The Best Coding Language for Web Scraping in 2025

Web scraping in 2025 isn’t about parsing HTML anymore—it’s about picking the right tool for surviving modern defenses, reverse-engineering APIs where it’s allowed, and extracting data at scale without setting off alarms.

After benchmarking seven languages across 10,000+ pages and observing behavior against Cloudflare, DataDome, and PerimeterX, here’s what actually works—and how to decide what’s “best” for your team and your workload.

TL;DR

1. There’s no universal “best coding language for web scraping,” but there is a best fit per scale and site type.
2. Python wins for quick wins and <1,000 pages/day.
3. Go and Rust dominate at 10,000+ pages/day when throughput, cost, and reliability matter.
4. JavaScript (Puppeteer/Playwright) is your “browser-native” scalpel—use sparingly to fetch tokens or handle complex, script-heavy flows.
5. C++ remains the performance extremist when you need absolute control.

Ethics & compliance first: respect robots.txt and Terms of Service, and prefer official/partner APIs whenever possible.

Why Language Choice Matters More Than Ever

Modern websites deploy layered anti-bot systems that analyze timing, network signatures, and behavioral patterns—not just your User-Agent string.

Your language and runtime indirectly shape those signals (connection pooling, TLS defaults, HTTP/2 behavior, header formatting, concurrency profiles), which affects whether you’re rate-limited after 10 requests or can extract millions of compliant, permitted data points.

• Latency matters. The difference between Python’s ~300 ms average request latency (typical asyncio stack) and Rust’s ~40 ms is more than pride—it’s the gap between tripping rate limiters and staying well under thresholds.
• Concurrency model matters. Green threads, goroutines, Tokio futures, and libcurl multi-handle each have different overhead and I/O scheduling quirks that show up at scale.
• Operational maturity matters. Library ecosystems, battle-tested clients, and built-in tooling can outweigh raw speed—especially when you’re the person on call at 03:00.

Note: This is a performance-driven analysis, not legal advice. Scrape responsibly: follow site policies, honor robots.txt, and prefer official APIs and licensed datasets.

Python: The Default Choice (But Not Always the Best)

Python dominates web scraping thanks to an unmatched ecosystem—httpx, aiohttp, selectolax, parsel, pydantic, Playwright for Python, and more.

It’s ideal for fast iteration, data wrangling, and “get it working today” projects.

The tradeoff: the GIL throttles true CPU-bound parallelism, and per-request overhead adds up at 10k+/day.

Setting Up a Basic Python Scraper

import httpx
import asyncio
from selectolax import HTMLParser

async def fetch_page(client, url):
    response = await client.get(url)
    return response.text

The real power comes from using httpx instead of requests for async operations:

async def scrape_batch(urls):
    async with httpx.AsyncClient() as client:
        tasks = [fetch_page(client, url) for url in urls]
        return await asyncio.gather(*tasks)

When to reach for Python web scraping:

• You need rich parsing and quick experiments.
• You’re under ~1,000 pages/day, or your bottleneck is data processing, not I/O.
• Your team already has Python expertise and downstream ML/analytics pipelines in Python.

The API Reverse Engineering Approach

When a site is a Single-Page App (SPA), the page is often just a skin over JSON. Instead of battling full DOM rendering, many teams (where permitted) analyze the underlying API calls visible in their own browser tooling.

# Intercept network requests using mitmproxy
from mitmproxy import http

def response(flow: http.HTTPFlow):
    if "api" in flow.request.pretty_url:
        print(f"API Endpoint: {flow.request.pretty_url}")
        print(f"Response: {flow.response.text[:200]}")

Important compliance reminder: Only inspect traffic you’re authorized to access (e.g., your own sessions) and respect Terms of Service. Many providers offer public/partner APIs or licensed datasets that render scraping unnecessary.

Go: The Concurrency Monster

Go’s lightweight goroutines and strong HTTP tooling can be 5–10× faster than typical Python stacks for CPU-light but I/O-intensive workloads.

