TL;DR
- Context.dev is the top pick for AI and LLM pipelines, returning clean Markdown in around 247ms with a flat 1-credit-per-call price and native MCP integration.
- Firecrawl is the strongest open-source choice for LangChain and LlamaIndex crawl workflows.
- ScrapingBee works well as a quick API-first unblocking service, and Bright Data and Oxylabs lead on enterprise proxy depth and geo-targeting.
- Apify wins when a pre-built Actor already covers your target site, while Zyte suits SLA-backed crawls against heavily protected domains and ScraperAPI handles raw HTML on a budget.
- This list is for developers who feed JavaScript-rendered content into AI systems and want structured output without maintaining browser infrastructure.
Why JavaScript-Heavy Sites Break Most Scrapers
Around 68% of target sites now rely on client-side JavaScript to render their content, which means most of what a user sees never exists in the raw HTML the server sends. A modern single-page application ships a nearly empty document with a script bundle, and the browser executes that JavaScript to fetch data, build the DOM, and paint the page. Prices, reviews, product listings, and dashboards all materialize after the browser runs the code, not before.
Static HTTP fetchers request the page and read whatever the server returns immediately. On a React, Vue, or Angular site, that response is a shell. A requests.get() call or a plain cURL command hands you <div id="root"></div> and a link to a script file, and no amount of parsing turns that empty container into the pricing table you wanted. The scraper succeeds, the request returns a 200, and the data you came for isn't there.
Rendering the page requires a real browser engine that executes the JavaScript, waits for network calls to resolve, and produces the finished DOM. That engine is expensive to run and hard to maintain at scale, which is why headless browser infrastructure separates the tools that work on dynamic sites from the ones that quietly return nothing.
What you actually need is rendered output, not the empty shell, delivered in a format your pipeline can use directly. The eight tools below all render JavaScript. They differ sharply in what they hand back and how much work you do afterward.
How We Evaluated These APIs
Every tool on this list earns its rank against five criteria that decide whether a scraper actually works for AI pipelines. We weighted each one by how much it affects the code you write and the tokens you spend.
- JS rendering approach. Whether the tool runs a real headless browser to execute client-side JavaScript, and how reliably it returns fully rendered content instead of empty divs.
- Output format and LLM-readiness. Whether you get clean Markdown or structured JSON ready for a model, or raw HTML you have to parse and strip yourself before spending tokens on it.
- Anti-bot handling. Whether proxy rotation and unblocking are on by default or bolted on as paid add-ons that inflate your per-page cost.
- Pricing model. How predictable the cost is at scale, including whether failed requests get billed and how much a JS render or structured extraction adds per page.
- Ease of integration for AI pipelines. How fast you can go from signup to working output, and whether the tool offers a single API, native SDKs, and direct MCP integration for agents.
Pricing, public documentation, and independent benchmarks informed each entry where we could verify them.
Quick Comparison: Web Scraping APIs for Dynamic Sites
The table below ranks all eight tools across the five criteria plus a "Best For" column, so you can orient before reading the detailed entries. Context.dev leads on clean structured output and MCP integration, while the enterprise proxy vendors lead on network depth.
| Tool | JS Rendering | Output Format | Anti-Bot Built-In | LLM-Ready | Starting Price | Best For |
|---|---|---|---|---|---|---|
| Context.dev | Auto-detecting headless render | Markdown, HTML, schema JSON | Yes, 96%+ first-attempt | Native (MCP, clean Markdown/JSON) | Free, then $25/mo | AI and LLM pipelines |
| Firecrawl | Headless browser, Actions | Markdown, HTML, JSON | Add-on (+4 credits/page) | Strong (clean Markdown) | Free, then $19/mo | Open-source LangChain/LlamaIndex teams |
| ScrapingBee | Single-param toggle, Chrome | HTML, JSON via AI API | Proxy rotation only | Partial (no native Markdown) | $49/mo | Quick API-first unblocking |
| Bright Data | Scraping Browser, Web Unlocker | HTML, JSON | Yes, proxy-backed | Limited | Enterprise/usage-based | Enterprise proxy-backed scraping |
| Apify | Actor-based headless | HTML, JSON, custom | Varies by Actor | Varies by Actor | Free, then usage-based | Ready-made scraper marketplace |
| Zyte | Zyte API auto-extraction | HTML, structured JSON | Yes, Smart Proxy Manager | Limited | Enterprise/usage-based | SLA-backed enterprise crawls |
| ScraperAPI | 10 credits per JS render | Raw HTML | Proxy rotation | No | $49/mo | Raw HTML at low cost |
| Oxylabs | Real-Time Crawler | HTML, structured JSON | Yes, proxy-backed | Limited | Enterprise/usage-based | Large-scale geo-distributed collection |
Two patterns stand out. Context.dev and Firecrawl bill JS rendering into a flat per-page cost, while ScraperAPI and ScrapingBee charge a multiplier for every rendered request. Only Context.dev and Firecrawl return clean Markdown by default, and Context.dev is the sole entry listing MCP as a core feature rather than an optional add-on. The enterprise vendors trade output convenience for proxy depth and SLA guarantees.
