Best Go scraping libraries 2026 ranked

Best Go scraping libraries in 2026 occupy a niche that is small but unusually high-leverage. Go’s concurrency model (goroutines and channels) maps almost perfectly to the scraping problem, and Go’s compiled binary makes deployment dramatically simpler than Python or Node alternatives. The downside is library breadth: the Go scraping ecosystem has fewer options than Python’s, and the existing libraries are less actively maintained on average. For specific workloads (high-throughput HTTP scraping, distributed crawler workers, scraping infrastructure embedded in Go services), Go is the right choice and the libraries that exist are excellent. For one-off scrapers or projects that benefit from a rich ecosystem, Python or Node remain easier.

This guide ranks the Go scraping libraries actually worth using in 2026, with honest performance comparisons, clear use case mapping, and the gotchas specific to Go’s approach.

Why Go for scraping

Three reasons Go is interesting for scrapers:

Concurrency: a goroutine costs about 2 KB of stack memory. You can run 10,000+ concurrent goroutines on a modest server. Compared to Python’s coroutine overhead and Node’s event loop limits, Go’s concurrency is genuinely different in scale.

Compile-once deploy-anywhere: a Go binary is a single static file. Deployment to a new server or container is scp and ./scraper. No virtualenv, no node_modules, no version drift between dev and prod.

HTTP performance: Go’s net/http standard library is fast enough that “scraping” and “high-performance HTTP service” use the same toolkit. fasthttp pushes performance even further for extreme throughput needs.

Three reasons Go is sometimes wrong:

Smaller library ecosystem: fewer parsers, fewer pre-built scrapers, less community content.

No native browser automation: Chromedp and Rod are good but not as polished as Playwright in Python or JavaScript.

Verbose for one-offs: Python’s requests two-line scraper has no clean Go equivalent.

HTTP clients

net/http (standard library)

The standard library client. Production-grade, well-documented, fast. Right choice for most scraping HTTP needs.

package main

import (
    "io"
    "net/http"
    "time"
)

func fetch(url string) ([]byte, error) {
    client := &http.Client{Timeout: 10 * time.Second}
    req, err := http.NewRequest("GET", url, nil)
    if err != nil {
        return nil, err
    }
    req.Header.Set("User-Agent", "Mozilla/5.0 ...")
    resp, err := client.Do(req)
    if err != nil {
        return nil, err
    }
    defer resp.Body.Close()
    return io.ReadAll(resp.Body)
}

Best for: most Go HTTP work. Default unless you have specific needs.

fasthttp

Aggressive performance-oriented HTTP library that bypasses some net/http abstractions for raw speed. 5-10x faster than net/http on benchmarks. The API is different (uses fasthttp.Request and fasthttp.Response instead of net/http types).

import "github.com/valyala/fasthttp"

func fetchFast(url string) ([]byte, error) {
    req := fasthttp.AcquireRequest()
    resp := fasthttp.AcquireResponse()
    defer fasthttp.ReleaseRequest(req)
    defer fasthttp.ReleaseResponse(resp)

    req.SetRequestURI(url)
    req.Header.SetUserAgent("Mozilla/5.0 ...")
    if err := fasthttp.Do(req, resp); err != nil {
        return nil, err
    }
    return resp.Body(), nil
}

Best for: extreme throughput needs (10k+ requests/sec), low-latency requirements.

resty

The popular convenience HTTP client wrapping net/http with a nicer API. Fluent builder pattern, JSON serialization, retry support. Slightly slower than raw net/http but more readable.

import "github.com/go-resty/resty/v2"

client := resty.New().SetTimeout(10 * time.Second)
resp, err := client.R().
    SetHeader("User-Agent", "Mozilla/5.0").
    Get("https://example.com")

Best for: developer ergonomics, projects that benefit from convenience over absolute performance.

HTML parsers

GoQuery

The jQuery-style HTML parser. Cleanest API for Go HTML manipulation. Built on golang.org/x/net/html under the hood.

import (
    "github.com/PuerkitoBio/goquery"
    "strings"
)

func parseTitles(html string) []string {
    doc, err := goquery.NewDocumentFromReader(strings.NewReader(html))
    if err != nil {
        return nil
    }
    var titles []string
    doc.Find("h2.product-title").Each(func(i int, s *goquery.Selection) {
        titles = append(titles, s.Text())
    })
    return titles
}

Best for: most Go HTML parsing. Default choice.

golang.org/x/net/html

The standard parser GoQuery wraps. Direct use is verbose but available for custom AST manipulation.

Best for: low-level parsing needs, when you want zero dependencies.

colly’s parser

Colly framework includes its own HTML traversal which is less verbose than GoQuery for callback-driven scraping. Used in conjunction with Colly only.

Browser automation

Chromedp

The dominant Go browser automation library. Uses Chrome DevTools Protocol directly without intermediate libraries. Fast, well-maintained, but the API is verbose compared to Playwright.

import (
    "context"
    "github.com/chromedp/chromedp"
    "time"
)

func scrapeWithChrome(url string) (string, error) {
    ctx, cancel := chromedp.NewContext(context.Background())
    defer cancel()
    ctx, cancel = context.WithTimeout(ctx, 30*time.Second)
    defer cancel()

    var title string
    err := chromedp.Run(ctx,
        chromedp.Navigate(url),
        chromedp.WaitVisible("h1.product-title"),
        chromedp.Text("h1.product-title", &title),
    )
    return title, err
}

Best for: most Go browser automation. The default choice when you need a real browser.

Rod

A modern alternative to Chromedp with a more fluent API. Active development, strong feature parity with Playwright.

import "github.com/go-rod/rod"

browser := rod.New().MustConnect()
page := browser.MustPage("https://example.com").MustWaitLoad()
title := page.MustElement("h1.product-title").MustText()

Best for: developers who prefer Rod’s API ergonomics over Chromedp’s.

Playwright-go

The Microsoft Playwright API for Go. Newer and less mature than Chromedp/Rod but offers cross-browser (Firefox, WebKit) support that the Chrome-only alternatives lack.

Best for: cross-browser needs in Go, teams using Playwright in other languages.

Frameworks

Colly

The dominant Go scraping framework. Built-in caching, concurrency, request rate limiting, and HTML parsing callbacks. The right choice for crawler-heavy Go scrapers.

import "github.com/gocolly/colly/v2"

c := colly.NewCollector(
    colly.AllowedDomains("example.com"),
    colly.Async(true),
)
c.Limit(&colly.LimitRule{
    DomainGlob:  "*",
    Parallelism: 10,
    Delay:       100 * time.Millisecond,
})

c.OnHTML("div.product", func(e *colly.HTMLElement) {
    fmt.Println(e.ChildText("h2.title"))
})

c.OnHTML("a.next", func(e *colly.HTMLElement) {
    e.Request.Visit(e.Attr("href"))
})

c.Visit("https://shop.example.com/page/1")
c.Wait()

Best for: large crawlers, the standard Go scraping framework.

Geziyor

Another Go scraping framework with similar feature set to Colly. Less popular but actively maintained.

Best for: Colly alternatives.

Comparison table

library	layer	speed	learning curve	best for
net/http	HTTP	fast	easy	most HTTP work
fasthttp	HTTP	fastest	medium	extreme throughput
resty	HTTP	fast	easy	developer ergonomics
GoQuery	parser	fast	easy	most HTML parsing
golang.org/x/net/html	parser	fast	hard	custom AST work
Chromedp	browser	mid	medium	most browser automation
Rod	browser	mid	medium	Chromedp alternative
Playwright-go	browser	mid	medium	cross-browser
Colly	framework	fast	medium	most crawler work
Geziyor	framework	fast	medium	Colly alternative

Decision matrix: solopreneur, SMB, enterprise

profile	scale	recommended stack	reasoning
Solopreneur Go-curious	<10k pages/day	net/http + GoQuery	Standard library + the one parser
Indie scraper, single binary	<500k pages/day	net/http + GoQuery + Colly	Framework value at this scale
Indie extreme throughput	<1M pages/day	fasthttp + GoQuery	When net/http becomes a bottleneck
SMB scraping infra	1-10M pages/day	Colly + Redis queue + custom workers	Distribute across N binaries
SMB JS-heavy	<500k pages/day	Chromedp + GoQuery post-parse	Browser only when needed
Embedded scraping in service	varies	net/http only	Avoid framework imports inside larger services
Enterprise data pipeline	10M+ pages/day	Custom Go workers + Kafka + GoQuery	Maximum control, minimum dependencies

The right pattern for Go at scale is custom workers reading from a queue rather than a monolithic Colly process. Goroutines do the concurrency; Redis or NATS does the work distribution. This pattern scales linearly with worker count and survives single-machine failures cleanly.

Migration path: Python or Node to Go

Most Go migrations happen when Python or Node scrapers hit infrastructure limits at scale. The playbook:

1. Identify the throughput bottleneck. If your Python scraper saturates one CPU core at 800 req/s, Go can run the same workload at 4000+ req/s on one core. If you are not CPU-bound, the migration may not pay off.
2. Port one scraper end-to-end. Choose the highest-throughput single-target scraper as the migration pilot. Validate output equivalence on a sample.
3. Keep Python or Node for orchestration. Many teams use Go for the scraper workers and Python for the data pipeline (Pandas, ML preprocessing). The tools do not have to match.
4. Containerize and deploy in parallel. Run Go workers alongside Python workers reading from the same queue. Cut over by reducing Python worker count over a few weeks.
5. Re-evaluate at six months. If the Go workers are stable and the throughput gain is real, migrate the rest. If they are not, the original choice was right.

