Spider vs. Firecrawl: Two Cloud APIs, Different DNA

If you’re evaluating managed scraping APIs for an LLM pipeline, Firecrawl and Spider are probably on your shortlist. Both take a URL and hand back clean markdown. Both handle proxies and JavaScript rendering behind the scenes. Both have SDKs, MCP servers, and integrations with LangChain and friends.

So the question isn’t “which one works.” It’s where the details diverge once you zoom past the feature checklist. This post covers pricing, performance, architecture, licensing, and AI capabilities so you can make a grounded decision for your specific workload.

The pricing gap

Cost is where these two tools differ most, so let’s start there.

Firecrawl uses tiered subscriptions billed annually or monthly. These are the annual billing rates (monthly billing costs more):

Unused credits expire at the end of each billing cycle. Use 60,000 out of 100,000 Standard credits, and the remaining 40,000 vanish.

Spider bills bandwidth ($1/GB) plus compute ($0.001/min) with no subscription:

No subscription floor, no credit expiration, no overage surcharges. The average production cost holds steady around $0.65 per 1,000 pages regardless of volume.

At 100K pages/month, Spider costs roughly $65 versus Firecrawl’s $83 on Standard. At lower volumes the gap widens further: 10K pages runs ~$6.50 on Spider versus a $16 minimum on Firecrawl’s Hobby plan.

Architecture under the hood

Firecrawl is TypeScript running on Node.js. The self-hosted version includes a server, a Redis-backed BullMQ job queue, and Playwright for browser rendering. The cloud API wraps this with managed proxy rotation and scaling.

Spider is compiled Rust. The core engine uses tokio for async I/O, zero-copy HTML parsing, and a smart rendering system that auto-detects whether a page actually needs a browser. Static pages never touch Chromium.

The architecture drives the performance difference. Rust’s compiled binary processes static HTML roughly 7x faster than Node.js for the same page. On JavaScript-heavy pages the browser is the bottleneck, so the gap narrows to 3-4x. Spider’s smart mode skips the browser entirely when it isn’t needed, which helps on the majority of pages.

Benchmark numbers

From our 1,000-URL benchmark, same hardware, same network, same URL list:

Spider averaged 4.6x faster across the corpus and 6.7x faster on static HTML. The success rate gap (99.9% vs 95.3%) translates to 46 extra successful pages per 1,000. In a RAG pipeline, every missing page is a gap in your vector store.

Time to first result matters for real-time use cases. Spider returns the first static page in 45ms versus 310ms. Both are sub-second, but in an agent loop making dozens of sequential fetches, a 265ms gap per call compounds fast.

Markdown quality was close. Both produce clean LLM-ready output. Spider’s parser strips boilerplate (nav bars, cookie banners, sidebars) more aggressively, which showed up as a 2.5-point edge in RAG retrieval accuracy.

The AGPL question

This matters more than most teams realize during evaluation.

Firecrawl’s AGPL-3.0 means that if you modify the source and serve it over a network (i.e., build a SaaS on top), you must release those modifications under AGPL. For companies building proprietary products on top of the scraping layer, this often makes the cloud API the only practical path.

Spider’s MIT license carries no such obligation. You can modify, embed, and redistribute without sharing source.

If you’re only consuming the cloud API and never modifying source, this distinction doesn’t apply. But if self-hosting or building on top of the OSS codebase is on your roadmap, bring your legal team into the conversation early.

AI extraction approaches

Both platforms are investing heavily in AI-powered extraction, but the philosophies differ.

Firecrawl offers extract: define a schema via Zod or JSON Schema and Firecrawl returns structured data matching that schema. It also has agent and browser tools in its MCP server for more interactive workflows. The schema approach is clean when you know your output format upfront and want deterministic results.

Spider takes two routes:

1. AI Studio: natural language endpoints (/ai/crawl, /ai/scrape, /ai/search, /ai/browser, /ai/links) where you describe what you want in plain English. No schema definition required.

