The True Cost of Web Scraping at Scale

Most scraping cost comparisons stop at the price-per-page number on the landing page. That number is almost never what you actually pay. Once you factor in proxy bandwidth, failure retries, engineer time keeping scrapers alive, and the infrastructure underneath, the real bill looks very different.

This post breaks down what web scraping actually costs across five approaches, at four volume tiers, with nothing hidden. If you are evaluating scraping solutions for a production workload, this is the analysis you need before committing.

The five approaches

We are comparing these options because they represent the realistic set of choices a developer faces today:

1. Self-hosted Scrapy on EC2: The “build it yourself” baseline. Scrapy is the most widely used open source scraping framework. You run it, you maintain it, you own every problem.
2. Firecrawl: A managed scraping API with strong developer ergonomics, focused on AI-ready output formats. Per-page pricing.
3. Apify: A marketplace of pre-built scraping “Actors” running on managed infrastructure. Compute-unit billing.
4. Crawl4AI (self-hosted): An open source Python framework for LLM-oriented crawling. Free software, but you provide the infrastructure.
5. Spider: A Rust-native crawling API with pay-as-you-go pricing, no subscriptions, and native markdown/AI output.

Tier 1: 10,000 pages per month

At low volume, the sticker price of managed services is low and self-hosting looks disproportionately expensive because fixed costs dominate.

At 10K pages, Firecrawl and Spider are both cheap. The difference is negligible at this volume. Self-hosted options carry a fixed-cost penalty that only makes sense if you are already running infrastructure for other reasons.

Takeaway: For small workloads, any managed API wins over self-hosting. Spider is the cheapest managed option at this tier.

Tier 2: 100,000 pages per month

This is where most production workloads start. You are running nightly crawls, feeding RAG pipelines, or monitoring competitor pricing across a few hundred sites.

At 100K pages, the gap opens up. Firecrawl is nearly 5x more than Spider. Apify is almost 9x. Self-hosting is 10x, and most of that cost is the engineer time you are burning on proxy rotation scripts, retry logic, and debugging broken selectors.

Takeaway: Spider’s per-page economics start pulling away. Firecrawl is reasonable but 5x the cost. Self-hosted and Crawl4AI are surprisingly expensive once you honestly account for maintenance time.

Tier 3: 1,000,000 pages per month

A million pages a month is where scraping becomes an infrastructure problem, not a scripting problem. You need concurrent connections, distributed scheduling, proxy management at scale, and monitoring.

This is the tier where self-hosting catches up to Firecrawl in raw cost, but for the wrong reason: you are spending $1,600/month in engineer time just to match what an API does out of the box. Apify’s CU-based billing starts to hurt at browser-rendered scale. Crawl4AI, despite being free software, costs as much as Firecrawl because you are still paying for all the infrastructure and human time.

Spider at this volume is roughly 4x cheaper than the next cheapest option. If you spend $4,000 in credits at once, Spider applies a 30% volume bonus, which drops the effective rate to about $0.50/1K pages.

Takeaway: At 1M pages, every option except Spider is in the $3,000 to $5,600 range. Spider is around $700.

Tier 4: 10,000,000 pages per month

Ten million pages a month is enterprise-grade crawling. Search engines, large-scale data aggregators, AI training pipelines, and competitive intelligence platforms operate at this tier. At this volume, architectural decisions made early either save or cost six figures annually.

At 10M pages per month, the annual costs look like this:

Spider saves roughly $140,000 per year compared to self-hosting and nearly $300,000 per year compared to Firecrawl. Using lite_mode for pages that do not require full-fidelity processing roughly halves the page cost, which could bring the Spider total under $45,000/year at this volume.

Takeaway: At enterprise scale, Spider is 3x cheaper than self-hosting and 5x cheaper than Firecrawl. The gap only widens as volume increases.

The hidden costs nobody puts on the pricing page

The tables above include engineering time, but it is worth unpacking what that time actually looks like in practice. These are the cost categories that teams consistently underestimate.