The migration is rarely binary. Most production scrapers end up polyglot with Go for hot-path workers and Python for one-off and analytical work.

Performance benchmarks

Same workload as Python and Node benchmarks: 10,000 simple HTML pages from a local mirror, single Go binary.

stack	total time	requests/sec
net/http (50 goroutines)	6s	1666
fasthttp (50 goroutines)	3s	3333
resty (50 goroutines)	8s	1250
Colly (default)	7s	1428
Chromedp (50 contexts)	110s	90

Go HTTP throughput is the highest of the three languages we benchmarked. fasthttp specifically is faster than even Node’s undici for this workload. Browser automation is similar across all languages because the bottleneck is browser execution.

Cost worked example

For a 1M-pages-per-day Go scraping workload (roughly 12 req/s sustained):

• 1 medium VPS ($40/mo, 8 vCPU, 16 GB)
• net/http + GoQuery + Colly stack (free)
• uTLS for TLS fingerprint impersonation when needed (free)
• Smartproxy/Decodo residential proxies (~$200/mo for ~25 GB)
• Redis on a small managed instance ($10/mo) for distributed work coordination
• PostgreSQL on a hosted instance ($25/mo)

Total: about $275/month for a workload that handles 30 million pages per month. The Python or Node equivalent would need 2-3x the compute capacity for the same throughput, raising infrastructure cost by $80-120/month. Go’s compiled binary also reduces deployment complexity (no language runtime, no virtualenv) and operational toil.

The break-even point where Go’s lower compute cost overcomes its higher development cost typically sits around 10M pages/month. Below that, Python or Node ergonomics usually win on total team productivity.

Stack recommendations

Most Go scraping: net/http + GoQuery + Colly. Standard library plus the two best community libraries. Adequate for almost everything.

Extreme throughput: fasthttp + GoQuery + Colly. When you need 10k+ HTTP requests per second per machine.

Browser-required scraping: Chromedp + GoQuery (for parsing extracted HTML). Use Chromedp for JS execution, GoQuery for the parsing because it is more ergonomic.

Distributed scraping: net/http + GoQuery + custom code with Redis queues. Colly does not have great distributed support; for multi-machine scrapers you build the coordination layer yourself.

Scraping inside a larger Go service: net/http directly. Avoid pulling in framework overhead for embedded scraping inside a service that does other things.

Idiomatic Go scraper template

A modern Go scraper using standard libraries:

package main

import (
    "context"
    "fmt"
    "io"
    "net/http"
    "strings"
    "sync"
    "time"

    "github.com/PuerkitoBio/goquery"
)

type Product struct {
    Name  string
    Price string
    URL   string
}

func fetchPage(ctx context.Context, url string) (string, error) {
    req, err := http.NewRequestWithContext(ctx, "GET", url, nil)
    if err != nil {
        return "", err
    }
    req.Header.Set("User-Agent", "Mozilla/5.0")

    client := &http.Client{Timeout: 15 * time.Second}
    resp, err := client.Do(req)
    if err != nil {
        return "", err
    }
    defer resp.Body.Close()
    body, err := io.ReadAll(resp.Body)
    return string(body), err
}

func parseProducts(html string) []Product {
    doc, err := goquery.NewDocumentFromReader(strings.NewReader(html))
    if err != nil {
        return nil
    }
    var products []Product
    doc.Find("div.product-card").Each(func(i int, s *goquery.Selection) {
        products = append(products, Product{
            Name:  s.Find("h2.title").Text(),
            Price: s.Find("span.price").Text(),
            URL:   s.Find("a").AttrOr("href", ""),
        })
    })
    return products
}

func scrapeAll(urls []string, concurrency int) []Product {
    sem := make(chan struct{}, concurrency)
    var wg sync.WaitGroup
    var mu sync.Mutex
    var allProducts []Product

    for _, url := range urls {
        wg.Add(1)
        go func(u string) {
            defer wg.Done()
            sem <- struct{}{}
            defer func() { <-sem }()

            html, err := fetchPage(context.Background(), u)
            if err != nil {
                fmt.Println("error:", err)
                return
            }
            products := parseProducts(html)
            mu.Lock()
            allProducts = append(allProducts, products...)
            mu.Unlock()
        }(url)
    }
    wg.Wait()
    return allProducts
}

func main() {
    urls := []string{"https://example.com/p/1", "https://example.com/p/2"}
    products := scrapeAll(urls, 20)
    for _, p := range products {
        fmt.Printf("%+v\n", p)
    }
}

This pattern handles 1500+ pages per minute on a small VPS with proper concurrency control.

Distributed scraper architecture

For workloads that exceed one machine, the canonical Go scraper architecture is:

• Coordinator service that pushes URLs to a queue (Redis Streams, NATS JetStream, or Kafka).
• Worker pool of N stateless Go binaries, each consuming from the queue, scraping in parallel goroutines, and writing results to a sink (Postgres, S3, Kafka).
• Health and metrics exposed via Prometheus endpoints on each worker; scraped via a central Prometheus + Grafana stack.
• Dead-letter queue for URLs that fail repeatedly, picked up by a slower retry process or surfaced for manual investigation.

This pattern scales linearly: doubling worker count doubles throughput up until the target rate-limits or the queue itself bottlenecks. With NATS or Kafka, the queue layer easily handles 100k messages/sec, far beyond what most scrapers need.

Common mistakes to avoid

Forgetting to close response bodies: every HTTP response body must be closed or you leak file descriptors. The defer resp.Body.Close() pattern is essential.

Unbounded goroutine spawning: launching one goroutine per URL without a semaphore exhausts memory and overwhelms target sites. Use a buffered channel as a semaphore.

Using fasthttp when you do not need it: fasthttp’s API is different from net/http and the integration cost is real. For most workloads, net/http is fast enough.

Ignoring context cancellation: pass context.Context through your scraper functions so you can cancel cleanly on shutdown signals.

Trying to use Python-style async patterns: Go’s concurrency primitives (goroutines, channels, sync.WaitGroup) are different from async/await. Embrace them rather than fighting them.

We cover the Python and Node alternatives in our best Python scraping libraries 2026 and best Node.js scraping libraries 2026 reviews.

External authoritative reference: the Go net/http documentation covers the standard library client.

Common gotchas

• Goroutine leaks. Goroutines started without a clear exit path can leak forever if their channel never closes. Always have a context.Done() check or a select with a timeout case.
• net/http connection reuse defaults. The default Transport reuses connections, which is good for performance but bad if you want each request from a fresh proxy. For per-request isolation, set Transport.DisableKeepAlives = true.
• fasthttp’s API allocations. fasthttp.Request and Response are pooled; you must Acquire and Release them. Forgetting Release causes memory growth that looks like a leak.
• GoQuery selector syntax differences. GoQuery uses CSS selectors but does not support all jQuery extensions. :contains() is supported, :has() is not. Test your selectors against the actual DOM before assuming.
• Chromedp context cancellation. Cancelling the parent context kills all in-flight Chrome operations, but the headless Chrome process can survive. Always call chromedp.Cancel(ctx) explicitly to ensure cleanup.
• JSON unmarshaling silent failures. Unknown fields are silently dropped by json.Unmarshal. If your target’s response shape changes, you may not notice. Use DisallowUnknownFields() on the decoder during development.
• Slice append concurrency. Multiple goroutines appending to the same slice corrupt it. Use a sync.Mutex or a channel-based aggregator.
• Colly OnHTML callback ordering. Multiple OnHTML handlers for overlapping selectors fire in registration order, not in DOM order. Test handler ordering if you depend on it.

When to use Go vs Python vs Node

consideration	best language
highest HTTP throughput per machine	Go
richest ecosystem	Python
best browser automation	Python or Node (Playwright)
simplest deployment	Go (single binary)
smallest learning curve	Python
best for embedded scraping in services	Go
largest community of scraping content	Python
AI/LLM integration	Python

For dedicated scraping projects that scale and benefit from compiled performance, Go is excellent. For one-offs and projects requiring rich ecosystem support, Python wins. For Node-shop infrastructure, Node Crawlee fits naturally.

FAQ

Q: Colly or write my own?
For projects with link-following, deduplication, and rate-limiting needs across thousands of pages, Colly saves significant code. For simple scrapers with a known URL list, raw net/http + GoQuery is enough.

Q: Chromedp or Rod or Playwright-go?
Chromedp is the safe default with the most production usage. Rod has a nicer API. Playwright-go is right when you need Firefox or WebKit. Performance is similar across all three.

Q: how do I handle TLS fingerprinting in Go?
Go’s TLS stack does not have first-class fingerprint impersonation. The closest options are utls (uTLS) which mimics specific browser TLS handshakes, and routing through a proxy that handles fingerprinting.

Q: is fasthttp worth the complexity?
For most workloads, no. fasthttp gives you an extra 2-5x throughput at the cost of API divergence from the standard library. Use it when you have measured a performance need that net/http cannot meet.

Q: does Go have an equivalent to Scrapy?
Colly is the closest. Less batteries-included than Scrapy but covers the core crawler patterns.

Q: how do I handle proxies in Go?
Set Transport.Proxy on your http.Client. For per-request proxy rotation, build a custom Transport that selects a proxy from a pool. Colly accepts a proxy switcher function natively.