The Best Web Scraping APIs for Dynamic Sites in 2026
The eight tools below are ranked by how well they serve developers feeding scraped content into AI systems. Context.dev leads because it returns clean, LLM-ready output from a single API with no browser infrastructure to run. The rest earn their place for specific jobs, from open-source crawl workflows to enterprise proxy depth. Each entry covers JS rendering, output format, anti-bot handling, pricing, and who it fits best.
1. Context.dev - Best for AI and LLM Pipelines
Context.dev builds its entire API around a single goal, which is handing an LLM clean text it can read without a parser in between. The Model Context Protocol server ships as a core feature, not an add-on, so an AI agent can call the scraper directly during a conversation and pull back structured content in the same loop. Pricing follows the same logic. Every web scraping call costs one flat credit whether the page is static or a heavy single-page app that needs a full headless render, and failed or blocked requests never get billed.
The default output tells you who the product is for. A GET /v1/scrape/markdown call returns clean GitHub Flavored Markdown with navigation, ads, and cookie banners already stripped, so what lands in your embedding pipeline is the semantic content and nothing else. A live demo against vercel.com/pricing returns "Pro $20/mo" in 247ms, and roughly 70% of requests come back from cache without a full render, which keeps latency low on repeat crawls. Feeding rendered Markdown instead of raw HTML into a model cuts token count directly, and one engineer reported a measurable token bill drop after the switch.
Anti-bot handling runs on every request by default rather than sitting behind a premium flag. Residential proxies, browser fingerprint rotation, and CAPTCHA solving for Cloudflare, DataDome, and PerimeterX are all built in, and Context.dev reports a 96%+ first-attempt success rate on protected pages with no extra configuration or per-request surcharge. You get the same bypass behavior on the free tier that you get at scale.
When you need typed data instead of prose, the /web/extract endpoint takes a Zod or JSON Schema you define and returns JSON matching that schema exactly. Ask for a company_name string and a pricing_tiers array, and that is the shape you get back, with no CSS selectors to maintain and no brittle scraper to rewrite when a site changes its layout. Extra endpoints for raw HTML, full-site crawls, sitemap discovery, image extraction, screenshots, and brand data all sit under one API key, so you never juggle multiple vendors to assemble a pipeline.
Two things set Context.dev apart from the rest of this list for AI work. It carries native brand data, meaning logos, colors, fonts, and company firmographics you would otherwise scrape and clean by hand. It also removes the browser infrastructure you would normally run yourself, which is the main reason SiteGPT migrated off Firecrawl in under a day and Mintlify integrated in under ten minutes. Official SDKs cover TypeScript, Python, Ruby, Go, and PHP, and an agent quickstart lets an AI agent self-register for a key without a human in the loop.
Best for: developers feeding JavaScript-rendered content into RAG pipelines and AI agents who want LLM-ready Markdown or schema-typed JSON from a single API, with anti-bot bypass on by default and no browser infrastructure to run.
| Plan | Monthly | Credits | Rate Limit |
|---|---|---|---|
| Free | $0 | 500 | 30 req/min |
| Hobby | $25 | 10,000 | 60 req/min |
| Starter | $49 | 30,000 | 120 req/min |
| Pro | $149 | 200,000 | 300 req/min |
| Growth | $299 | 500,000 | 600 req/min |
| Scale 1M | $499 | 1,000,000 | 700 req/min |
| Scale 2.5M | $949 | 2,500,000 | 800 req/min |
Web scraping runs one credit per call, Brand API calls cost ten, and early-stage startups qualify for up to 30% off for a year.
2. Firecrawl - Best for Open-Source LangChain/LlamaIndex Teams
Firecrawl is the strongest open-source option for teams already building on LangChain or LlamaIndex, where crawl-scale workflows matter more than single-call extraction. Its headless browser fully renders pages before analysis, and independent testing puts its JavaScript site success rate at 97% with single-URL extraction averaging 1.2 to 3.5 seconds. If your pipeline needs to crawl a whole documentation set or product catalog rather than pull one clean record, Firecrawl's endpoints map cleanly to that job.
