What is the cheapest API that works on Walmart?

Sort the ranked table by cost per successful request and read down to the first provider still clearing Walmart this run. Lower priced APIs further down often fail too many requests for their sticker price to be meaningful.

Best Web Scraping APIs for Walmart: July 2026 Benchmark

Name: walmart.com Web Scraping API Benchmark
Creator: Scrapeway

Benchmarks run by the Scrapeway team · Last updated: August 14, 2026 · How we benchmark

Scrapfly is the best web scraping API for Walmart, with a 99% success rate across 8 web scraping APIs benchmarked against live Walmart pages in July 2026. 3 of the 8 cleared Walmart reliably enough to recommend.

Walmart is protected by PerimeterX (HUMAN), so most scraping APIs either fail it or pay for it in speed and cost. The benchmark is refreshed twice a month, with no affiliate links and no sponsors.

Ranked by live Walmart success rate, best first:

🥇 Scrapfly: 99% success on Walmart
🥈 Scraperapi: 91% success on Walmart
🥉 Firecrawl: 91% success on Walmart

All 7 web scraping APIs for Walmart, ranked

#	Service	Success	Speed	Cost/1k		Code
1 🥇	Scrapfly	99%	6.3s	$3.89	(237) ★ 4.9	code
2 🥈	Scraperapi	91%	7.1s	$2.45	(62) ★ 4.6	code
3 🥉	Firecrawl	91%	4.9s	$7.21	n/a	code
4	WebScrapingAPI	90%	16.8s	$2.71	n/a	code
5	Scrapingant	89%	45.2s	$1.9	n/a	code
6	Scrapingdog	87%	6.7s	$1.0	n/a	code
7	Zenrows	73%	10.9s	$6.9	(103) ★ 4.8	code

Data range Jul 31 to Aug 14

Ranking history: web scraping APIs for Walmart over time

Walmart target ranking history

The 7 web scraping APIs for Walmart, reviewed

1. Scrapfly: 99% success on Walmart

On Walmart	Speed	Cost/1k	Overall	From
99%	6.3s	$3.89	#1 of 7	$30/mo

Walmart puts PerimeterX in front of its product, search, and store pages, and the data itself is tucked into a __NEXT_DATA__ JSON blob rather than the visible HTML. Scrapfly cleared 99% here by generating a genuine browser fingerprint and passing PerimeterX's sensor and cookie checks, so it reaches the rendered page and the embedded JSON instead of a challenge screen. Because it only bills for successful requests, the "Press & Hold" and block pages Walmart returns as a 200 don't quietly run up the cost.

At $3.89 per 1,000 successful requests and 6.3s average response time, the figures are strong for an API capable of clearing PerimeterX. The 99% success rate is the headline, and the ranking history above shows how that rate has held across previous runs.

Pros:

Highest success rate in the test, clearing PerimeterX's sensor and cookie checks
Only charges for successful scrapes, so challenge pages returned as 200s cost nothing
One asp flag plus residential proxies handles Walmart with little tuning
First class SDKs for Python, TypeScript, Go, and Rust, plus a Scrapy extension

Cons:

Credit cost per request rises once ASP, JavaScript rendering, or residential proxies are enabled
The entry (Discovery) plan caps concurrency at 5, so high volume catalog crawls need a higher tier
The free tier is a single batch of 1,000 credits, enough to prototype but not to benchmark at volume

2. Scraperapi: 91% success on Walmart

On Walmart	Speed	Cost/1k	Overall	From
91%	7.1s	$2.45	#2 of 7	$49/mo

Scraperapi is built around a fast request path. When it clears a request it tends to return quickly, which suits latency sensitive lookups that can absorb retries. It also ships a dedicated Walmart structured data endpoint. Walmart's PerimeterX layer has to be cleared before the __NEXT_DATA__ payload is reachable, and the scorecard carries how it did on that this run. On Walmart it cleared 91% this run.

Pros:

Fast when it clears, with a dedicated Walmart structured data endpoint
Broad language SDK support and simple integration

Cons:

Login required flows and form filling are off limits
Geotargeting is gated by plan (US and EU only until the Business tier)

3. Firecrawl: 91% success on Walmart

On Walmart	Speed	Cost/1k	Overall	From
91%	4.9s	$7.21	#3 of 7	$16/mo

Firecrawl's draw on Walmart is its output format. It returns structured markdown rather than raw HTML, so a pipeline feeding an LLM or RAG system skips the parsing step on Walmart's page markup. Rendering a full browser on every page and generating that markdown carries a cost and latency premium over lighter, HTTP based options, so that saving has to be worth the premium for your use case. On Walmart it cleared 91% this run.