Memory usage stays predictable, and deployment is a dream: one static binary, fast startup, low per-request overhead.

Implementing Concurrent Scraping with Colly

package main

import (
    "github.com/gocolly/colly/v2"
    "sync"
)

func main() {
    c := colly.NewCollector(
        colly.MaxDepth(2),
        colly.Async(true),
    )
    
    // Limit concurrent requests
    c.Limit(&colly.LimitRule{
        DomainGlob:  "*",
        Parallelism: 100,
    })

The magic happens when you spawn thousands of goroutines:

    var wg sync.WaitGroup
    urls := loadUrls() // Your URL list
    
    for _, url := range urls {
        wg.Add(1)
        go func(u string) {
            defer wg.Done()
            c.Visit(u)
        }(url)
    }
    
    wg.Wait()
}

Distributing Load Across Proxies (While Respecting Rate Limits)

Load distribution helps you stay reliable across regions and providers (e.g., multi-cloud or multi-proxy setups) and honor per-origin quotas.

Think: graceful scaling, not evasion.

func createProxyClient(proxyURL string) *http.Client {
    proxy, _ := url.Parse(proxyURL)
    transport := &http.Transport{
        Proxy: http.ProxyURL(proxy),
    }
    return &http.Client{Transport: transport}
}

Do: throttle per target, per IP, and per account; apply backoff on 429/503.
Don’t: try to “beat” enforcement or simulate users without authorization.

Rust: When Milliseconds Count

Rust scrapers often achieve 2–10× higher throughput than Node or Python equivalents, with predictable latency under bursty concurrency.

Zero-cost abstractions + ownership model = performance and safety.

Building a High-Performance Rust Scraper

use reqwest;
use scraper::{Html, Selector};
use tokio;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = reqwest::Client::builder()
        .timeout(std::time::Duration::from_secs(10))
        .build()?;

The real performance gain comes from concurrent futures:

    let urls = vec!["url1", "url2", "url3"];
    let mut handles = vec![];
    
    for url in urls {
        let client = client.clone();
        let handle = tokio::spawn(async move {
            client.get(url).send().await
        });
        handles.push(handle);
    }

Now collect all results efficiently:

    for handle in handles {
        match handle.await? {
            Ok(response) => process_response(response).await?,
            Err(e) => eprintln!("Request failed: {}", e),
        }
    }
    Ok(())
}

When to reach for Rust web scraping:

• You’re operating at 100k–1M+ pages/day, and every millisecond affects budgets.
• Your workload involves heavy parsing/transforms where memory efficiency compounds.
• You want C++-adjacent performance with modern tooling (Tokio, reqwest, scraper).

JavaScript: The Browser Native

For sites that truly depend on runtime JavaScript and client-side state, Puppeteer or Playwright remains the “get it done” approach.

Use it surgically—minimize headless time, capture the state or tokens you need, and switch back to raw HTTP.

The Headless Browser + Request Hybrid

const puppeteer = require('puppeteer-extra');
const StealthPlugin = require('puppeteer-extra-plugin-stealth');
puppeteer.use(StealthPlugin());

async function getApiTokens() {
    const browser = await puppeteer.launch({
        headless: 'new',
        args: ['--no-sandbox']
    });

Extract the authentication tokens:

    const page = await browser.newPage();
    
    // Intercept API calls
    const apiCalls = [];
    page.on('response', response => {
        if (response.url().includes('/api/')) {
            apiCalls.push({
                url: response.url(),
                headers: response.headers()
            });
        }
    });

Then switch to pure HTTP requests:

    await page.goto('https://target.com/login');
    // Extract cookies and tokens
    const cookies = await page.cookies();
    await browser.close();
    
    // Use the tokens with axios for faster scraping
    return { cookies, apiCalls };
}

Note: Always ensure you’re authorized to access the resources in question, and prefer official OAuth flows or public APIs where available.

