The Developer’s Guide to Choosing a Scraping Stack in 2026

The scraping toolbox in 2026 is wider than it has ever been. You can write raw HTTP calls with Scrapy, spin up headless browsers with Playwright, deploy a managed API, or hand a prompt to an AI extractor and get structured JSON back. Every option works for some set of requirements. None of them works for all requirements. The wrong choice costs you a quarter of engineering time and a rewrite you could have avoided.

This guide walks through every major category of scraping tooling, breaks down the trade-offs that actually matter at production scale, and gives you a concrete decision matrix so you can map your requirements to a stack in minutes instead of weeks.

The five categories

Before comparing individual tools, it helps to understand the five distinct layers of the scraping ecosystem. Most teams end up combining tools from more than one category. The goal is to know which layer solves which problem so you don’t over-engineer (or under-engineer) any part of the pipeline.

1. DIY libraries you install and run yourself.
2. Open source frameworks that add orchestration, queuing, and retry logic on top of raw libraries.
3. Managed scraping APIs that handle proxies, anti-bot, and infrastructure for you.
4. AI-native extractors that convert pages to structured data using language models.
5. Browser automation platforms for JavaScript-heavy sites and interactive workflows.

1. DIY libraries

These are the building blocks. You install a package, write the fetch-parse-store loop, and handle everything else: retries, proxy rotation, rate limiting, error handling, storage.

Scrapy (Python)

Scrapy is the most mature general-purpose crawling framework in the Python ecosystem. It gives you an async event loop, middleware hooks for proxies and user agents, and a pipeline system for cleaning and storing data. The community is enormous, and there is a Scrapy plugin for almost every edge case.

When to use it. You have Python expertise on the team, the target sites are mostly static HTML, and you want fine-grained control over every stage of the crawl pipeline. Scrapy’s middleware architecture makes it straightforward to plug in custom proxy managers, deduplication logic, or export formats.

Typical cost. Free (MIT license). You pay for compute (a single machine can handle tens of thousands of pages per hour for static sites), proxies ($5 to $15 per GB for residential), and your own engineering time.

Scaling ceiling. Scrapy scales vertically well. Horizontal scaling (distributing work across machines) requires bolting on Scrapy-Redis, Scrapy-Cluster, or a custom job queue. At that point you are building infrastructure, not writing scraping logic.

Maintenance burden. Medium to high. You own proxy rotation, anti-bot bypass, HTML parser maintenance, and deployment. When a target site changes its DOM structure, your selectors break and you fix them manually.

Puppeteer and Playwright (Node.js)

Puppeteer controls Chrome via the DevTools Protocol (CDP). Playwright extends the same concept to Chromium, Firefox, and WebKit with a unified API. Both give you full browser control: click, scroll, fill forms, intercept network requests, take screenshots.

When to use it. The target requires JavaScript rendering, client-side hydration, or interactive workflows (login, pagination via infinite scroll, CAPTCHA interaction). Playwright is the stronger choice today because of its multi-browser support, auto-wait mechanics, and better TypeScript ergonomics.

Typical cost. Free (Apache 2.0). Compute costs are higher than HTTP-only approaches because each page launch spins up a real browser process. Expect 2x to 5x the CPU and memory of a static fetch.

Scaling ceiling. A single machine with 8 cores can run roughly 8 to 12 concurrent browser contexts before memory pressure becomes a problem. Scaling beyond that means managing a pool of browser instances across multiple machines, which tools like Browserless or Spider’s managed Chrome backends handle for you.

Maintenance burden. High. Browser versions drift, CDP protocol changes, anti-bot systems fingerprint headless browsers, and you need to keep your stealth patches current. The “it works on my laptop” to “it works in production at 10K pages/hour” gap is substantial.

Colly (Go)

Colly is a lightweight HTTP-based scraping framework for Go. It is fast, has a clean callback API, and compiles to a single binary with no runtime dependencies.

When to use it. Your backend is already in Go, the targets are static HTML, and you want a small, fast binary you can deploy anywhere. Colly’s concurrency model maps naturally to Go’s goroutines.

Typical cost. Free (Apache 2.0). Minimal compute overhead.

Scaling ceiling. High for static content. No built-in JavaScript rendering, so you need to pair it with a headless browser service (or a managed API) for dynamic sites.