The Actions feature is where Firecrawl earns its place for complex targets. You script pre-scrape browser interactions like click, scroll, wait, and screenshot, which lets you reach content behind login walls or paginated "Load More" buttons that static fetchers never see. A managed Browser Sandbox keeps a session open across these steps at 2 credits per browser minute. For sites that gate their real content behind interaction, few tools handle the sequence as directly.
Firecrawl outputs Markdown, HTML, raw HTML, links, and schema-based JSON, which covers most LLM ingestion needs. The JSON extraction path is accurate, with schema-based accuracy near 98.7% in the same testing. The cost structure is where AI teams should look closely. A standard scrape runs 1 credit per page, but JSON mode adds 4 credits per page, and enhanced proxy adds another 4. The exact output most AI pipelines want carries the steepest per-page multiplier.
Two gaps separate Firecrawl from Context.dev for AI work. Anti-bot handling is not on by default, so you opt into enhanced proxy and pay the credit uplift once a target starts blocking you. Firecrawl also returns 0% success on LinkedIn, Twitter, and Instagram, so heavily protected social targets fall outside its reach. The variable credit model compounds at scale, since every JSON page and every proxy retry multiplies against a fixed monthly credit pool. Context.dev's flat 1-credit-per-request pricing avoids that math, and its anti-bot handling runs by default rather than as a paid add-on.
Best for: open-source AI teams running crawl-scale workflows on LangChain or LlamaIndex, where multi-step interaction and breadth outweigh flat per-request cost.
| Plan | Monthly | Credits/month |
|---|---|---|
| Free | $0 | 500 one-time |
| Hobby | $19 | 3,000 |
| Standard | $99 | 100,000 |
| Growth | $399 | 500,000 |
| Scale | $599 | 1,000,000 |
3. ScrapingBee - Best for Quick API-First Unblocking
ScrapingBee is the fastest way to add JavaScript rendering to an existing HTTP scraper without touching browser infrastructure. You flip a single render_js parameter in the API request, and ScrapingBee runs the target through the latest headless Chrome before returning the result. That design fits developers who already have a working scraper and just need React, Angular, or Vue pages to resolve before parsing, per Byteful's comparison.
The headless Chrome approach removes the operational cost of running and patching your own browser instances. ScrapingBee manages the browser pool, rotates IPs from its in-house proxy network on each request, and supports geotargeting through proxy selection. For price monitoring or job-listing tracking, that combination handles the common failure modes of a plain HTTP fetch without extra configuration.
ScrapingBee also ships an AI Web Scraping API that accepts a natural language prompt and returns JSON. You describe the fields you want, and the API extracts them without CSS selectors, which avoids the selector breakage that hits every layout change. If you prefer explicit control, ScrapingBee still supports CSS selectors and XPath for targeting specific elements rather than pulling the full page.
The gaps show up once you feed output into an LLM. ScrapingBee returns HTML or extracted JSON, but has no native Markdown output, so you carry the token cost and cleanup work that raw HTML forces on a model. It also has no MCP server, which means no direct wiring into an agent runtime the way Context.dev exposes extraction to LLM pipelines. Every JavaScript render burns extra credits against your plan, and the in-house proxy pool sits below the residential networks that Bright Data and Oxylabs run for heavily protected targets.
Pricing starts with 1,000 free API credits, then the entry paid Freelance plan at $49 per month, with higher tiers scaling concurrency from 5 to 200 requests. Billing is credit-based, and JS renders consume more per call.
Best for: developers who want a single-parameter JS toggle and quick unblocking, and who are comfortable parsing HTML or JSON themselves rather than feeding clean Markdown straight into an AI pipeline.
4. Bright Data - Best for Enterprise Proxy-Backed Scraping
Bright Data wins when the hard part of your scraping job is reaching the target at all, not shaping what comes back. Its proxy network spans tens of millions of residential and ISP addresses across nearly every country, which lets you rotate through real consumer IPs and defeat the geo-blocks and rate limits that stop most scrapers. For AI engineering teams collecting data from sites that aggressively fingerprint traffic, that network depth is the differentiator no output format can replace.