Pros:

Returns markdown built for LLM pipelines, saving a parsing step for AI and RAG pipelines
Runs a real browser, which covers pages that need JavaScript execution

Cons:

Cost is the top user complaint in reviews
Renders a full browser on every page, which adds latency

4. WebScrapingAPI: 90% success on Walmart

On Walmart	Speed	Cost/1k	Overall	From
90%	16.8s	$2.71	#4 of 7	$19/mo

WebScrapingAPI covers a broad range of language SDKs behind a simple REST interface, so it drops into most stacks without a client library, and it supports async and batch submission for queued jobs. The scorecard carries this run's speed and cost. On Walmart it cleared 90% this run.

Pros:

Broad language SDK range behind a simple REST interface
Async and batch submission for queued jobs

Cons:

PerimeterX's block pages return as 200s, so it's easy to keep paying unless you check content
Support tickets often go unanswered for days, per user reviews

5. Scrapingant: 89% success on Walmart

On Walmart	Speed	Cost/1k	Overall	From
89%	45.2s	$1.90	#5 of 7	$19/mo

Scrapingant bundles JavaScript rendering and session support at a low entry price, with a smaller feature surface than the larger providers. On Walmart it cleared 89% this run.

Pros:

Low sticker price
JavaScript rendering included

Cons:

Smaller feature surface and fewer integration options than the larger providers
Billing covers requests that return block or challenge pages, so failures still cost credits
Small provider with a thin public track record

6. Scrapingdog: 87% success on Walmart

On Walmart	Speed	Cost/1k	Overall	From
87%	6.7s	$1.00	#6 of 7	$40/mo

Scrapingdog offers low entry pricing and a simple API, without first party SDKs. PerimeterX scores each request against its sensor and cookie signals before returning the page. On Walmart it cleared 87% this run.

Pros:

Low entry pricing
Simple API

Cons:

No first party SDKs
Reviewers frequently cite slow, email only support with no live chat option

7. Zenrows: 73% success on Walmart

On Walmart	Speed	Cost/1k	Overall	From
73%	10.9s	$6.90	#7 of 7	$69/mo

Zenrows is a general purpose scraping API with JavaScript rendering and session support, positioned as a generalist rather than a specialist on any single axis. It renders a real browser and supports sessions, and on Walmart it cleared 73% this run. It fits when you want one general purpose tool for Walmart plus other targets rather than optimizing hard for cost, speed, or maximum reliability.

Pros:

Real browser rendering and session support for targets that need JavaScript execution
One general purpose tool spans Walmart and other targets

Cons:

Cost climbs on heavy or large scale usage, the recurring user complaint
Premium proxy geographic coverage is unclear

COMPARISON

About scraping Walmart

Walmart.com is one of the largest US retail catalogs, and the data people scrape from it is mostly commercial. That means product titles, prices, availability and stock status, seller and fulfillment info, ratings and review counts, and search result and category rankings. Most of this lives on product detail pages (the /ip/{id} URLs), search pages, and store/aisle listings.

Walmart is a Next.js site, so the visible HTML is only part of the story. The reliable place to read data is the __NEXT_DATA__ JSON blob embedded in the page, which holds the structured product and search state in one object and arrives in the server response. Clearing the antibot layer matters more here than rendering, and once you have the HTML, the JSON is cleaner to parse than scraping individual DOM nodes. Our Walmart benchmark covers product pages, and search and store or aisle endpoints may behave differently.

HTTP ANALYSIS

Walmart is protected by PerimeterX (now HUMAN). See the PerimeterX benchmark page for how PerimeterX detects bots. The practical detail for scraping Walmart is that its blocks and "Press & Hold" challenges come back as a 200 rather than a hard error, so success has to be measured on response content, not status codes.

walmart_scraper.py

import json
from parsel import Selector

# install using `pip install scrapfly-sdk`
from scrapfly import ScrapflyClient, ScrapeConfig, ScrapeApiResponse

# create an API client instance
client = ScrapflyClient(key="YOUR API KEY")

# create scrape function that returns HTML parser for a given URL
def scrape(url: str, country: str="", render_js=False, headers: dict=None) -> Selector:
    api_result = client.scrape(ScrapeConfig(
            url=url,
            headers=headers,
            asp=True,
            render_js=render_js or False,
            cache=False,
            cache_ttl=900,
            method='GET',
    ))
    return api_result.selector

url = "https://www.walmart.com/ip/Apple-MacBook-Air-13-3-inch-Laptop-Space-Gray-M1-Chip-8GB-RAM-256GB-storage/609040889"
selector = scrape(url)

# Walmart is using NextJS framework so the product data is stored in a JSON variable
data = selector.xpath('//script[@id="__NEXT_DATA__"]/text()').get()
data = json.loads(data)
product = data["props"]["pageProps"]["initialData"]["data"]["product"]

# the resulting dataset is pretty big but here are some example fields:
from pprint import pprint
pprint(product)