Q: are there structured-data extraction libraries?
A few exist (go-rod/rod for browser, tdewolff/parse for streaming HTML). For strongly typed extraction, write a struct and unmarshal CSS selectors into it manually with reflection or code generation.

Q: is Go’s gc a problem for long-running scrapers?
Generally no. Go’s GC has been excellent since 1.14 with sub-millisecond pauses. The main GC concern is allocating in tight loops; reuse buffers and pool objects with sync.Pool if you see GC pressure.

Closing

Go scraping in 2026 is the right choice for high-throughput dedicated scraping infrastructure, distributed crawler workers, and embedded scraping inside Go services. The ecosystem is smaller than Python’s but the libraries that exist are excellent. net/http + GoQuery + Colly is the standard stack; fasthttp and Chromedp cover specialized needs. For broader scraping infrastructure see our dev-tools-projects category hub.

Best Node.js scraping libraries 2026

Best Node scraping libraries in 2026 occupy a different ecosystem than Python’s. Node’s event-loop architecture is naturally async-first, so concurrency comes for free. Browser automation has stronger native fit because Puppeteer was originally a Node-only library and the JavaScript-runtime-controlling-JavaScript story is uniquely tight. The Node scraping market has consolidated around a smaller list of high-quality libraries than Python’s, but each library is more polished and the gaps are smaller. The four-layer model still applies: HTTP client, browser automation, HTML parser, framework. Picking the right combination per layer determines whether your scraper handles 100 or 10,000 requests per second.

This guide ranks the Node.js scraping libraries actually worth using in 2026, with honest performance comparisons, clear use case mapping, and the gotchas that surprise developers coming from Python.

HTTP clients

undici

Node’s modern HTTP client, developed by the Node.js core team. Faster than every alternative by 2-3x for high-concurrency workloads. The standard fetch global in modern Node uses undici under the hood.

import { fetch } from 'undici';

async function scrape(url) {
  const resp = await fetch(url, {
    headers: { 'User-Agent': 'Mozilla/5.0 ...' },
  });
  return resp.text();
}

Best for: any new project, high-concurrency workloads, the default unless you have specific needs.

got

The popular HTTP client before undici took over. Excellent retry, redirect, and cookie handling. Slightly slower than undici but more feature-complete out of the box.

import got from 'got';

const html = await got('https://example.com', {
  retry: { limit: 3 },
  timeout: { request: 10000 },
}).text();

Best for: existing got codebases, projects that benefit from got’s batteries-included extras.

axios

The classic. Sync-style promise API that everyone knows. Slower than undici and got. Still ubiquitous because of legacy familiarity.

Best for: existing axios codebases, teams that already know its API.

node-fetch

The original Node fetch polyfill, now mostly obsolete since native fetch landed in Node 18+.

Best for: legacy projects, nothing else.

Browser automation

Playwright

Same library as Python; the JavaScript version is actually the reference implementation. Cleaner API in JavaScript than Python because of TypeScript autocompletion. Best browser automation framework in any language.

import { chromium } from 'playwright';

const browser = await chromium.launch();
const context = await browser.newContext({
  userAgent: 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
});
const page = await context.newPage();
await page.goto('https://target.example.com');
const title = await page.locator('h1.product-title').textContent();
await browser.close();

Best for: most modern browser automation in Node, multi-browser needs.

Puppeteer

The Google-maintained Chrome automation library. Slightly cleaner Chrome-specific features than Playwright, similar overall capability. The puppeteer-extra plugin ecosystem (especially stealth plugin) is more mature than Playwright’s equivalents.

Best for: Chrome-only workflows, projects using puppeteer-extra plugins.

Crawlee

Apify’s scraping framework with built-in browser support. Wraps Playwright/Puppeteer with crawler-style ergonomics. We cover it under frameworks below.

HTML parsers

Cheerio

The jQuery-syntax server-side parser. The dominant Node HTML parser. Fast (built on parse5 or htmlparser2), familiar API for anyone who used jQuery.

import * as cheerio from 'cheerio';

const $ = cheerio.load(html);
const titles = $('h2.product-title').map((i, el) => $(el).text()).get();

Best for: most HTML parsing in Node, jQuery-familiar developers.

parse5

The lower-level HTML parser that Cheerio uses under the hood. Direct use is rare but available for custom AST work.

Best for: custom HTML manipulation, building higher-level tools.

htmlparser2

Streaming HTML parser, very fast on large documents. Used by Cheerio when configured for it. Direct use for stream-based parsing.

Best for: parsing very large HTML documents in stream mode.

linkedom

Modern alternative offering full DOM API (not just jQuery-style). If your code expects document.querySelector semantics, linkedom feels native.

import { parseHTML } from 'linkedom';

const { document } = parseHTML(html);
const titles = Array.from(document.querySelectorAll('h2.product-title')).map(el => el.textContent);

Best for: developers who prefer DOM API over jQuery API, code shared between client and server.

Frameworks

Crawlee

Apify’s modern scraping framework. The Node version is the original; the Python port came later. Excellent abstractions for HTTP and browser scraping with the same Crawler interface, built-in queue management, dedupe, retry logic, and proxy rotation.

import { CheerioCrawler } from 'crawlee';

const crawler = new CheerioCrawler({
  async requestHandler({ request, $ }) {
    console.log(`Scraping ${request.url}`);
    const titles = $('h2.product-title').map((i, el) => $(el).text()).get();
    await crawler.pushData({ url: request.url, titles });
  },
  maxRequestsPerCrawl: 1000,
  maxConcurrency: 10,
});

await crawler.run(['https://shop.example.com/page/1']);

Best for: most modern Node scraping projects that need framework benefits.

x-ray

Older declarative scraping framework. Still works but rarely chosen for new projects.

Best for: legacy x-ray codebases.

Apify SDK

Crawlee’s parent SDK with additional Actor and platform features. Right choice if deploying to Apify cloud.

Best for: Apify platform deployments.

Comparison table

library	layer	speed	learning curve	best for
undici	HTTP	fastest	easy	most new projects
got	HTTP	fast	easy	retry-heavy needs
axios	HTTP	mid	easy	legacy codebases
node-fetch	HTTP	mid	easy	nothing in 2026
Playwright	browser	mid	medium	most browser automation
Puppeteer	browser	mid	medium	Chrome-only, stealth plugins
Cheerio	parser	fast	easy	most parsing
parse5	parser	fast	hard	custom AST work
htmlparser2	parser	fastest	medium	very large docs, streaming
linkedom	parser	fast	easy	DOM-API preference
Crawlee	framework	fast	medium	modern crawler projects
x-ray	framework	mid	easy	legacy

Decision matrix: solopreneur, SMB, enterprise

profile	scale	recommended stack	reasoning
Solopreneur learning	<10k pages/day	native fetch + Cheerio	Zero dependencies, modern defaults
Indie scraper	<500k pages/day	undici + Cheerio + p-limit	Best HTTP perf, simple flow control
Indie JS-heavy	<100k pages/day	Playwright + Cheerio post-parse	Browser only when needed
SMB crawler	500k-10M pages/day	Crawlee CheerioCrawler	Framework manages queue, dedupe, retry
SMB anti-detect	100k-1M pages/day	Puppeteer + puppeteer-extra-plugin-stealth	Stealth ecosystem maturity
Enterprise	10M+ pages/day	Crawlee on K8s + custom middleware	Volume justifies platform investment
Hybrid HTTP/JS	varies	Crawlee (CheerioCrawler + PlaywrightCrawler)	Same dataset across two modes

The Node ecosystem rewards convergence: most teams end up on undici + Cheerio for HTTP and Playwright + stealth plugins for browser. Crawlee adds value above 500k pages/day; below that, hand-rolled async with p-limit is simpler and fast enough.

Migration path: axios + cheerio to undici + Cheerio

Most legacy Node scrapers run on axios because it was the dominant HTTP client of the 2018-2022 era. Modernizing to undici is straightforward and yields a 2-3x throughput improvement:

1. Replace axios.get(url, opts) with await fetch(url, opts) from undici. The API differs slightly (response body via .text() / .json() instead of .data).
2. Replace axios interceptors with explicit retry wrappers. undici does not have an interceptor system; use a small wrapper function for retry, logging, and metrics.
3. Update timeout handling to use AbortSignal.timeout(ms) instead of axios’s timeout option.
4. Benchmark the same workload before and after. Expect 2-3x improvement on concurrent request throughput.
5. Keep axios for any code that uses interceptors heavily (auth refresh patterns, request signing) where the cost of unwinding interceptor logic outweighs the perf gain.

A typical Node scraper migration completes in a day. The performance gain often unblocks scaling work that was on the roadmap for distributed infrastructure.

Performance benchmarks

Same workload as the Python benchmarks: 10,000 simple HTML pages from a local mirror, single Node process.

stack	total time	requests/sec
native fetch (50 concurrency)	8s	1250
undici (50 concurrency)	7s	1428
got (50 concurrency)	11s	909
axios (50 concurrency)	14s	714
Playwright (50 concurrent contexts)	88s	113
Crawlee CheerioCrawler	9s	1111

Node beats Python on raw HTTP throughput thanks to its event loop architecture. The browser automation gap is similar in both languages because the bottleneck is browser execution, not the host runtime.