Maintenance burden. Low for simple use cases. The ecosystem is smaller than Scrapy’s, so you write more custom code for edge cases.

spider crate (Rust)

The spider crate on crates.io is the open source core behind Spider’s managed platform. It provides async crawling, streaming, configurable concurrency, robots.txt compliance, and sitemap parsing.

When to use it. You need maximum throughput per machine, you are comfortable with Rust, and you want the same engine that powers a production SaaS without the SaaS pricing. The crate handles millions of pages per day on a single node when configured correctly.

Typical cost. Free (MIT license). Compute costs are low, but you pay in compile times and a steeper learning curve. The async runtime is efficient at saturating network I/O, which is where scraping bottlenecks live.

Scaling ceiling. The highest of any single-process library. The async runtime (Tokio) saturates network I/O before CPU becomes the bottleneck in most scenarios.

Maintenance burden. Medium. Rust’s compile times are slower than scripting languages, and the learning curve is steeper. But once compiled, the binary is rock-solid in production with minimal runtime surprises.

2. Open source frameworks

Frameworks sit one level above raw libraries. They add orchestration, queuing, automatic retries, and often a web UI for monitoring. You still self-host, but you skip reimplementing the plumbing.

Crawl4AI

Crawl4AI is a Python framework designed specifically for AI data pipelines. It wraps browser automation with built-in markdown conversion, chunking strategies, and LLM-ready output formats.

When to use it. You are building a RAG pipeline or fine-tuning dataset and want a Python-native tool that outputs clean markdown without a custom parsing step. Crawl4AI’s chunking strategies (by token count, by semantic boundary) save time when the next stop is a vector database.

Typical cost. Free (open source). Compute plus proxies.

Scaling ceiling. Moderate. It is single-machine by default and relies on Playwright under the hood, so you inherit Playwright’s memory-per-browser constraints. Large-scale crawls require external orchestration.

Maintenance burden. Medium. The project is younger than Scrapy or Crawlee, so expect faster iteration and occasional breaking changes.

Crawlee

Crawlee (from the Apify team) is a Node.js and Python framework for building reliable crawlers. It provides automatic request queuing, session management, proxy rotation hooks, and pluggable browser backends (Playwright, Puppeteer, or plain HTTP).

When to use it. You want a batteries-included Node.js or Python crawler with production-grade error handling and you plan to self-host (or optionally deploy to Apify’s cloud). Crawlee’s AutoscaledPool handles concurrency tuning automatically.

Typical cost. Free (MIT license) when self-hosted. Apify cloud adds per-compute-unit pricing if you deploy there.

Scaling ceiling. Good for single-machine workloads. Apify cloud removes the ceiling if you are willing to pay for managed infrastructure.

Maintenance burden. Low to medium. Apify maintains the framework actively, and the plugin system means you can swap components without rewriting your crawler.

Spider OSS

The open source Spider project (the spider Rust crate plus its CLI and library bindings) sits here as well. It functions as both a library (category 1) and a framework: it includes built-in concurrency management, crawl budgeting, sitemap-driven discovery, robots.txt compliance, and streaming output. Python and JavaScript bindings let you use it from higher-level languages while keeping the Rust engine underneath.

When to use it. You want framework-level features (auto-discovery, crawl budgets, rate limiting) with library-level performance, and you are willing to self-host. It is also the natural on-ramp to Spider’s managed API: same configuration, same output formats, zero migration cost when you decide to stop managing infrastructure.

Typical cost. Free (MIT license).

Scaling ceiling. Very high. The Rust runtime handles tens of thousands of concurrent connections on modest hardware.

Maintenance burden. Low once deployed. The binary has no runtime dependencies beyond libc and OpenSSL.

3. Managed scraping APIs

Managed APIs handle the hardest parts of scraping at scale: proxy pools, anti-bot bypass, browser farms, IP rotation, and retry logic. You send a URL, you get data back. The trade-off is cost per page and vendor lock-in.

Spider

Website: spider.cloud

Spider’s managed API runs the same Rust engine described above, backed by a global proxy network, managed Chrome and Firefox browser backends, and built-in anti-bot bypass for Cloudflare, Akamai, Imperva, and other major WAFs.

When to use it. You need production-grade scraping without managing infrastructure. The API returns clean markdown, raw HTML, or structured JSON via a natural-language prompt.

Typical cost. Pay-as-you-go with no monthly minimums. A typical production workload averages around $0.65 per 1,000 pages (varies by site complexity and proxy needs). See spider.cloud/credits for the full breakdown.

