Top 5 Data Collection Platforms for AI and Web Scraping in 2026

If you’re building anything with LLMs right now (RAG pipelines, autonomous agents, training data workflows) you’ve already hit the same wall everyone else has: getting clean data off the web, fast, without burning through your budget.

The scraper market has exploded over the past two years. Dozens of platforms are competing for your dollar, and most of them look identical at first glance. We picked five platforms that represent different approaches: a high-throughput API (Spider), an actor marketplace (Apify), a proxy-first API (ScrapingBee), a complexity-tiered proxy (Crawlbase), and an AI extraction service (Diffbot). They are not interchangeable — what matters is matching the tool to your use case.

1. Spider

Website: spider.cloud Pricing: Pay-as-you-go, no subscription Open Source: Yes, MIT licensed (spider-rs/spider)

Spider’s core is written in Rust. The crawl engine handles HTTP connections, HTML parsing, and content transformation natively, which is what lets it sustain higher throughput per server than interpreted-language alternatives.

Cost

No subscription tiers, no monthly minimums. You buy credits and use them. The base components start at:

These are entry-level rates. The actual cost per page scales with the complexity of the crawl: what type of proxy is needed (datacenter, residential, or mobile), whether the site requires anti-bot handling, and how much data each page returns. Simple static sites cost a fraction of a cent. Sites behind Cloudflare with residential proxies cost more. Averaged across production traffic mixing static and JS-rendered pages, a typical workload runs around $0.65 per 1,000 pages. For purely JS-rendered crawls with residential proxies, expect $1.00-1.50 per 1,000 pages.

Volume purchases up to $4,000 come with a 30% credit bonus. There’s also a lite_mode flag that halves costs when you don’t need full-fidelity processing. You can cap spend per request with max_credits_per_page and max_credits_allowed, so a runaway crawl won’t surprise you on the invoice.

Speed and reliability

Spider handles up to 50,000 requests per minute per account with a p99 latency of 12ms. Each request can batch multiple URLs, so actual concurrent connections go well beyond that. The default smart mode inspects each page and picks the cheapest path: a lightweight HTTP fetch for static content, headless Chrome only when JavaScript rendering is actually needed.

At scale, this adds up quickly. If you’re crawling thousands of domains for a RAG pipeline or monitoring hundreds of sites for an agent, the gap between a scraper that processes pages in milliseconds versus seconds becomes hours of wall-clock difference.

Success rate sits at roughly 99% across production traffic. Anti-bot bypass for Cloudflare, Akamai, Imperva, and Distil is built in, along with proxy rotation across datacenter, residential, and mobile IPs. When a page fails, Spider handles retries automatically. You don’t need to write fallback logic.

Built for AI pipelines

Every response can come back as clean markdown with navigation, ads, footers, and boilerplate already stripped. Or you can send a natural language prompt and get structured JSON, no CSS selectors, no XPath.

Spider ships as an official document loader in LangChain, LlamaIndex, CrewAI, and Microsoft AutoGen. The path from URL to vector store looks like this:

import requests, os

response = requests.post('https://api.spider.cloud/crawl',
  headers={
    'Authorization': f'Bearer {os.getenv("SPIDER_API_KEY")}',
    'Content-Type': 'application/json',
  },
  json={
    "url": "https://example.com",
    "limit": 100,
    "return_format": "markdown",
    "request": "smart"
  }
)

for page in response.json():
    print(f"{page['url']}: {len(page['content'])} chars")

For larger crawls, set Content-Type to application/jsonl and stream results as they arrive instead of buffering everything in memory.

The underlying Rust crate (crates.io/crates/spider) has over 2,200 GitHub stars and 43+ feature flags. It runs standalone without the cloud API if you want to self-host. SDKs exist for Python, JavaScript, and Rust.

Spider also runs its own extraction models for structured HTML-to-JSON conversion, so extraction workloads do not incur external LLM API costs.

Trade-offs

Spider’s strength is the combination of raw throughput and low per-page cost for general-purpose crawling. The main trade-off is community size: with 2,200 GitHub stars on the Rust crate, Spider has a smaller community than Firecrawl (30,000+) or Crawl4AI (30,000+). If you need pre-built scrapers for specific platforms (Amazon, Google Maps), Apify’s marketplace will get you there faster. If you need entity-level Knowledge Graph data, Diffbot is purpose-built for that.

Notable absence from this list: Firecrawl is a direct competitor for AI/RAG use cases with 30,000+ GitHub stars, clean markdown output, and a polished developer experience. We excluded it because this list focuses on platforms with distinct approaches rather than overlapping offerings, but it deserves evaluation alongside Spider if you are building AI data pipelines.

2. Apify

Website: apify.com Pricing: From $29/mo (Starter) Open Source: Platform is proprietary; Crawlee SDK is MIT

Apify is a marketplace. Rather than a single scraping API, it offers a store with over 10,000 pre-built “Actors,” scraping tools built by the community for specific sites like Amazon, Google Maps, LinkedIn, and Instagram. If someone’s already solved your exact scraping problem, Apify lets you run their code on managed infrastructure.

Where it works well

The breadth of the Actor marketplace is genuinely hard to match. Need to pull Google Maps listings or LinkedIn profiles at volume? There’s probably an Actor for it, with proxy management and scheduling handled by the platform. The open source Crawlee SDK (formerly Apify SDK) is also solid if you want to build custom scrapers in JavaScript or Python that run outside Apify entirely.