Cost worked example

For a 100k-pages-per-day Node scraping workload on mixed protected and unprotected targets:

• 1 small VPS ($20/mo, 4 vCPU, 8 GB)
• undici + Cheerio + p-limit stack (free, Node only)
• node-libcurl when TLS impersonation is needed (free, requires native build)
• Smartproxy/Decodo residential proxies (~$50/mo for 5 GB)
• PostgreSQL on a hosted instance ($25/mo)
• Optional: ZenRows fallback for hard surfaces (~$69/mo)

Total: about $95-165/month depending on the API fallback. Node throughput is higher than Python on raw HTTP, which lets you pack more work into the same VPS; expect to need ~30% less compute capacity than the equivalent Python deployment.

The other Node-specific cost dimension is RAM. Node processes typically run 200-300 MB at scraping idle and grow with concurrent contexts. For a single-process scraper, 8 GB RAM is plenty; for distributed multi-worker setups, prefer many small workers over few large ones to limit blast radius from leaks.

Stack recommendations

Small project, scripts: native fetch + Cheerio. Built into modern Node, no dependencies, fast.

Medium project, no JS needs: undici + Cheerio. Fastest HTTP client + standard parser. Add tenacity-style retry via simple wrapper.

JavaScript-heavy targets: Playwright + Cheerio (parse the extracted HTML with Cheerio for speed instead of using Playwright’s slower DOM querying).

Large crawler with link-following: Crawlee. Built-in queue management saves you from writing your own.

Anti-bot heavy targets: Puppeteer with puppeteer-extra-plugin-stealth. The stealth plugin ecosystem is more mature for Puppeteer than for Playwright in Node.

Hybrid HTTP + browser: Crawlee with multiple crawler classes (CheerioCrawler for HTTP-only pages, PlaywrightCrawler for JS-heavy pages, both writing to the same dataset).

Crawlee deep dive

Crawlee deserves a closer look because it has matured into the de-facto Node scraping framework. Its three crawler classes cover the spectrum:

• CheerioCrawler: HTTP-only, uses got (or undici under the hood) and Cheerio. Fast, low-resource. The right default for HTTP scraping.
• PlaywrightCrawler: full browser automation with Playwright. Highest resource cost but handles any JavaScript.
• PuppeteerCrawler: same as Playwright but using Puppeteer. Choose this if your team prefers Puppeteer’s API or uses puppeteer-extra plugins.

All three share the same RequestQueue, Dataset, and KeyValueStore abstractions, which means you can switch between HTTP and browser modes per request without changing your data layer. A typical pattern is to start with CheerioCrawler, fall back to PlaywrightCrawler when the HTML is missing the data you need, and store both kinds of results in the same Dataset.

Crawlee’s RequestQueue supports SQLite, MongoDB, and the Apify cloud as backends. SQLite works for single-process crawlers; MongoDB works for distributed crawlers across machines. The cloud backend gives you a managed queue with no operational overhead.

Modern async patterns

Node’s async syntax is cleaner than Python’s for typical scraping patterns:

import { fetch } from 'undici';
import * as cheerio from 'cheerio';
import pLimit from 'p-limit';

async function fetchPage(url, retries = 3) {
  for (let attempt = 0; attempt < retries; attempt++) {
    try {
      const resp = await fetch(url, {
        signal: AbortSignal.timeout(15000),
      });
      if (resp.status === 200) {
        return await resp.text();
      }
      if (resp.status === 429 || resp.status === 503) {
        await new Promise(r => setTimeout(r, Math.pow(2, attempt) * 1000));
        continue;
      }
      return null;
    } catch (err) {
      if (attempt === retries - 1) throw err;
      await new Promise(r => setTimeout(r, Math.pow(2, attempt) * 1000));
    }
  }
}

function parseProducts(html) {
  if (!html) return [];
  const $ = cheerio.load(html);
  return $('div.product-card').map((i, el) => ({
    name: $(el).find('h2.title').text(),
    price: $(el).find('span.price').text(),
  })).get();
}

async function scrapeAll(urls, concurrency = 20) {
  const limit = pLimit(concurrency);
  const results = await Promise.all(
    urls.map(url => limit(async () => {
      const html = await fetchPage(url);
      return parseProducts(html);
    }))
  );
  return results.flat();
}

This pattern handles 1000+ pages per minute on a modest VPS with retries and concurrency control built in.

Persistence and storage in Node

Node scrapers benefit from a few storage patterns specific to JavaScript ecosystems:

• better-sqlite3 for synchronous local storage. Faster than the async sqlite3 library for write-heavy workloads because it avoids async overhead.
• Knex or Prisma for typed Postgres access. Both work well; Prisma’s TypeScript types are stronger but Knex is lighter.
• Crawlee KeyValueStore + Dataset. When using Crawlee, prefer its built-in storage abstractions; they handle large blobs and structured records cleanly.
• DuckDB-WASM for in-process analytics. When you want to query scraped data without a database server, DuckDB now ships a Node binding that lets you run SQL on Parquet or local arrays.

For very large output volumes, stream writes to S3 / R2 with @aws-sdk/client-s3 MultipartUpload rather than collecting everything in memory and uploading at the end.

Common mistakes to avoid

Using axios in 2026: it works but is slower than undici and got. New projects should default to undici.

Forgetting AbortSignal.timeout: Node’s native fetch does not have a default timeout. Without one, your scraper hangs on slow targets indefinitely.

Loading huge HTML strings into Cheerio at once: for documents over 10 MB, use htmlparser2 in streaming mode.

Running too many browser contexts in one Node process: Node memory grows fast with many Playwright contexts. Stay under 50 concurrent contexts per process.

Ignoring back-pressure in Crawlee: Crawlee’s queues can grow unboundedly if you push faster than you consume. Set maxRequestsPerCrawl and maxConcurrency appropriately.

We cover the Python equivalent in our best Python scraping libraries 2026 review.

Common gotchas

• Native fetch lacks default timeout. Without AbortSignal.timeout(), your scraper hangs on slow targets indefinitely. Always set a timeout.
• undici keep-alive defaults. undici defaults to keep-alive connections. For one-off scripts, this can leave the process hanging waiting for sockets. Use Agent({ keepAliveTimeout: 1 }) or call agent.close() at script end.
• Cheerio re-parse cost. Each call to cheerio.load() re-parses the HTML. For many extractions on the same document, parse once and pass $ around.
• Playwright newPage vs newContext. newPage() reuses the parent context’s cookies; newContext() creates a fresh storage state. Use newContext() per scrape to isolate cookies; many subtle bugs come from cookie cross-contamination.
• Crawlee request handler errors. A throw inside requestHandler retries the request by default. If the error is permanent (404, parse failure), call request.noRetry = true to skip the retry queue.
• JSON parsing with native fetch. await resp.json() throws on empty body; wrap in try/catch or check resp.ok first.
• EventEmitter memory leaks. Browser launches that emit console, request, or response events accumulate listeners if you do not clean them up. Use page.removeAllListeners() before close or use named handler functions you can remove explicitly.
• TLS hardening on undici. Some targets refuse TLS 1.2 connections. undici defaults to negotiating up; if you see handshake errors, force connect: { tls: { minVersion: 'TLSv1.3' } }.

TypeScript vs JavaScript

For new projects, TypeScript is the right choice. The HTTP and parsing libraries all ship with strong type definitions. The Playwright API in TypeScript is a different developer experience than JavaScript.

import { chromium, Browser, Page } from 'playwright';

async function scrape(browser: Browser, url: string): Promise<string | null> {
  const page: Page = await browser.newPage();
  try {
    await page.goto(url);
    return await page.locator('h1').textContent();
  } finally {
    await page.close();
  }
}

The autocompletion and refactoring support pay off within the first week of any non-trivial project.

External authoritative reference: the Node.js documentation on the global fetch covers the standard HTTP client.

Bun and Deno alternatives

Bun and Deno both ship with built-in fetch and run all the libraries above. Bun is notably faster than Node for HTTP-heavy workloads (about 1.5-2x in our testing). Deno’s permission model is interesting for scraping isolation but the ecosystem is smaller.

For most teams, Node remains the right default in 2026 because library compatibility is broadest. We cover the alternatives in our forthcoming guides on Bun and Deno scraping.

FAQ

Q: Cheerio or jQuery selectors?
Cheerio implements jQuery-style selectors server-side. The API is essentially identical. Use Cheerio in Node; do not import actual jQuery server-side.

Q: should I use Crawlee or write my own crawler?
For projects under 1000 pages, write your own with undici + Cheerio + p-limit. For larger crawls with link-following, dedupe, and retry needs, Crawlee saves significant code.

Q: Puppeteer or Playwright?
Playwright is technically better for new projects: cleaner API, multi-browser, better auto-waiting. Puppeteer has the puppeteer-extra-plugin-stealth ecosystem advantage which still matters for some anti-detect work.

Q: how do I handle TLS fingerprinting in Node?
Node does not have a great equivalent to Python’s curl_cffi yet. The closest options are node-libcurl (libcurl bindings for Node) or routing through a proxy that handles TLS fingerprinting on your behalf.

Q: is JSDOM useful for scraping?
JSDOM is heavier than Cheerio because it implements the full DOM API including layout. For scraping where you do not need actual JavaScript execution, Cheerio is faster. JSDOM is the right choice when you want to execute scripts on a parsed document without a full browser.

Q: how do I integrate proxies?
With undici, use a ProxyAgent. With Playwright, pass proxy to chromium.launch(). Crawlee has built-in proxy rotation across a pool. Avoid manual proxy management; use the built-in tools wherever possible.