Two products handle JavaScript-heavy targets. The Scraping Browser is a hosted headless Chrome instance you drive with Puppeteer or Playwright, and it runs proxy rotation and CAPTCHA solving underneath your automation code. Web Unlocker takes a URL and returns the fully rendered HTML after clearing whatever anti-bot challenge the site throws, so you skip writing browser logic yourself. Both render client-side content reliably, and both return HTML rather than clean Markdown or schema-shaped JSON.
The tradeoff shows up in cost and integration effort. Bright Data prices by traffic and product tier, and residential proxy bandwidth runs several dollars per gigabyte, so a high-volume crawl bills far above Context.dev's flat 1-credit-per-call model. You also inherit more setup work. You wire in the proxy manager, tune your headless browser, and then build a parsing layer to turn raw HTML into the structured fields your LLM pipeline consumes. Context.dev collapses that chain into a single API call that returns rendered Markdown or JSON directly, with no browser infrastructure to maintain and no separate parsing step.
Choose Bright Data when compliance review, contractual SLAs, or precise geo-targeting drive your decision, and when your team already has engineers who can own proxy configuration and downstream parsing. Its scale and legal footprint suit large data infrastructure groups that treat scraping as a governed pipeline. If your goal is feeding an AI agent clean structured data fast, the proxy depth solves a problem you may not have, and the raw HTML output leaves the LLM-readiness work on your plate.
Best for: Enterprise teams with compliance or geo-targeting requirements and the engineering capacity to manage proxy infrastructure.
5. Apify - Best for Ready-Made Scraper Marketplace
Apify wins when someone has already built the scraper you need. Its Actor marketplace hosts thousands of pre-built scrapers for specific targets like Instagram, Google Maps, and Amazon, so you can run a job against a well-known site without writing extraction logic yourself. For a team that needs a common source scraped once or occasionally, pulling an existing Actor off the shelf beats building from scratch.
The platform pairs that marketplace with a full development stack. Apify's open-source SDK gives you a Crawlee-based framework for writing custom scrapers, and its cloud runs them on managed infrastructure with scheduling, storage, and proxy rotation handled for you. Larger buyers get SOC 2 compliance, audit logging, and account controls, which makes Apify a defensible choice for enterprises with procurement and data-governance requirements.
The tradeoff shows up when your goal is clean structured output for an AI pipeline. Each Actor returns data in whatever shape its author chose, so feeding that into a RAG system or an agent means writing normalization code to reconcile inconsistent fields across sources. You also inherit the operational surface of the Actor model. You pick Actors, manage their versions, monitor runs, and pay per-Actor compute costs that vary by author.
Context.dev takes the opposite approach for AI work. Instead of a catalog of author-defined scrapers, one API returns clean Markdown, rendered HTML, or schema-matched JSON from any URL, so the same call shape works across every site you hit. You define a Zod or JSON Schema on the /web/extract endpoint and get back data that matches it exactly, with no per-source normalization step. MCP support ships as a core feature, which lets an agent call the API directly without a translation layer. There is no Actor to select, no version to track, and no infrastructure to run.
Choose Apify when a ready-made Actor already covers your target and you value the marketplace and compliance depth. Choose Context.dev when you need consistent, LLM-ready structured output from arbitrary sites through a single API.
6. Zyte - Best for SLA-Backed Enterprise Crawls
Zyte earns its enterprise reputation on reliability guarantees, not output flexibility. If you run scraping jobs against banks, airlines, or retail sites that fight bots aggressively, and you need a contractual success-rate SLA behind those jobs, Zyte is the incumbent most data infrastructure teams already trust. The Zyte API combines automatic extraction with a proxy layer tuned for the hardest targets, and its Smart Proxy Manager rotates through residential and datacenter pools to keep protected domains reachable at scale.
The automatic extraction feature detects common page types, such as product and article pages, and returns parsed fields without you writing selectors. That works well when your targets match Zyte's supported schemas. For AI pipelines that need arbitrary structured output shaped to a specific JSON schema, you still do post-processing, since Zyte optimizes for its own extraction models rather than schema-on-demand extraction like Context.dev's /web/extract endpoint.
The tradeoff is cost and setup. Zyte's pricing and onboarding target enterprise buyers with procurement cycles and infrastructure teams, not a developer wiring up a single API call over a weekend. You configure crawl policies, tune proxy behavior per domain, and lean on account support to hit the SLAs you're paying for. A solo developer feeding scraped content into an LLM will find that overhead hard to justify when a flat 1-credit call returns clean Markdown directly.