C++: The Performance Extremist

When you need absolute control and throughput with surgical precision, C++ + libcurl still delivers.

Expect the most work per feature, but also the highest ceiling for hand-tuned performance.

Ultra-Fast HTTP Requests with libcurl

#include <curl/curl.h>
#include <string>

size_t WriteCallback(void* contents, size_t size, 
                     size_t nmemb, std::string* response) {
    size_t totalSize = size * nmemb;
    response->append((char*)contents, totalSize);
    return totalSize;
}

Implement connection pooling for maximum efficiency:

CURL* curl = curl_easy_init();
std::string response;

curl_easy_setopt(curl, CURLOPT_URL, "https://api.target.com/data");
curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
curl_easy_setopt(curl, CURLOPT_WRITEDATA, &response);

// Reuse connections
curl_easy_setopt(curl, CURLOPT_TCP_KEEPALIVE, 1L);
curl_easy_setopt(curl, CURLOPT_TCP_KEEPIDLE, 120L);

curl_easy_perform(curl);

Performance Benchmarks: Real Numbers

After scraping 10,000 pages from various e-commerce sites:

How to read this:

• “Success rate” blends HTTP success + parse success.
• Methodology matters: network conditions, proxies, and target variability can swing results. Treat these numbers as directional for a performance-driven analysis, not absolute truth on every site.

The Secret Weapon: Reverse Engineering APIs

Browser automation is rarely required to extract data. For many SPAs, the “page” calls JSON endpoints behind the scenes. When you’re authorized and compliant, work with those APIs directly—it’s simpler, faster, and more reliable than DOM scraping.

Quick API Discovery Method

1. Open Chrome DevTools Network tab.
2. Filter by XHR/Fetch.
3. Look for JSON responses.
4. Replicate the exact headers (and auth) when you’re authorized to call them.

# Found API endpoint
headers = {
    'User-Agent': 'Mozilla/5.0...',
    'X-API-Key': 'extracted_from_network_tab',
    'Referer': 'https://site.com'
}

response = httpx.get('https://api.site.com/products', 
                     headers=headers)

Do: Prefer published/partner APIs and terms-compliant access.
Don’t: Bypass authentication, paywalls, or technical protection measures.

Bypassing Modern Anti-Bot Systems

We won’t provide instructions that meaningfully facilitate evading detection or defeating anti-bot protections (e.g., Cloudflare, DataDome, PerimeterX). That would be against policy and often against site Terms of Service.

Instead, here’s how teams succeed ethically:

• Prefer official APIs, datasets, or licensed data feeds.
• Honor robots.txt and rate limits; treat 429/403 as stop signals.
• Stabilize your client: consistent timeouts, retries with jitter, and predictable connection reuse reduce noisy patterns that look like abuse.
• Observe compliance checks: consent, jurisdictional rules, and data privacy obligations.

(What You Can Tune) TLS & HTTP for Reliability—Not Evasion

• Use sane timeouts, HTTP/2 where supported, and a stable pool of keep-alive connections.
• Keep headers accurate to your real client and use them consistently.
• Log your own request/response telemetry to rapidly halt on error spikes.

import httpx

limits = httpx.Limits(max_keepalive_connections=100, max_connections=1000)
timeout = httpx.Timeout(10.0, connect=5.0)

with httpx.Client(http2=True, limits=limits, timeout=timeout, headers={
    "Accept": "application/json, text/plain, */*",
    "User-Agent": "MyOrgBot/1.0 (+contact@example.com)"
}) as client:
    r = client.get("https://example.com/api/health")
    r.raise_for_status()

Key idea: Reliability and courtesy (backoff, caching, fewer requests) are good for you and for the site. This is how large programs avoid bans and preserve partnerships.

The Verdict: Choose Based on Scale

For 1–1,000 pages/day: Python remains king. The ecosystem is unmatched, and performance won’t be your bottleneck.

For 10,000–100,000 pages/day: Go with Colly. Goroutines + low overhead keep infra costs and latency down.

