacenter for first-pass coverage; switch to proxy_type=residential where needed — the same key, with higher per-request credit use.
AI extraction adds ceil((Markdown input characters + output characters) / 30) credits on top of the fetch cost. See the full credit-cost breakdown.
- 2M+ residential IPs across 100+ countries — regional pricing capture works out of the box
- 100K+ datacenter IPs for high-volume low-block storefronts
- Failed requests cost 0 credits; successful retries and extra fetches consume credits
Daily snapshots. Idempotent responses.
The API returns deterministic JSON for the same URL — diff against your previous snapshot, write the delta. No event sourcing, no complex state. Most teams have a working daily price-history pipeline in under a day.
For planning, 3M credits over 30 days averages 100K non-browser datacenter requests/day, 10K rendered datacenter requests/day, or 800 rendered residential requests/day. These fetch-only examples exclude retries, multiple fetches per product and AI extraction; they are request budgets, not guaranteed product counts.
- JSON output stable across days when storefront HTML changes — AI extraction adapts
- Unlimited concurrency — push as many parallel requests as your worker pool handles
- Webhook-friendly: kick off a fetch from your scheduler, store the response, alert on diffs
Six pricing workloads teams build on top.
Same API, same credit pool — different ways of slicing the data underneath.
E-commerce price monitoring
Track SKU prices and stock across thousands of products daily. Amazon, Walmart, Shopify storefronts — same endpoint, same credit pool.
MAP enforcement
Catch resellers violating minimum advertised price agreements across a moving list of marketplaces — without paying enterprise per-domain fees.
Travel & airline fares
Hit fare-search results across airlines, OTAs, and hotel aggregators. Headless Chrome handles the dynamic price tables; residential proxies handle the geo-pricing.
Marketplace seller tracking
Monitor competitor sellers on Amazon, Walmart, eBay — buy-box ownership, price changes, inventory swings — at the SKU level.
Crypto & NFT floor price
Pull listing prices and 24h volume from OpenSea, Blur, marketplaces with cloudflare walls. Residential proxies bypass the bot detection.
Retail-media & ad-price tracking
Capture sponsored-product placements, ad slot prices, and bid-landscape data from retail-media platforms running on JS frameworks.
Pricing
Industry leading pricing that scales with your business.
|
Plans
|
Enthusiast
100K credits / mo
$19/mo
|
★ Most Popular
Startup
500K credits / mo
$49/mo
|
Business
3M credits / mo
$249/mo
|
Business Pro
8M credits / mo
$599/mo
|
Custom
10M+ credits / mo
$699+/mo
|
|---|---|---|---|---|---|
| Monthly API credits | 100,000 | 500,000 | 3,000,000 | 8,000,000 | 10M+ |
| Support channel | Priority email | Priority email | Priority email | Priority + dedicated | |
| Integration help | Docs only | Custom code snippets | Debug sessions | Priority debug sessions | Full enterprise onboarding |
| Expert assistance | — |  |
|
|
|
| Custom proxy pools | — | — | |
|
|
| Custom anti-bot avoidances | — | — | |
|
|
| Dedicated account manager | — | — | |
|
|
| Start Free | Start Free → | Start Free | Start Free | Talk to Sales |
What teams are saying.
From solo developers shipping side projects to enterprise pipelines at Fortune 500s.
★★★★★ 5.0 on Capterra →★★★★★“Onboarding and API integration was smooth and clear. Everything works great. The support was excellent.”
★★★★★“Great communication with co-founders helped me to get the job done. Great proxy diversity and good price.”
★★★★★“This product helps me to scale and extend my business. The setup is easy and support is really good.”
What is a price scraping API?
A price scraping API is a managed HTTP endpoint that fetches product pages from e-commerce storefronts and returns the raw HTML, structured JSON, or extracted price fields — so your pricing-intelligence pipeline doesn't have to maintain headless Chrome, proxy rotation, anti-bot bypass, or per-domain scraping templates. ScrapingAnt's /v2/general endpoint covers the full stack (browser + proxies + anti-bot) and the /v2/extract endpoint returns ready-to-store price/SKU/availability JSON without per-site parsers.
How is this different from a finished tool like Prisync or Competera?
Prisync and Competera are finished pricing-intelligence products — they store your competitor list, run the scrapes, draw dashboards, send alerts. ScrapingAnt is the data infrastructure that lets you build that yourself. You bring the URLs, the storage, the dashboard, the alerting; we hand back clean responses. Teams choose us when they want to own their pricing data pipeline (and pay infra rates, not per-domain SaaS fees) rather than rent a turn-key tool.
Can it handle Amazon, Walmart, and other anti-bot-heavy storefronts?
Yes. browser=true spins up headless Chrome for JS-heavy storefronts; proxy_type=residential swaps in 2M+ rotating residential IPs when datacenter ranges get blocked. Most teams start on datacenter for first-pass coverage and selectively upgrade to residential for the SKU pages that 403 — credits-only billing, no separate plan per proxy type.
How much does it cost to monitor 100,000 products daily?
Assuming one successful request per product per day over 30 days, 100,000 products require 3M credits with non-browser datacenter fetching, 30M credits with rendered datacenter requests, or 375M credits with rendered residential requests.
Business ($249/month) includes 3M credits. Over 30 days, that is a fetch-only budget averaging 100,000 non-browser datacenter requests/day (1 credit each), 10,000 rendered datacenter requests/day (10 credits each), or 800 rendered residential requests/day (125 credits each). These are request-budget examples, not guaranteed product counts; they exclude retries, multiple fetches per product, and AI extraction. Failed requests cost zero; successful retries and extra fetches consume credits.
AI extraction adds ceil((Markdown input characters + output characters) / 30) credits on top of the fetch cost. Check the full credit-cost breakdown and choose your request mix before sizing a plan.
Do you return structured price JSON or just raw HTML?
Both. /v2/general returns rendered HTML (your parser of choice). /v2/extract takes an extract_properties list — pass price,currency,availability,sku and get JSON back. /v2/markdown returns LLM-clean Markdown if you're feeding the page into an LLM downstream.
Can I monitor international storefronts with regional pricing?
Yes. proxy_country=DE routes through German IPs to see German-region prices; proxy_country=JP for Japan, and so on across 100+ countries via the residential pool. The same product URL returns different prices depending on the egress country — pricing-intel pipelines that ignore this consistently underestimate price spread.
How fast can I get a price-history pipeline running?
The minimal pipeline is: (1) sign up, (2) hit /v2/general?url=PRODUCT_URL&browser=true, (3) extract price from the response, (4) write to your DB with a timestamp. Most teams have a working diff-against-previous-snapshot job in a day. The API is idempotent and returns deterministic JSON, so the pipeline stays simple — no event sourcing, no complex state.
What happens when a storefront changes its HTML?
If you parse HTML yourself, your parser breaks. If you use /v2/extract, the AI extraction layer adapts — pass the same extract_properties list and it figures out the new layout. Teams moving off in-house scrapers cite this as the single biggest reason: less maintenance per supported storefront.
Tracking a high-volume SKU catalog?
Custom volume pricing, dedicated proxy pools, migration help from in-house scrapers, or a one-shot dataset to bootstrap your price-history table — drop us a line and a real human gets back within a few hours.
“Our clients are pleasantly surprised by the response speed of our team.”