Best for: Enterprise data teams scraping heavily defended domains where a contractual reliability SLA matters more than LLM-ready output format. If your priority is real-time structured extraction with no infrastructure to maintain, Context.dev is the faster path. If your priority is a guaranteed success rate against the internet's most hostile anti-bot systems, Zyte remains a defensible pick.
7. ScraperAPI - Best for Raw HTML at Low Cost
ScraperAPI wins on price for teams that want raw HTML and plan to parse it themselves. It charges by a credit model where a standard request costs 1 credit, a JavaScript render costs 10 credits, and an ultra-premium request against hard targets costs 75 credits. You send a URL, ScraperAPI handles proxy rotation and retries, and you get back the rendered page HTML.
The proxy rotation and automatic retry handling are the real value here. ScraperAPI manages a large pool and rotates IPs on your behalf, which clears the low-to-moderate anti-bot checks most sites use without any configuration from you. For a scraper hitting hundreds of thousands of pages where you already have parsing logic, that low per-request floor adds up in your favor.
The output is where ScraperAPI stops short of an AI pipeline. It returns raw HTML with no native Markdown and no schema-based JSON extraction. If you feed that HTML straight into an LLM, you pay for every navigation menu, script tag, and inline style in your token bill. To get clean structured data, you write and maintain your own parsers for each site layout, and those parsers break every time a target changes its markup.
Treat ScraperAPI as a stepping stone rather than a destination for AI work. It fits teams with existing extraction code and a tight budget who need reliable page retrieval at volume. Teams building LLM pipelines end up bolting a parsing and cleanup layer on top, which is the work Context.dev returns as Markdown or JSON in a single call.
Best for: budget-conscious teams that need raw HTML at scale and own their parsing.
8. Oxylabs - Best for Large-Scale Data Collection with Geo Precision
Oxylabs targets teams collecting data across many countries at high volume, where proxy depth and geographic control decide whether a job finishes. Its residential proxy network spans over 100 million IPs across nearly every country, which lets you pin requests to specific cities and regions when a target site serves different content by location. For price monitoring across regional storefronts or ad verification in specific markets, that geo precision does real work.
The Real-Time Crawler handles JavaScript rendering and anti-bot evasion behind a single API call, returning parsed data for e-commerce and search targets without you managing browser pools. Oxylabs also sells structured data products for major sites, delivering fields like product name and price instead of raw HTML. For a data infrastructure team running geo-distributed collection at scale, that combination removes a large chunk of proxy and rendering maintenance.
The cost and enterprise orientation are where Oxylabs stops making sense for smaller AI teams. Pricing sits in the enterprise tier, and the sales-led onboarding assumes committed monthly volume rather than a card and an API key. Output leans toward HTML and per-site structured formats built for analytics pipelines, not the clean Markdown or schema-driven JSON that an LLM consumes efficiently.
If your bottleneck is scraping thousands of localized pages across dozens of countries with tight geographic control, Oxylabs earns its price. If you are feeding a language model and want structured output on the first call without an enterprise contract, Context.dev delivers LLM-ready JSON and Markdown from a single API at flat per-call pricing, with no proxy network to provision.
Best for: Large-scale, geo-targeted data collection where proxy depth and regional accuracy outrank output format.
Decision Guide: Matching Your Use Case to the Right Tool
The right tool depends on what you optimize for. Most teams get pulled toward whichever product they discovered first, then fight its defaults for months. Match the priority to the tool instead.
| If your priority is... | Use |
|---|---|
| Feeding an AI or LLM pipeline with clean, structured output | Context.dev |
| Open-source crawl workflows tied to LangChain or LlamaIndex | Firecrawl |
| Quick JS unblocking with minimal setup | ScrapingBee |
| Deep proxy networks and geo-targeting at enterprise scale | Bright Data or Oxylabs |
| A pre-built scraper for a specific, well-known site | Apify |
| SLA-backed reliability against heavily protected domains | Zyte |
| Raw HTML on the tightest budget | ScraperAPI |
If you are building RAG pipelines or AI agents, Context.dev is the strongest fit. Its Markdown API returns LLM-ready output in around 247ms, its /web/extract endpoint returns JSON matching a schema you define, and MCP support ships as a core feature rather than an add-on. You pay 1 credit per scraping call, blocked requests are not billed, and there is no browser infrastructure to run yourself.