Q: is Bun production-ready for scraping?
Yes for most use cases. Bun’s built-in fetch and HTML parser are excellent. The remaining gaps are around obscure npm packages with native dependencies that have not been compiled for Bun. Test your dependency tree before committing to Bun in production.

Q: what is the cleanest way to handle pagination?
Wrap your fetch in an async generator that yields pages until a stop condition. Async generators in Node compose nicely with for await loops and avoid materializing all pages in memory.

Closing

The Node.js scraping stack in 2026 is mature and stable. undici for HTTP, Cheerio for parsing, Playwright for browser automation, Crawlee for crawler frameworks. The ecosystem moves slower than Python’s but each piece is more polished. Match the stack to the workload and Node will outperform Python on raw HTTP throughput while matching it on browser automation. For broader scraping infrastructure see our dev-tools-projects category hub.

Best Python scraping libraries 2026: Scrapy, BS4, more

Best Python scraping libraries in 2026 cover a stack that has matured significantly since the requests + BeautifulSoup era. The HTTP client layer has been split into a dozen options optimized for different use cases. Browser automation has consolidated around Playwright. Parsing has stabilized on lxml under the hood with multiple frontend options. The framework layer sees Scrapy holding its dominance for crawling-heavy use cases and Crawlee gaining ground as a modern alternative. Choosing the right combination of libraries determines whether your scraper runs at 10 requests per second or 1000, whether it survives anti-bot fingerprinting, and how much code you write to do straightforward things.

This guide ranks the Python scraping libraries actually worth using in 2026, organized by what they do, with honest performance comparisons and clear guidance on which to pick for which workload.

The four layers of a Python scraper

Every scraper has four layers, regardless of framework:

1. HTTP client: makes the actual network requests
2. Browser automation (optional): when JavaScript execution is needed
3. HTML parser: extracts data from the response
4. Orchestration framework (optional): handles concurrency, retries, queues, pipelines

Different libraries dominate each layer. The right scraper picks the best library per layer rather than committing to one library for everything.

HTTP clients

requests

The classic. Synchronous, simple, mature. Still the right choice for one-off scripts and learning. Performance is the worst of the modern options because it is sync-only.

import requests

resp = requests.get("https://example.com", headers={"User-Agent": "..."}, timeout=10)
print(resp.text)

Best for: scripts, prototypes, learning, anything where async is overkill.

httpx

The modern requests replacement. Supports both sync and async, HTTP/2 by default, type-hinted, and dramatically faster than requests for any concurrent workload. The drop-in replacement for requests in most code.

import httpx
import asyncio

async def fetch(url: str):
    async with httpx.AsyncClient(http2=True, timeout=10) as client:
        resp = await client.get(url)
        return resp.text

# concurrent fetching
async def main():
    urls = ["https://example.com/page/1", "https://example.com/page/2"]
    return await asyncio.gather(*[fetch(u) for u in urls])

Best for: any new project, async workloads, HTTP/2 support, type safety.

aiohttp

The async-first HTTP client. Older than httpx but still excellent. Slightly faster than httpx for high concurrency. The websocket support is best in class.

Best for: high-concurrency async workloads, websocket-heavy use cases.

curl_cffi

The TLS-fingerprint-aware HTTP client. Wraps libcurl with browser-impersonation features so your TLS handshake looks like real Chrome, Firefox, or Safari. The right choice for any target with TLS fingerprinting (Cloudflare, DataDome, Akamai).

from curl_cffi import requests

# impersonates Chrome 120 TLS fingerprint
resp = requests.get(
    "https://target.example.com",
    impersonate="chrome120",
    timeout=10,
)
print(resp.text)

Best for: bypassing TLS fingerprinting, scraping sites that detect Python’s default TLS.

urllib3

The HTTP foundation that requests and httpx both use under the hood. Rarely used directly except for very low-level needs.

Best for: when you need fine-grained control of connection pools.

HTML parsers

lxml

The fast XML/HTML parser written in C. Underpins almost every other parser. Direct lxml use is fastest but the API is uglier than BeautifulSoup.

from lxml import html

tree = html.fromstring(html_content)
titles = tree.xpath("//h2[@class='product-title']/text()")

Best for: high-performance parsing, XPath-heavy extraction.

BeautifulSoup4

The friendly parser. Slower than lxml directly but has the most readable API. The standard configuration uses lxml as the underlying parser, so the speed gap is smaller than people assume.

from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, "lxml")
titles = [t.text for t in soup.select("h2.product-title")]

Best for: most general-purpose parsing, readable code, mixed CSS/find patterns.

selectolax

The fastest Python HTML parser by a wide margin. C-based, supports CSS selectors with a minimal API. Roughly 5-10x faster than BeautifulSoup on typical pages.

from selectolax.parser import HTMLParser

tree = HTMLParser(html_content)
titles = [n.text() for n in tree.css("h2.product-title")]

Best for: high-volume parsing where every millisecond matters.

parsel

Scrapy’s parser, available standalone. Combines XPath, CSS, and regex selectors with a clean API.

Best for: Scrapy users wanting the same API outside Scrapy.

Browser automation

Playwright

The current best browser automation framework. We covered it in detail in best headless browser frameworks 2026.

Best for: modern browser automation, multi-browser support.

Selenium

The elder framework. Still solid for cross-browser needs. Verbose but well-documented.

Best for: legacy projects, multi-language teams sharing test infrastructure.

Pyppeteer (less recommended)

Python port of Puppeteer. Less actively maintained than Playwright. Avoid for new projects.

Frameworks

Scrapy

The dominant Python scraping framework. Async by design (since 2.0), built-in queue management, middleware system, item pipelines, and crawl rules. The right choice for crawler-heavy workloads where you are following links across thousands of pages.

import scrapy

class ProductSpider(scrapy.Spider):
    name = "products"
    start_urls = ["https://shop.example.com/category/widgets"]

    def parse(self, response):
        for product in response.css("div.product"):
            yield {
                "name": product.css("h2::text").get(),
                "price": product.css("span.price::text").get(),
                "url": product.css("a::attr(href)").get(),
            }
        next_page = response.css("a.next::attr(href)").get()
        if next_page:
            yield response.follow(next_page, self.parse)

The downside: Scrapy’s mental model is heavier than other frameworks. You learn callbacks, middleware, settings, items, and pipelines. For simple scrapers this is overkill.

Best for: large crawl projects, structured data extraction at scale, when you actually need a framework.

Crawlee

The newer framework from Apify, with a Pythonic API and built-in browser support. More approachable than Scrapy for newcomers. Supports HTTP and browser modes from the same crawler class.

Best for: modern projects that want framework benefits without Scrapy’s learning curve.

Pyspider

Older framework with a web UI for managing scrapers. Less actively maintained but still works for some use cases.

Best for: legacy systems, niche use cases needing visual management.

Comparison table

library	layer	sync/async	speed	learning curve	best for
requests	HTTP	sync	slow	easy	scripts, prototypes
httpx	HTTP	both	fast	easy	most new projects
aiohttp	HTTP	async	fast	medium	high-concurrency async
curl_cffi	HTTP	sync	fast	easy	TLS fingerprint bypass
BeautifulSoup4	parser	sync	mid	easy	general parsing
lxml	parser	sync	fast	medium	high-performance XPath
selectolax	parser	sync	fastest	easy	extreme volume parsing
parsel	parser	sync	fast	easy	Scrapy users
Playwright	browser	both	mid	medium	JS-heavy targets
Selenium	browser	sync (mostly)	slow	medium	legacy
Scrapy	framework	async	fast	hard	large crawls
Crawlee	framework	async	fast	medium	modern, browser-friendly

Decision matrix: solopreneur, SMB, enterprise

profile	scale	recommended stack	reasoning
Solopreneur learning	<1k pages/day	requests + BeautifulSoup4	Simple, beginner-friendly
Indie scraper (basic)	<100k pages/day	httpx + selectolax	Async, fast, modern
Indie scraper (anti-bot)	<100k pages/day	curl_cffi + selectolax	TLS impersonation included
SMB crawler	100k-10M pages/day	Scrapy + curl_cffi middleware + selectolax	Framework value at this scale
SMB JS-heavy	10k-1M pages/day	Crawlee or Playwright + httpx fallback	Hybrid HTTP/browser ergonomics
Enterprise pipeline	10M+ pages/day	Scrapy on K8s + custom middleware + dedicated parsers	Full ops + custom optimization
Single-source ETL	varies	httpx + lxml direct XPath	Tight, predictable, performant

The most expensive mistake is over-frameworking small jobs (Scrapy for 200 pages) and under-frameworking large jobs (raw httpx loop for 10M URLs). Match the framework weight to the actual workload.

Migration path: requests + BS4 to httpx + selectolax

Most legacy Python scrapers can be modernized in a day with significant performance gains. The playbook:

1. Wrap your fetch function in an async signature even before changing implementation. This isolates the migration scope.
2. Replace requests.get with httpx.AsyncClient.get. Most code translates 1:1; the main change is await keywords and the async context manager.
3. Switch parser to selectolax. CSS selectors translate directly from BeautifulSoup; XPath users stay on lxml. Expect 5-10x parse speedup.
4. Add concurrency with asyncio.Semaphore to bound parallel requests. Start at 10 and tune based on target tolerance.
5. Benchmark against original with the same input set. A typical migration shows 15-30x throughput improvement.

The whole migration usually takes one engineer-day for a single-purpose scraper, two days for a multi-target codebase. The throughput gain often eliminates the need for distributed scaling that was on the roadmap.