2. Spider Browser: live WebSocket sessions with AI methods baked into the SDK:

import { SpiderBrowser } from "spider-browser";

const spider = new SpiderBrowser({
  apiKey: process.env.SPIDER_API_KEY,
  stealth: 0,
});

await spider.init();
await spider.page.goto("https://example.com/pricing");
await spider.page.click(".show-enterprise-plans");
await spider.page.waitForSelector(".enterprise-table");

const pricing = await spider.page.extract(
  "Get all plan names, prices, and included features"
);
console.log(pricing);
await spider.close();

Spider Browser’s extract(), act(), observe(), and agent() methods work on the live DOM, including content loaded after clicks, scrolls, and JavaScript execution. Firecrawl’s extraction can also work with browser-rendered content through its agent and browser tools.

SDK and integration coverage

Both have mature Python and JavaScript SDKs. Spider adds Go and CLI clients. Firecrawl has a community-maintained Rust SDK covering v1 of the API. On the MCP front, both offer servers. Firecrawl’s includes scrape, crawl, map, search, agent, and browser tools. Spider’s covers its core and AI endpoints.

A side-by-side crawl

The same task (crawl a site, get markdown) in both tools:

Spider

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": 10,
        "return_format": "markdown",
        "request": "smart",
    },
)

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

Firecrawl

from firecrawl import FirecrawlApp

app = FirecrawlApp(api_key="fc-YOUR_API_KEY")

result = app.crawl_url(
    "https://example.com",
    params={
        "limit": 10,
        "scrapeOptions": {
            "formats": ["markdown"],
        },
    },
    poll_interval=2,
)

for page in result.get("data", []):
    print(f"{page['metadata']['url']}: {len(page.get('markdown', ''))} chars")

Both are straightforward. Firecrawl’s crawl is async by default, so you poll for results or use a webhook. Spider returns results synchronously or streams via JSONL.

Where Firecrawl has the edge

Developer polish. Firecrawl’s documentation is thorough, the API design is clean, and the TypeScript SDK has excellent types. The crawl → poll → get results flow is well-documented.

Community gravity. 80,000+ GitHub stars and a large Discord mean more blog posts, Stack Overflow answers, and developers who’ve used it before. When you’re evaluating tools with non-technical stakeholders, community size provides confidence.

Map endpoint. Firecrawl’s /map discovers all URLs on a site without crawling content, useful for understanding site structure before committing to a full crawl.

Schema-based extraction. If you have a well-defined output schema and want repeatable, structured JSON extraction, the Zod/JSON Schema approach gives you type safety and predictability.

Where Spider has the edge

Raw throughput. 4.6x faster across a mixed corpus and 99.9% success rate. When your workload is measured in hundreds of thousands of pages, speed is the dominant factor.

Flat pricing. ~$0.65 per 1K pages with no subscription, no credit expiration, and no volume tiers. The bill is predictable whether you’re scraping 10K or 10M pages.

MIT licensing. No restrictions on commercial use, modifications, or derivative works.

Live browser automation. WebSocket-based sessions with AI-powered extraction on the interactive DOM, not just the initial page load.

Built-in anti-bot. Cloudflare, DataDome, PerimeterX, and Akamai bypass without extra config or cost.

Format flexibility. Markdown, commonmark, plain text, XML, raw HTML, or bytes from the same endpoint via a single return_format parameter. Firecrawl focuses on markdown and structured extraction.

Picking the right one

Firecrawl is a strong choice if your workload is moderate (under 100K pages/month), you value polished developer experience, your team is already invested in the ecosystem, or you need schema-driven extraction with deterministic outputs.

Spider fits better when throughput and cost matter at your scale, you need live browser automation, MIT licensing is important, or your targets include heavily protected sites where success rate is critical.

Both are production-grade tools backed by active teams. The right answer depends on your workload, your scale, and your constraints.

Spider pricing breakdown