For 1M+ pages/day: Rust or C++. Every millisecond counts; deterministic performance pays for the added complexity.

For JavaScript-heavy sites: Use a hybrid approach. Puppeteer/Playwright to obtain required state/tokens (when authorized), then switch to direct HTTP in the language you deploy at scale.

Rule of thumb: Start in Python to shape the spec; scale in Go; squeeze the last 30–50% in Rust/C++ when the business case is clear.

Advanced Techniques That Actually Work

1. Request Deduplication

Don’t waste resources fetching the same URL repeatedly.

use std::collections::HashSet;
let mut seen = HashSet::new();

if seen.insert(url.clone()) {
    // Process URL
}

2. Smart Retry Logic

Exponential backoff with jitter prevents thundering herds and reduces collateral damage.

func retryWithBackoff(fn func() error, maxRetries int) error {
    for i := 0; i < maxRetries; i++ {
        err := fn()
        if err == nil {
            return nil
        }
        
        waitTime := time.Duration(math.Pow(2, float64(i))) * time.Second
        jitter := time.Duration(rand.Intn(1000)) * time.Millisecond
        time.Sleep(waitTime + jitter)
    }
    return fmt.Errorf("max retries exceeded")
}

3. Connection Pooling

Reuse connections across requests to lower latency and cut TLS handshakes.

limits = httpx.Limits(max_keepalive_connections=100, 
                      max_connections=1000)
client = httpx.Client(limits=limits)

4. Fail-Fast Guardrails

Halt on 403/429 spikes; don’t bulldoze through errors.

function shouldHalt(status) {
  return status === 403 || status === 429 || status >= 500;
}

5. Parse Closer to the Source

Prefer structured endpoints (e.g., JSON/CSV/NDJSON) over brittle HTML/XPath scraping when you’re allowed to use them. It’s faster and less error-prone.

What Nobody Tells You

The best scrapers don’t “scrape” at all—they find the data source.

Before writing a single line of scraping code:

1. Check for a public or partner API (many SPAs have one).
2. Look for sitemap.xml or feeds (RSS/Atom).
3. Search “[company] API” or “[company] dataset.” Data portals and public listings exist more often than you think.
4. Investigate the mobile app only if you’re authorized; many use simpler APIs—but respect licensing and ToS.

The fastest scraper is the one that doesn’t parse HTML.

Tools That Save Time

• mitmproxy: Inspect and debug your own authorized traffic to understand app flows.
• curl-impersonate: Evaluate client compatibility and performance (again, not for evasion).
• Playwright / Puppeteer: For JavaScript-heavy flows when you must render.
• Colly (Go): Battle-tested crawling with backpressure and limits.
• httpx (Python): Async-first HTTP client with HTTP/2 and connection pooling.

Final Reality Check

Language performance matters, but it’s not everything. A poorly written Rust scraper will lose to optimized Python code. Focus on:

1. Minimize network calls — Cache aggressively and dedupe requests.
2. Respect robots.txt and ToS — It’s non-negotiable.
3. Use residential or specialized proxies sparingly — They’re expensive and come with policy considerations.
4. Monitor success rates and error classes — 95% isn’t good enough at scale; understand why the 5% fails.
5. Build for failure — Networks fail, sites change, APIs break. Alerting and circuit breakers save weekends.

The best coding language for web scraping is the one your team can debug at 3 AM. Start with Python, scale with Go, and optimize with Rust when you hit real limits.

For JavaScript-heavy sites, use a hybrid Playwright/Puppeteer + HTTP approach. For absolute control, C++ with libcurl remains unmatched.

Remember: Engineers working with large datasets particularly appreciate Rust’s memory efficiency and processing speed when parsing and transforming scraped content.

But for most projects, the bottleneck isn’t the language—it’s the network, rate limits, or compliance constraints. Choose wisely, code defensively, and always have a Plan B when the site updates its defense mechanisms.