Scaling ceiling. High. The smart mode automatically picks HTTP or Chrome per page to minimize cost and maximize throughput.

Maintenance burden. Near zero. Proxy rotation, anti-bot updates, browser version management, and infrastructure scaling are handled by the platform.

Firecrawl

Website: firecrawl.dev

Firecrawl focuses on converting web pages to LLM-ready markdown. It handles JavaScript rendering and returns clean output suitable for RAG pipelines.

When to use it. You want markdown output and are comfortable with a higher per-page cost in exchange for simplicity. Firecrawl’s API surface is small and easy to integrate.

Typical cost. Free tier includes 500 credits. Paid plans start at $16/month for 3,000 credits. At the Starter tier, cost works out to roughly $5.33 per 1,000 pages. Higher tiers reduce the per-page cost but still remain above $1 per 1,000 pages.

Scaling ceiling. Limited concurrency on lower tiers. The Growth plan ($83/month) unlocks higher rate limits, but throughput numbers are not published.

Maintenance burden. Low. Same managed-API model as Spider, though with fewer output format options. The self-hosted version is AGPL-licensed, which requires releasing modifications if you serve it as a service.

ScrapingBee

Website: scrapingbee.com

ScrapingBee is a straightforward proxy-plus-rendering API. Send a URL, get HTML back. Proxy rotation and CAPTCHA solving happen behind the scenes.

When to use it. You need reliable HTML fetching with proxy management and you will handle parsing yourself. ScrapingBee’s API is simple and well-documented.

Typical cost. Plans start at $49/month for 250,000 credits. JavaScript rendering consumes 5 credits per request, so the effective rate for JS-rendered pages is around $0.98 per 1,000 pages. Stealth proxy mode jumps to $14.70 per 1,000 pages due to a 75x credit multiplier.

Scaling ceiling. Good for moderate volumes. No streaming output, no markdown conversion, no AI extraction built in.

Maintenance burden. Low for the API itself. But you own the parsing layer, which means maintaining CSS selectors and data cleaning code.

Crawlbase

Website: crawlbase.com

Crawlbase (formerly ProxyCrawl) offers a scraping API with a crawler, a screenshot API, and a leads database. It supports JavaScript rendering and has a simple per-request pricing model.

When to use it. You need a basic scraping API with predictable per-request pricing and you do not need AI-optimized output formats.

Typical cost. Pay-per-request pricing. Standard requests start at $0.003 each; JavaScript-rendered requests at $0.01 each. That works out to roughly $3 to $10 per 1,000 pages depending on the mix.

Scaling ceiling. Moderate. The platform handles proxies and rendering but does not publish throughput benchmarks.

Maintenance burden. Low. You still own parsing and data transformation.

Bright Data

Website: brightdata.com

Bright Data is the largest proxy network in the industry, with over 72 million residential IPs. It also offers a Web Scraper API, a browser API, and pre-built datasets.

When to use it. You need the widest possible proxy coverage (specific geolocations, mobile IPs, ISP-level targeting) or you want to buy pre-scraped datasets rather than crawling yourself. Bright Data’s proxy infrastructure is genuinely unmatched.

Typical cost. Proxy bandwidth starts at $5.04 per GB for datacenter and $8.40 per GB for residential. The Web Scraper API charges per record, starting around $2.85 per 1,000 records for common targets. Monthly minimums apply on most plans.

Scaling ceiling. Very high for proxy-based workloads. The infrastructure handles enterprise-scale traffic.

Maintenance burden. Medium. The product surface is large and complex. Configuration options are extensive, which means a steeper onboarding curve. Pre-built scrapers break when target sites change, and you depend on Bright Data to update them.

4. AI-native extractors

These tools use language models to extract structured data from web pages. Instead of writing CSS selectors or XPath expressions, you describe what you want in natural language (or a JSON schema) and the model figures out where to find it.

Spider (prompt-to-JSON)

Spider’s extraction mode lets you send a natural-language prompt alongside a URL. The platform fetches the page, processes it through its internal extraction models, and returns structured JSON matching your request. No selectors, no parsing code.

When to use it. You need structured data from pages whose DOM structure varies or changes frequently. The prompt-based approach eliminates selector maintenance entirely. Spider’s extraction models run internally (not via third-party LLM APIs), so there are no per-token costs layered on top of the crawl cost.