The trade-offs

Pricing uses “Compute Units” (memory multiplied by runtime), which makes per-page cost hard to predict before you run a job. A lightweight HTML Actor is cheap. A full-browser Actor against a JavaScript-heavy site can eat through credits fast.

In practice, browser-based Actors typically cost $5 or more per 1,000 pages once you account for container spin-up time and memory usage. That makes Apify roughly 8x more expensive than Spider for equivalent JS-rendered crawls. Unused credits expire monthly.

Performance varies by Actor. The platform adds serverless container overhead, and community-maintained Actors can break when target sites change without warning. There’s no platform-wide markdown output or LLM-optimized formatting; you’ll get that from some individual Actors but not consistently across the board.

For general-purpose crawling where you need uniform, fast output across arbitrary domains, the marketplace model adds moving parts without a corresponding speed gain.

3. ScrapingBee

Website: scrapingbee.com Pricing: From $49/mo (Freelance) Open Source: No

ScrapingBee keeps things simple: send a URL, get HTML back. Proxy rotation and CAPTCHA solving happen behind the scenes without configuration.

Pricing catches

The headline numbers look straightforward until you notice the credit multipliers:

JavaScript rendering is on by default, so the real baseline is 5 credits per request. On the Freelance plan ($49/mo, 250K credits), that’s about 50,000 JS-rendered pages, roughly $0.98 per 1,000. Need stealth proxy for tougher sites? That jumps to $14.70 per 1,000 pages.

What you get and what you don’t

Reliability is decent for standard sites, and the stealth proxy tier handles harder targets. Geotargeting is locked behind the $249+ Business plan. Speed benchmarks aren’t published.

The bigger gap is AI readiness. ScrapingBee returns raw HTML with no markdown conversion and no LLM-optimized output. There’s an AI extraction add-on at +5 credits per request, but it feels bolted on rather than baked in. If you’re feeding data into a RAG pipeline, you’ll need to build the cleaning layer yourself.

The API is genuinely easy to integrate for small-scale work. But the credit multipliers mean the price you see on the pricing page isn’t the price you actually pay, and the lack of native AI output means extra engineering time for LLM use cases.

4. Crawlbase

Website: crawlbase.com Pricing: Pay-as-you-go, from $2/1,000 requests Open Source: Client libraries only (MIT)

Crawlbase (formerly ProxyCrawl) has been around a while and has over 70,000 users. It’s a proxy-based scraping API with built-in CAPTCHA solving: straightforward and functional.

Complexity-based pricing

Crawlbase categorizes every domain into a difficulty tier, and you pay accordingly:

The wrinkle: Crawlbase decides the tier, not you. A site you’d expect to be “Standard” might get classified as “Moderate” based on their internal scoring. LinkedIn is a flat $15 per 1,000 requests regardless of volume.

Solid but limited

Success rates are good (they claim 99.9%) and CAPTCHA solving is included. Speed isn’t published, and the platform isn’t optimized for high-throughput crawling at the volume some AI pipelines demand.

The biggest limitation is the complete absence of AI features. No markdown output, no extraction, no LLM formatting. You get raw HTML and that’s it. For teams whose scraping feeds directly into AI pipelines, that’s a significant amount of extra work to bridge the gap.

5. Diffbot

Website: diffbot.com Pricing: From $299/mo (Startup) Open Source: No

Diffbot does something different from everyone else on this list. Instead of rendering pages and handing you the output, it runs computer vision and NLP models against each page to classify it (article, product, discussion board) and extract structured fields automatically.

The price of AI-native extraction

That approach is powerful, but it’s reflected in the bill:

At the Startup tier, you’re looking at roughly $59.80 per 1,000 pages, close to 90x Spider’s cost. Even at enterprise volume the per-page price stays well above every other platform here.

Where it fits

Diffbot is reliable within its lane. When it correctly identifies a page type, the extraction is consistent and saves you from writing selectors. Articles, product pages, and discussion threads work well. Pages outside its models can return incomplete results.

The AI processing adds meaningful latency to each request, so high-throughput crawling isn’t realistic. And despite being AI-powered, Diffbot’s output comes in its own proprietary JSON schema. If your LLM expects markdown or plain text, you’ll need to transform it. There are no LangChain or LlamaIndex integrations.

Diffbot makes sense if you need automated structural extraction at low volume and budget isn’t the constraint. For most teams building AI applications today, the cost and format mismatch make it hard to justify.

Side-by-side

Which one should you pick?

It depends on what you’re building.

Building AI applications (RAG, agents, training data)? You need clean markdown output and high throughput. Spider and Firecrawl both produce markdown natively. Spider is cheaper at scale; Firecrawl has a simpler API surface and a generous free tier for prototyping.

Need pre-built scrapers for specific platforms like Amazon or Google Maps? Apify’s marketplace has breadth nobody else matches. Nothing on this list competes with 10,000+ pre-built Actors.

Just need a quick, simple scraping API for occasional use? ScrapingBee is genuinely easy to set up. Watch the credit multipliers — JS rendering at 5x is the real baseline, not 1x.

Need raw HTML through anti-bot protections? Crawlbase is reliable and straightforward. No AI features, but solid proxy infrastructure.

Need automated structural extraction at low volume? Diffbot’s Knowledge Graph and visual approach are genuinely superior for entity resolution and structured data extraction. Nothing else on this list matches it for that specific use case.

These tools are not interchangeable. Match the tool to what you are actually building, not to a feature comparison table.