Firecrawl wins when you already live inside an open-source stack and need crawl-scale flexibility, though its anti-bot handling is off by default and JSON mode costs extra per page. Bright Data and Oxylabs earn their price when proxy depth and compliance outrank output format. Apify makes sense when someone has already built the exact Actor you need. Zyte fits teams that value reliability guarantees over clean structured data, and ScraperAPI works as a stepping stone when you handle parsing yourself and cost drives the decision.
Start with the free tier if your target is an AI pipeline. You can test rendered Markdown and schema-based JSON against your own URLs before committing to a plan.
How JavaScript Rendering Works (and Why Output Format Matters for LLMs)
A static HTTP fetcher grabs the raw HTML a server sends and stops there. Modern single-page apps send an almost empty shell like <div id="root"></div>, then rely on JavaScript to build the visible content in the browser. To capture that content, a scraper spins up a headless browser, loads the page, executes the JavaScript, and reads the DOM after the framework finishes rendering. Only then does the actual text, pricing, or product data exist to extract.
What you do with that rendered DOM decides how much your LLM pipeline costs. Raw HTML carries the full document, including navigation, ad markup, cookie banners, and inline styles the model never needs. Feeding it straight into a prompt wastes tokens on structure that teaches the model nothing, and it dilutes the signal your retrieval step depends on.
Clean Markdown solves the token problem. Context.dev's Markdown API strips navigation, ads, and cookie banners while preserving headings, lists, and links, so a page arrives as the semantic text a model can actually use. One LinkedIn writeup reported a measurable drop in token spend after switching from raw HTML to clean Markdown. Fewer tokens per page means cheaper embeddings and more documents inside a fixed context window.
Structured JSON goes one step further when you know the fields you want. Context.dev's /web/extract endpoint takes a Zod or JSON Schema and returns data matching it exactly, so a pricing page becomes a typed pricing_tiers array instead of prose your code has to parse. Reach for Markdown when you want the whole page as readable text for RAG, and reach for JSON when you need specific values a downstream agent will act on.
Methodology
We selected these eight tools because each ships a hosted API that renders JavaScript, and each targets developers rather than no-code users. We ranked every entry against five dimensions: JS rendering approach, output format and LLM-readiness, built-in anti-bot handling, pricing model, and ease of integration for AI pipelines.
Each entry draws on public documentation, published pricing, and independent benchmarks where they exist. Firecrawl's numbers come from third-party testing reported by AffTank, including a 97% success rate on JavaScript sites. ScrapingBee's feature coverage draws on a Byteful comparison. For tools without recent independent benchmarks, we relied on documented capabilities and stated pricing rather than performance claims.
The ranking reflects one bias worth naming. We weighted clean, structured output for LLM consumption above raw proxy scale, because the reader here feeds scraped content into AI systems.
FAQs
What is a web scraping API and how does JS rendering work?
A web scraping API fetches a target page and returns its content over a single HTTP call, so you never run browsers or proxies yourself. Context.dev renders JavaScript with a headless browser layer that executes the page's scripts, then returns the full DOM instead of the empty <div id="root"> shell a static fetch returns. You get the same output an AI pipeline needs without building or maintaining that infrastructure.
Which scraping API gives the cleanest output for LLMs?
Context.dev returns GitHub Flavored Markdown that strips navigation, ads, and cookie banners while keeping the semantic structure your model reads. Its Markdown API answered a live vercel.com scrape in 247ms, and the /web/extract endpoint returns JSON matching a schema you define. Firecrawl and ScrapingBee also produce Markdown or JSON, but they charge extra credits for structured extraction and lack native MCP support.
How do I scrape JavaScript sites without building browser infrastructure? Send the URL to a managed API that runs the headless browser for you and returns rendered content. Context.dev auto-detects whether a page needs a full render, bypasses Cloudflare, DataDome, and PerimeterX on every request, and reports a 96%+ first-attempt success rate. You skip proxy pools, browser pools, and CAPTCHA solvers entirely.
What's the difference between Markdown, HTML, and JSON output for AI pipelines? Raw HTML carries tags, scripts, and styling your model does not need, which inflates your token bill. Markdown keeps the readable content and drops the noise, so it works well for RAG chunks and embeddings. JSON returns only the fields you asked for, which suits agents that need specific data points like pricing tiers or company profiles.
How does pricing compare across these tools at scale? Context.dev charges 1 credit per web scraping call and does not bill failed or blocked requests, with paid plans starting at $25/month. Firecrawl starts at $19/month but adds 4 credits per page for JSON extraction, so structured jobs cost five times a plain scrape. Flat 1-credit pricing keeps large AI pipelines predictable where variable per-feature credits do not.