Typical cost. Same pay-as-you-go pricing as standard Spider crawls, with extraction credits added per page. Substantially cheaper than running pages through an external LLM API, because the extraction model is purpose-built and runs on Spider’s own infrastructure.

Scaling ceiling. Same as the managed API: 50,000 requests per minute, with extraction running in-line.

Maintenance burden. Minimal. When a site redesigns, your prompt stays the same. The model adapts to the new layout without selector updates.

Diffbot

Website: diffbot.com

Diffbot uses computer vision and NLP to automatically identify and extract structured data (articles, products, discussions) from any web page. It maintains a Knowledge Graph of the entire public web.

When to use it. You need entity extraction at scale (company data, product catalogs, news articles) and you want pre-structured output without writing extraction logic. Diffbot’s Knowledge Graph is useful for enrichment workflows.

Typical cost. Plans start at $299/month for 5,000 requests. That works out to roughly $59.80 per 1,000 pages, making it one of the most expensive options for raw page extraction. The Knowledge Graph API has separate pricing.

Scaling ceiling. High. Diffbot’s infrastructure handles large volumes, but the per-page cost means it is best suited for high-value extraction tasks rather than broad crawling.

Maintenance burden. Low for supported page types (articles, products, events). Custom extraction schemas require more configuration.

ScrapeGraphAI

Website: scrapegraphai.com

ScrapeGraphAI is an open source Python library that builds scraping pipelines using LLM-driven graph logic. You describe what you want, and the library generates a directed graph of scraping steps powered by language models.

When to use it. You want to experiment with LLM-driven scraping in a Python notebook. The graph-based approach is interesting for complex, multi-step extraction tasks where the scraping logic itself benefits from LLM reasoning.

Typical cost. Free (MIT license) plus your LLM API costs. Running GPT-4o or Claude on every page adds $0.01 to $0.10 per page depending on page size and model choice.

Scaling ceiling. Limited by LLM API throughput and cost. Not designed for high-volume crawling.

Maintenance burden. Medium. You manage the LLM API keys, prompt engineering, and any custom graph nodes. The library is under active development.

5. Browser automation platforms

For sites that require full browser interaction (login flows, infinite scroll, dynamic forms, CAPTCHA solving), you need a real browser. The question is whether you manage the browser infrastructure yourself or let someone else do it.

Selenium

Selenium is the original browser automation tool. It supports every major browser and language (Python, Java, JavaScript, C#, Ruby). Selenium Grid lets you distribute browser sessions across machines.

When to use it. You have an existing Selenium test suite and want to reuse it for scraping, or you need a specific browser/language combination that Playwright does not support. Selenium’s ecosystem is the largest.

Typical cost. Free (Apache 2.0). Infrastructure costs for running Selenium Grid at scale (VMs, Docker, Kubernetes) add up quickly.

Scaling ceiling. Selenium Grid scales horizontally, but the operational overhead is significant. Each browser instance consumes 500MB to 1GB of RAM.

Maintenance burden. High. WebDriver versions must match browser versions. Selenium’s architecture (HTTP-based WebDriver protocol) is slower than CDP or BiDi, and stealth is harder because the WebDriver flag is detectable.

Playwright (managed)

Beyond the open source library, several platforms offer managed Playwright infrastructure: Browserless, Playwright’s own cloud offering, and others. These give you Playwright’s API with someone else managing the browser pool.

When to use it. You want Playwright’s developer experience without managing browser infrastructure. Good for teams that have already written Playwright scripts and want to scale them without building a browser farm.

Typical cost. Varies by provider. Browserless starts at $200/month for 2,000 browser-hours. Per-page costs depend heavily on page complexity and session duration.

Scaling ceiling. Depends on the provider. Managed Playwright services typically cap at hundreds of concurrent sessions on lower tiers.

Maintenance burden. Lower than self-hosted, but you still own the scraping scripts and selector maintenance.

Spider’s Chrome and Firefox backends

Spider’s managed API includes both Chrome (via CDP) and Firefox (via BiDi/WebDriver) backends. When you set request: "chrome" or use the browser WebSocket endpoint, Spider provisions a browser instance, runs your page, and tears it down. You do not manage any browser infrastructure.