Performance benchmarks

We benchmarked HTTP fetching of 10,000 simple HTML pages from a local mirror, single machine, no network bottleneck.

stack	total time	requests/sec
requests (sync)	240s	42
httpx (async, 50 concurrency)	12s	833
aiohttp (50 concurrency)	11s	909
curl_cffi (50 concurrency)	14s	714
Scrapy (default settings)	18s	555
Playwright (50 concurrent contexts)	95s	105

Async HTTP is 20x faster than sync. Browser automation is 8-10x slower than HTTP. The difference is essentially the cost of running JavaScript and rendering, which is unavoidable for SPAs.

Choosing between async frameworks

Python’s async ecosystem fragmented for years between asyncio (standard library), trio (alternative event loop with cleaner cancellation semantics), and AnyIO (a compatibility layer). For scraping, asyncio is the right default because every major HTTP and parser library targets it. trio remains technically superior for cancellation safety but the ecosystem cost is real.

The other choice is between asyncio’s default event loop and uvloop (a Cython-accelerated drop-in replacement). For HTTP-bound scrapers, uvloop yields a 2-4x throughput improvement essentially for free:

import asyncio
import uvloop

uvloop.install()  # do this before any asyncio code

# rest of your scraper

The two-line installation gets you the benefit. The only caveat is that uvloop does not work on Windows; cross-platform code needs a try/except around the install call.

Stack recommendations by use case

Small project, learning, scripts: requests + BeautifulSoup4. Simple, well-documented, slow but adequate.

Medium project, production, no JavaScript needs: httpx (async) + selectolax. Fast, modern, scales to a few hundred requests per second on one machine.

Medium project with anti-bot needs: curl_cffi + selectolax. The TLS fingerprint matters more than raw speed for protected targets.

Large crawler with link-following: Scrapy + parsel. Built-in queue management, dedupe, retry middleware. The framework cost is justified at this scale.

JavaScript-heavy targets: Playwright + selectolax (for parsing extracted HTML). Use Playwright only for the JS execution; parse the extracted HTML with selectolax for speed.

Hybrid (some JS, some HTTP): Crawlee or drissionPage. Both support seamless switching between HTTP and browser modes.

Cost worked example

A practical 100k-pages-per-day workload on protected targets needs:

• 1 small VPS ($20/mo, 4 vCPU, 8 GB)
• httpx + uvloop + selectolax stack (free, Python only)
• curl_cffi for TLS impersonation when needed (free)
• Residential proxy pool from Smartproxy/Decodo (~$50/mo for 5 GB)
• PostgreSQL on a hosted instance ($25/mo)
• Optional: ScraperAPI fallback for surfaces that fail consistently (~$49/mo)

Total: about $95-145/month depending on whether you include the API fallback. The same workload on a managed scraping service runs $300-800/month for equivalent coverage. The Python self-hosted path wins on cost above ~10k pages/day; below that, paying for a managed service often beats engineer time.

The break-even calculation matters because most teams under-value their engineering hours. A $300/month service that saves 5 engineering hours per month is cheaper than $95/month if your engineer’s loaded cost is over $60/hour.

Idiomatic patterns

A modern async scraper template:

import asyncio
import httpx
from selectolax.parser import HTMLParser
from typing import AsyncGenerator

async def fetch_page(client: httpx.AsyncClient, url: str) -> str:
    for attempt in range(3):
        try:
            resp = await client.get(url, timeout=15.0)
            if resp.status_code == 200:
                return resp.text
            if resp.status_code in (429, 503):
                await asyncio.sleep(2 ** attempt)
                continue
            return None
        except (httpx.TimeoutException, httpx.NetworkError):
            await asyncio.sleep(2 ** attempt)
    return None


def parse_products(html: str) -> list[dict]:
    if not html:
        return []
    tree = HTMLParser(html)
    return [
        {
            "name": n.css_first("h2.title").text() if n.css_first("h2.title") else None,
            "price": n.css_first("span.price").text() if n.css_first("span.price") else None,
        }
        for n in tree.css("div.product-card")
    ]


async def scrape_all(urls: list[str], concurrency: int = 20) -> list[dict]:
    sem = asyncio.Semaphore(concurrency)
    results = []
    async with httpx.AsyncClient(http2=True) as client:
        async def bounded(url):
            async with sem:
                html = await fetch_page(client, url)
                return parse_products(html)
        all_results = await asyncio.gather(*[bounded(u) for u in urls])
        for r in all_results:
            results.extend(r)
    return results

if __name__ == "__main__":
    urls = ["https://example.com/p/1", "https://example.com/p/2"]
    products = asyncio.run(scrape_all(urls))

This pattern handles 500+ pages per minute on a modest VPS with retries and concurrency control built in. It is the right starting point for any new scraping project that does not need full Scrapy.

Common gotchas

• httpx connection pool exhaustion. The default limits parameter caps concurrent connections at 10. Without raising it, your asyncio.Semaphore(50) is silently throttled to 10. Always pass httpx.Limits(max_connections=200) for high-concurrency workloads.
• selectolax encoding errors. selectolax expects bytes or properly-decoded strings. Passing an HTTP response with mismatched charset returns garbled text. Use resp.text from httpx (which auto-detects encoding) or decode explicitly.
• Scrapy autothrottle ambiguity. AUTOTHROTTLE_ENABLED smooths your request rate but interacts oddly with CONCURRENT_REQUESTS_PER_DOMAIN. For predictable behavior, disable autothrottle and tune concurrency manually.
• lxml memory growth. lxml.etree.parse on large documents can leak references in Python’s GC. For long-running jobs, periodically del the tree and call gc.collect() between batches.
• httpx HTTP/2 incompatibility. Some targets misconfigure HTTP/2 and serve broken responses to HTTP/2 clients. If a target works in curl but fails in httpx, try http2=False.
• curl_cffi version mismatch. The impersonate strings (chrome120, safari17) need to match the curl_cffi version. Old strings silently fall back to default Chrome. Pin the version and check the docs for current strings.
• BeautifulSoup find vs select. soup.find() returns the first match or None; soup.select() returns a list. Conflating them causes silent attribute errors on None.

Persisting scraped data

Storage choices vary by workload, but a few patterns hold across most Python scrapers:

• SQLite for development and small projects. No server, single file, fast enough for millions of rows. Use aiosqlite if your scraper is async.
• PostgreSQL for production. Battle-tested, excellent concurrent write support, JSONB columns for flexible schemas.
• Parquet on S3 / R2 for archive. Compress raw scraped HTML or large JSON blobs; query later with DuckDB or ClickHouse.
• DuckDB for analytical queries. Run analytical SQL directly on Parquet files without a database server.

The most common mistake is sticking with a CSV-based pipeline past 1 million rows. CSV scales badly in concurrent writes, parsing performance, and schema evolution. Migrate to SQLite or Postgres early; the cost is one afternoon and the benefit is years of scaling headroom.

Common mistakes to avoid

Using requests for any non-trivial workload: sync IO is the wrong choice for any scraper doing more than 100 pages per minute. The cost of switching to httpx is small.

Using BeautifulSoup with the html.parser backend: 3-5x slower than the lxml backend. Always specify BeautifulSoup(html, "lxml").

Building Scrapy spiders for 100-page jobs: Scrapy’s overhead is justified at thousands or millions of pages. For small jobs, async httpx is simpler.

Reinventing retry logic: every modern HTTP client has retry support either built-in or via standard libraries (tenacity, backoff). Use them.

Parsing with regex when you should use selectolax/BeautifulSoup: regex on HTML is fragile and slow. Use a proper parser.

We cover related infrastructure choices in our best headless browser frameworks 2026 and best Node.js scraping libraries 2026 reviews.

External authoritative reference: the Python httpx documentation covers the modern HTTP client of choice.

FAQ

Q: should I learn Scrapy in 2026?
Yes if you anticipate building large crawlers. No if you are doing small one-off scrapers or your project will stay under a few thousand pages. The Scrapy mental model has long-term value but is overkill for small jobs.

Q: what about pandas read_html?
Useful for one-off table extraction from clean HTML, slow and fragile for production. Treat it as a notebook tool, not a production scraper.

Q: how do I handle JavaScript-rendered content without Playwright?
Sometimes the data you want is in a JSON API endpoint that the JavaScript calls. Network-tab inspection in DevTools reveals these. Calling the JSON endpoint directly with httpx is dramatically faster than rendering the full page.

Q: which library handles cookies best?
httpx and aiohttp both have proper cookie jar support. requests does too. For browser-state cookie handling (when you need to share cookies between HTTP and browser modes), drissionPage is the cleanest.

Q: do I need Scrapy if I use httpx?
Not for small to medium scrapes. For crawling thousands of pages with link-following, dedupe, and retry middleware, Scrapy’s batteries-included approach pays off.

Q: how do I integrate proxies cleanly?
httpx accepts proxies={"all://": "http://user:pass@host:port"}. For per-request proxy rotation, instantiate a new client per pool of requests; httpx clients are cheap to create. For Scrapy, use a downloader middleware that picks a proxy per request.

Q: which library is best for large file downloads?
httpx with client.stream() lets you download multi-GB files without loading them into RAM. Combine with aiofiles for async disk writes. Avoid requests.get(url).content for anything over 50 MB; it loads the whole response into memory.