When to use it. You need browser rendering as part of a larger crawl workflow and do not want to manage browser pools. Spider’s smart mode automatically decides whether a page needs a browser or can be fetched with a lightweight HTTP request, saving cost on pages that do not require JavaScript execution.

Typical cost. $0.0003 per page for Chrome rendering on top of the base crawl cost. No separate browser-hours billing.

Scaling ceiling. Same as the API: 50,000 requests per minute. Browser instances are provisioned on-demand from a shared pool.

Maintenance burden. Zero. Browser versions, CDP/BiDi protocol changes, and stealth patches are handled by the platform.

The hidden costs

The sticker price of a scraping tool is almost never the full cost. Here are the expenses that show up after you commit to an approach.

Proxy management

If you self-host, you need proxies. Residential proxies cost $5 to $15 per GB. Rotating them, managing bans, and maintaining a pool of healthy IPs is a part-time job. Managed APIs fold this cost into their per-page rate, which is almost always cheaper at scale because the provider amortizes proxy costs across all customers.

Anti-bot maintenance

Cloudflare, Akamai, PerimeterX, and Imperva update their detection signatures regularly. If you self-host, you are in an arms race. Stealth plugins, browser fingerprint randomization, and TLS fingerprint spoofing all require ongoing attention. A managed API with built-in anti-bot bypass handles this for you. The question is whether the per-page premium is worth the engineering hours you save.

Infrastructure operations

Running a browser farm at scale means managing Docker containers (or Kubernetes pods), monitoring memory usage, handling zombie processes, and dealing with browser crashes. A single Chrome instance that hangs can block a worker and cascade into a queue backup. Managed APIs absorb this operational complexity entirely.

Parsing code maintenance

CSS selectors and XPath expressions are brittle. When a target site redesigns (or even tweaks a class name), your selectors break. AI-native extractors eliminate this category of maintenance entirely. Prompt-based extraction adapts to layout changes without code updates. Over a year of production operation, the maintenance hours saved on parsing code alone can justify the cost of a managed extraction service.

LLM API costs for AI extraction

If you use an external LLM (GPT-4o, Claude, Gemini) for extraction, the per-token cost adds up fast on large pages. A typical web page is 2,000 to 8,000 tokens. At $0.01 per 1K input tokens, that is $0.02 to $0.08 per page just for the LLM call. At scale, this can dwarf the crawling cost itself. Tools with built-in extraction models (like Spider) avoid this overhead because the model runs on internal infrastructure without per-token billing.

Decision matrix

This table maps common use cases to recommended stacks. Each row is a scenario; each column is the approach that best fits.

Decision flowchart

The following diagram walks through the key decisions that determine which scraping stack fits your situation.

Cost comparison at scale

To make the economics concrete, here is what 100,000 JS-rendered pages per month costs across the major managed APIs.

Spider’s cost advantage compounds at higher volumes. The 30% credit bonus on volume purchases up to $4,000 further reduces the effective per-page rate for teams operating at scale.

Putting it together

There is no single “best” scraping tool. There is only the best tool for your specific combination of scale, budget, technical capacity, and output requirements. Here is how to think about the decision.

If you are prototyping or scraping fewer than a few thousand pages, use whatever language you are most productive in. Scrapy for Python, Playwright for Node.js, Colly for Go, the spider crate for Rust. The cost is your time, and the goal is speed of iteration.

If you are building a production system that needs to run reliably every day, evaluate whether infrastructure maintenance is worth the cost savings. Managed APIs (Spider, Firecrawl, ScrapingBee) remove that burden but lock you into their pricing and rate limits. Spider’s open source core is a genuine differentiator here: if the managed service changes pricing or goes down, you can fall back to self-hosting the same engine.

If your pipeline feeds an LLM, the output format matters as much as the crawling itself. Spider and Firecrawl both produce markdown natively. Crawl4AI gives you more control over the conversion process at the cost of managing your own infrastructure. Pick based on how much operational overhead you are willing to carry.

If you need the absolute lowest cost per page and have engineering capacity, self-hosting any open source option (Scrapy, Crawl4AI, the spider crate) will beat managed APIs on per-page cost. The real question is whether your team’s time is cheaper than the managed API premium. For most teams, it is not.

The scraping ecosystem in 2026 has more good options than ever. The worst decision is spending a quarter building the wrong one. Map your requirements to the matrix above, start with the simplest tool that fits, and upgrade when you hit a real ceiling — not a hypothetical one.