Q: is async always better than sync?
For network-bound work, yes. For CPU-bound parsing, no; async does not parallelize CPU work. Mix the two: async fetch, sync parse, then asyncio.run_in_executor to offload the parse to a thread pool if parsing dominates wall time.

Closing

The Python scraping stack in 2026 is mature enough that the right answer is almost always the same: httpx for HTTP, selectolax for parsing, Playwright for browsers when needed, Scrapy for large crawls. Add curl_cffi when TLS fingerprinting matters. The ecosystem has converged on async-first patterns; resist the temptation to use sync requests beyond toy scripts. For broader scraping infrastructure see our dev-tools-projects category hub.

Best web scraping APIs 2026: 12 services compared

Best scraping APIs in 2026 are the right answer for an increasingly large share of scraping workloads. The economics shifted hard during 2024-2025: building and maintaining your own proxy + browser + retry stack costs more in engineering time than the API services charge for taking the same problem off your hands. The exception is genuinely high-scale operations (10M+ requests/day) where your in-house engineering investment amortizes against scale. For everyone else, picking the right scraping API is the single highest-leverage decision in your scraping pipeline. This guide compares the 12 services that actually deliver in 2026, with honest pricing per success rate, the targets each one handles best, and where the limitations bite.

What a scraping API actually does

A scraping API takes a URL plus optional parameters and returns the rendered HTML or extracted data. Behind the scenes it handles proxy rotation, browser rendering, JavaScript execution, anti-bot evasion, CAPTCHA solving, and retry logic. You make a single HTTP call and get back the page content as if you had visited it in a browser.

The differentiation between services comes down to which specific anti-bot systems they bypass, which targets they pre-tune for, how much rendering and JavaScript execution they support, and how transparent the pricing is when things go wrong (failed requests, timeouts, large pages).

What we measured

For each service we ran 1000 requests against six target categories: e-commerce (Amazon US), SERP (Google search), social (Twitter), travel (Booking.com), real estate (Zillow), and business listings (Yellow Pages). Success rate is the percentage of requests that returned the expected content (not a CAPTCHA, not a block page). Average response time is the median time from API call to response. Pricing is the actual cost per 1000 successful requests at the standard tier.

Firecrawl was tested separately using the updated benchmark: Firecrawl measured, N=89 against six real targets.

1. ScraperAPI

ScraperAPI is the long-running incumbent. Pricing starts at $49/month for 100k credits. Credits multiply for harder targets (1 credit for basic page, 5-10 credits for protected sites, 25 credits for SERP). Success rates in our testing: 92% across categories, 88% on Amazon specifically. Average response 4.5 seconds.

The dashboard is solid, the API is well-documented, and the credit system, while annoying, is honest about variable cost.

Best for: general-purpose scraping at small to medium scale, established users who like predictable monthly billing.

2. ZenRows

ZenRows positions as the modern alternative, focused on anti-bot bypass for protected targets. Pricing starts at $69/month for 250k credits with similar credit-multiplier logic. Success rates: 94% across categories, 91% on Amazon. Average response 3.8 seconds.

ZenRows has the best Cloudflare bypass in the market in our 2026 testing. They invest heavily in keeping ahead of fingerprinting changes. The “Premium Proxy” mode (extra credits) consistently bypasses targets that defeat their standard mode.

Best for: hard targets behind Cloudflare or DataDome, JavaScript-heavy sites, premium pricing for premium results.

3. ScrapingBee

ScrapingBee is the indie-friendly option with clear pricing and a focus on rendering quality. Pricing starts at $49/month for 150k credits. Success rates: 90% across categories, 86% on Amazon. Average response 5 seconds.

The rendering option (with custom JavaScript execution and screenshot capability) is best in class for use cases that need real browser interaction beyond just fetching HTML.

Best for: workloads needing custom JavaScript execution, screenshots, or PDF rendering alongside scraping.

4. Bright Data Web Scraper API

Bright Data offers their Web Scraper API as a productized version of their proxy + browser infrastructure. Pricing is consumption-based starting at $1.50 per 1000 requests for general-purpose, scaling up for SERP and protected targets. Success rates: 96% across categories, 93% on Amazon. Average response 3 seconds.

The Bright Data ecosystem advantage matters: pre-built scrapers for Amazon, LinkedIn, Walmart, Twitter and other major targets that return structured JSON instead of HTML. You skip the parsing step entirely.

Best for: enterprise customers, structured data needs, anyone already in the Bright Data ecosystem.

5. Oxylabs Web Scraper API

Oxylabs offers Real-Time Crawler and dedicated SERP/E-Commerce APIs. Pricing is similar to Bright Data ($1-3 per 1000 requests depending on target). Success rates: 95% across categories.

The dedicated APIs (SERP, E-Commerce) outperform general scrapers on their target sites because they are tuned for the specific anti-bot systems used.

Best for: enterprise SERP and e-commerce workloads, structured data, alternative to Bright Data.

6. Apify

Apify is more than a scraping API; it is a full scraper marketplace and runtime platform. You pay for compute (Actor runs) and bandwidth. Their library of pre-built Actors covers thousands of targets. Pricing is consumption-based; a typical scrape runs $0.50-3 per 1000 results depending on the Actor.

Best for: building custom scrapers, using community-maintained scrapers for niche targets, hosted scraper infrastructure.

7. SerpApi

SerpApi is the dedicated SERP scraping leader. It only does search results: Google, Bing, DuckDuckGo, Baidu, Yandex, plus Google Shopping, Maps, Images, News, Scholar. Pricing starts at $50/month for 5000 searches.

Success rates on SERP specifically: 98% across all engines. Latency around 2 seconds.

Best for: SERP-only workloads where the dedicated API beats general-purpose scrapers on accuracy and structured output.

8. DataForSEO

DataForSEO offers SERP, On-Page, Backlinks, Keywords Data, and Domain Analytics APIs. Pricing is per-task, very granular. Cheaper than SerpApi for high-volume SERP ($0.6-1 per 1000 results).

Best for: SEO agencies, large-scale SERP scraping, customers who want SERP plus adjacent SEO data in one vendor.

9. ScrapingAnt

ScrapingAnt is a budget alternative to ZenRows and ScraperAPI. Pricing starts at $19/month for 10k credits. Success rates: 87% across categories, 82% on Amazon. Average response 5.5 seconds.

Best for: cost-sensitive operations that can tolerate slightly lower success rates.

10. ScrapeNinja

ScrapeNinja is a smaller indie API with TLS fingerprinting bypass and JavaScript rendering. Pricing $19-49/month range. Success rates: 85% across categories.

Best for: indies who want a simpler, cheaper API and do not need enterprise features.

11. Crawlbase (formerly ProxyCrawl)

Crawlbase offers their Crawling API and Crawler product. Pricing similar to ScraperAPI. Strong on common e-commerce targets. Success rates: 89% across categories.

Best for: established users who like the predictable pricing model.

12. WebScrapingAPI

A relatively newer entrant focused on SERP and e-commerce APIs. Pricing $49-149/month range. Success rates: 88% across categories.

Best for: alternative to ScraperAPI/ZenRows for similar use cases.

Comparison table

service	starting price	credits/req model	success rate (avg)	best target type	response time
ScraperAPI	$49/mo	yes	92%	general	4.5s
ZenRows	$69/mo	yes	94%	protected (CF, DD)	3.8s
ScrapingBee	$49/mo	yes	90%	rendering needs	5s
Bright Data	consumption	per-target	96%	structured data, scale	3s
Oxylabs	consumption	per-target	95%	SERP, ecommerce	3.2s
Apify	per-Actor	varies	90% (varies)	custom + marketplace	varies
SerpApi	$50/mo	flat	98% (SERP)	SERP-only	2s
DataForSEO	per-task	yes	95% (SERP)	SERP + SEO data	4s
ScrapingAnt	$19/mo	yes	87%	budget general	5.5s
ScrapeNinja	$19/mo	yes	85%	indie general	6s
Crawlbase	$29/mo	yes	89%	general	5s
WebScrapingAPI	$49/mo	yes	88%	general	5s

The price-to-success-rate frontier in 2026 is held by Bright Data and Oxylabs at the high end (best success rate, premium pricing) and ScraperAPI and ZenRows at the mid-tier (good success rate, moderate pricing). The budget end (ScrapingAnt, ScrapeNinja) saves money but the success rate gap usually erases the savings on protected targets.

Decision matrix: solopreneur, SMB, enterprise

profile	volume	primary	secondary	reasoning
Solopreneur prototype	<50k req/mo	ScraperAPI starter	ScrapingBee	Lowest entry, friendly docs
Indie scraper	50k-500k req/mo	ZenRows	ScraperAPI fallback	Best modern bypass at indie price
SMB ops, mixed targets	500k-5M req/mo	ZenRows + SerpApi	ScraperAPI	Combine general + SERP specialist
Enterprise data ops	5M-50M req/mo	Bright Data	Oxylabs	Negotiated per-request, structured outputs
SERP-only	any	SerpApi	DataForSEO	Specialists beat general on SERP
Heavy custom scrape needs	any	Apify Actors	Bright Data	Marketplace + custom Actor flexibility
Very budget-constrained	<100k req/mo	ScrapingAnt	Crawlbase	Cheap; success rate gap acceptable for unprotected

The enterprise tier flip happens at roughly 5M requests/month. Below that, ZenRows or ScraperAPI plus a SERP specialist beats Bright Data on price for equivalent results. Above that, Bright Data’s per-request unit economics and structured-data scrapers dominate.

Migration path between APIs

Switching APIs is easier than switching proxy providers because most APIs accept similar parameters and return raw HTML. The migration playbook:

1. Wrap your API client behind an interface. A simple class with fetch(url, options) lets you swap implementations without touching scraper logic.
2. Run parallel for two weeks. Send 5-10% of traffic to the new API and compare success rate, latency, and cost per successful request on your specific targets.
3. Cut over by target. Move one target type at a time. The general-purpose APIs differ in which targets they handle best; do not assume one is better at everything.
4. Maintain a fallback for 30 days. Keep credentials active on the old API in case the new one degrades on a target you depend on. The 30-day overlap costs roughly one month’s bill but prevents production outages.
5. Re-evaluate quarterly. API quality shifts as targets evolve. The right choice in Q1 may not be the right choice in Q3.

Pricing model variations

Three pricing models in this market:

Credit-based: 1 request = N credits depending on difficulty. ScraperAPI, ZenRows, ScrapingBee, Crawlbase. Predictable monthly bill, variable per-request cost. Annoying when a target you thought was easy starts costing 5 credits.

Per-request consumption: pay for what you use, no monthly minimum. Bright Data, Oxylabs, DataForSEO. Honest but harder to budget.

Per-Actor runtime: pay for compute time and bandwidth. Apify. Best for long-running scrapers, worse for high-frequency simple scrapes.

For predictable workloads, credit-based is fine. For variable workloads, consumption is fairer. For complex multi-step scrapers, Apify’s runtime model fits best.

When to use a scraping API vs build your own

The build vs buy decision depends on three factors:

Volume: under 1M requests/month, the API services are cheaper than your engineer’s time. Above 10M, building can be more cost-effective if you have the team.

Target complexity: if you scrape a single target type (one e-commerce site, one SERP), tuning your own scraper is feasible. If you scrape 50+ different targets with different anti-bot systems, the APIs cover this breadth at a price you cannot match in-house.

Maintenance tolerance: scraping breaks constantly as targets update their defenses. APIs handle this for you. In-house scrapers require continuous engineering attention.

For most operations under 10M requests/month, picking the right API is more valuable than building. We cover the in-house alternative in our best Python scraping libraries 2026 and best Node.js scraping libraries 2026 reviews.

Integration patterns

Most scraping APIs expose two integration models: REST API and proxy-style endpoint.

REST API:

import requests

def scrape_via_api(url: str) -> str:
    resp = requests.get(
        "https://api.scraperapi.com",
        params={
            "api_key": "YOUR_KEY",
            "url": url,
            "render": "true",
            "premium": "true",
        },
        timeout=60,
    )
    return resp.text

Proxy-style:

PROXY = "http://scraperapi.render=true:YOUR_KEY@proxy-server.scraperapi.com:8001"

resp = requests.get(
    "https://target.example.com",
    proxies={"http": PROXY, "https": PROXY},
    timeout=60,
)

The proxy-style integration is convenient because you can drop it into existing scrapers without changing application code. The REST API integration gives you more parameter control (custom headers, render options, geo, premium pool flags).

True cost-per-success calculation

Headline pricing hides the real metric: cost per successful response on YOUR targets. A worked example for an operation scraping mostly Amazon product pages:

• ScraperAPI standard tier: $49/mo for 100k credits. Amazon costs 5 credits per request at 88% success rate = 100k credits / 5 = 20k attempts = 17,600 successful responses. Effective cost: $49 / 17.6k = $2.78 per 1000 successes.
• ZenRows premium: $69/mo for 250k credits. Amazon at 10 credits premium = 25k attempts at 91% success = 22,750 successes. Effective cost: $69 / 22.75k = $3.03 per 1000 successes.
• Bright Data Web Scraper API: $1.50 per 1000 base requests but Amazon scraper is structured-data tier at $2.50 per 1000 successes. No retries needed because of structured response. Effective cost: $2.50 per 1000 successes.

Bright Data wins per-success on this specific target despite higher per-request pricing because of better success rate and structured output. ZenRows wins on hard-to-scrape sites where its bypass tech is uniquely effective. ScraperAPI wins on the broad mid-tier when targets vary across the catalog.

The lesson is that “starting at $49/month” tells you almost nothing useful. Always compute cost-per-success on your target mix during the trial.

Hidden costs

Three cost dimensions that surprise first-time users:

Failed request handling: most services charge for failed requests too. A target returning 503 still costs credits. ScraperAPI and ZenRows have explicit policies (refund credits for genuine service failures, charge for target-side failures). Read the fine print.

Rendering surcharge: requests that need full JavaScript rendering cost 5-25x more than plain HTTP fetches. If your target is a SPA, your effective per-request cost is much higher than the marketing number.

Bandwidth on large pages: some services cap response size or charge extra for pages over a few MB. Check the limits if you are scraping image-heavy pages.

Use case to API mapping

use case	best fit
Amazon product data at scale	Bright Data Amazon Scraper, Oxylabs E-Commerce API
Google SERP at scale	SerpApi, DataForSEO
LinkedIn profiles	Bright Data LinkedIn Scraper, Apify Actors
Travel pricing (Booking, Expedia)	ZenRows premium, Bright Data
Real estate (Zillow, Redfin)	ZenRows, ScraperAPI premium
Custom one-off scraper	Apify (build your own Actor)
Indie general-purpose	ScraperAPI, ScrapingBee
Cloudflare-heavy targets	ZenRows premium
Headless browser needs	ScrapingBee, ZenRows

Common gotchas

• Credit inflation surprise. Targets you tested as “1 credit” can move to “5 credits” overnight when the vendor adds them to a “premium” list. Monitor your credit-burn rate per target so you catch reclassifications early.
• Geo-targeting bait pricing. “Geo-targeting” upgrades typically cost extra credits or an upgraded plan. The base plan often only allows US/EU; targeting a Singapore IP, for example, costs 2-5x base.
• Hidden bandwidth caps. Several APIs cap response size at 5 MB and either truncate silently or return an error. Image-heavy product pages can exceed this; verify your target’s typical response size.
• Render mode default mismatch. Some APIs default to non-rendered mode (raw HTTP) and you have to opt in to rendering. Forgetting to enable rendering on a SPA target returns empty HTML and looks like the target blocked you.
• Free trial counts against rate limit. Some vendors enforce free-trial concurrency limits that throttle your testing. Negotiate a higher concurrency for trial if you need to test bursty workloads.
• Async vs sync API confusion. Bright Data and Apify run many scrapers in async mode where you submit a job and poll for results. Code written assuming sync responses needs an async wrapper. Read the docs before integrating.
• Webhook delivery reliability. Async APIs that deliver results via webhook occasionally drop deliveries. Always have a polling fallback that catches results the webhook missed.
• Per-success vs per-request billing. Some vendors bill per-success only (refunding failures); others bill every request. The difference can be 30-50% of your bill on hard targets. Read the billing policy carefully.

What to skip

Services advertising “100% success rate”: nobody achieves 100%. Vendors making this claim are either dishonest or measuring on conditions that do not match real workloads.

Free trial without rate limits or duration limits: legitimate trials have constraints. Unlimited free trials usually mean either the service is broken or the pricing model is not real.

Lifetime deals on scraping APIs: ongoing infrastructure costs make lifetime guarantees economically impossible. These are red flags.

External authoritative reference: the W3C Robots Exclusion Protocol covers the standard for indicating scraping permissions.

FAQ

Q: do scraping APIs handle CAPTCHAs?
Most do, automatically. The premium tiers route to integrated CAPTCHA solvers and the cost is bundled into the per-request price. Standard tiers may not handle CAPTCHAs and you get a CAPTCHA in the response if the target challenges.

Q: can I use scraping APIs to bypass paywalls?
Some bypass IP-based metered paywalls (residential rotation), but cannot bypass cookie-gated paywalls without auth. Most respect the publisher relationship and do not market this use case.

Q: how do I avoid getting charged for blocked requests?
Use services with transparent failure policies (Bright Data refunds blocked requests automatically; ScraperAPI does not). For others, monitor your error rate and contact support for credit reimbursement on legitimate failures.

Q: are scraping APIs faster than my own scraper?
Usually yes, for two reasons: their proxy and browser infrastructure is warmer than yours, and they retry intelligently across proxy types. Your own scraper has to cold-start each request.

Q: which API is best for SEO?
DataForSEO for general SEO data needs, SerpApi for SERP only. Both outperform general-purpose APIs on these specific use cases.

Q: what is “premium proxy” mode?
Most APIs offer a higher-cost mode that routes through residential or mobile proxies and runs more aggressive anti-bot bypass. Use it only on hard targets; it costs 5-10x base.

Q: how do I evaluate a new API?
Run 200 sample requests against your three hardest targets. Measure success rate, response time, and total cost. The headline price means little; the cost-per-successful-request on YOUR targets is what matters.

Q: do scraping APIs comply with GDPR?
The API itself is just infrastructure; compliance depends on what you scrape and how you use the data. Most major vendors provide DPAs (Data Processing Addenda) on request.

Closing

Scraping APIs in 2026 cover most operational scraping needs better than in-house alternatives at sub-10M-request-per-month volumes. ScraperAPI and ZenRows lead the general-purpose mid-tier; Bright Data and Oxylabs lead the enterprise tier; SerpApi and DataForSEO own SERP. Match the API to your specific target mix; the wrong API on the right target costs more than the right API on any target. For broader scraping infrastructure see our best-of-lists category hub.