Proxy management

Proxies are the single largest hidden cost in self-hosted scraping. A basic datacenter proxy pool costs $50 to $100/month and gets blocked by any serious anti-bot system within hours. Residential proxies that actually work cost $5 to $15 per GB of bandwidth, and a JS-rendered page averages 2 to 5 MB. At 1M pages per month, proxy bandwidth alone can exceed $1,000/month.

Managed services (Spider, Firecrawl, Apify) include proxies in their pricing. When you see “$0.65 per 1K pages” from Spider, that includes datacenter, residential, and mobile proxy rotation, with automatic escalation when a site requires it. Self-hosted solutions require you to manage proxy provider contracts, rotation logic, geographic targeting, and ban detection yourself.

Anti-bot maintenance

Cloudflare, Akamai, Imperva, and DataDome update their detection fingerprints regularly. A scraper that works today can break tomorrow. Maintaining anti-bot bypass for self-hosted scrapers is a continuous engineering cost, not a one-time setup. Teams typically spend 2 to 8 hours per month patching browser fingerprints, rotating TLS configurations, and updating header patterns.

Spider handles anti-bot bypass as a platform feature across Cloudflare, Akamai, Imperva, and Distil. When detection patterns change, the platform updates once and every customer benefits. With self-hosted setups, every team fights the same battle independently.

Failure handling and retries

At scale, failures are not edge cases. They are a constant. Network timeouts, rate limiting, CAPTCHAs, IP bans, site structure changes, and intermittent server errors all happen regularly. A well-built self-hosted scraper needs retry logic with exponential backoff, dead-letter queues for manual review, alerting for success-rate degradation, and circuit breakers to avoid hammering unresponsive sites.

Building and maintaining this infrastructure takes weeks of engineering time upfront and ongoing attention. Spider’s ~99% success rate with automatic retries means you can treat failures as genuinely exceptional rather than part of the normal workflow.

Data cleaning and format conversion

If you are feeding scraped data into an LLM, you need clean markdown or structured text, not raw HTML full of navigation bars, cookie banners, and ad scripts. Building a robust HTML-to-markdown pipeline that handles the variety of real-world HTML is a project in itself. Libraries like Turndown or html2text cover the basics, but production pipelines need custom rules for tables, code blocks, embedded media, and malformed markup.

Spider returns clean markdown natively, with boilerplate already stripped. Firecrawl also offers markdown output. Apify, Crawl4AI, and Scrapy leave this to you.

Monitoring and observability

Self-hosted scraping systems need monitoring for success rates, latency distributions, proxy health, queue depth, and output quality. At 1M+ pages per month, you need dashboards, alerting, and likely a dedicated on-call rotation. That is infrastructure cost (Datadog, Grafana Cloud, PagerDuty) plus the engineer time to set it up and respond to alerts.

With a managed API, monitoring reduces to tracking your API response codes and credit usage. The provider handles the rest.

How Spider’s pricing actually works

Spider uses component-based pricing with no subscriptions and no monthly minimums:

The total cost per page depends on what each page requires. Static HTML pages cost a fraction of a cent. Pages that need Chrome rendering and residential proxies cost more. Across a typical production mix of sites, the blended average is around $0.65 per 1,000 pages.

Three pricing features that matter at scale:

1. lite_mode: A flag that halves costs by skipping full-fidelity processing. When you need the text content but not pixel-perfect rendering, this is the switch to flip.
2. Volume bonus: Purchasing $4,000 or more in credits at once applies a 30% bonus, dropping the effective rate to roughly $0.50/1K pages.
3. Spend caps: max_credits_per_page and max_credits_allowed let you set hard limits per request, so a crawl against an unexpectedly expensive site does not blow your budget.

There is no subscription. No monthly commitment. You buy credits, use them at your own pace, and they do not expire.

Why self-hosted “free” tools are not free

Scrapy and Crawl4AI are both excellent open source projects. Nobody is questioning the quality of the software. The issue is that “free to download” and “free to operate” are completely different things.

Running a self-hosted scraping system at 1M pages per month requires:

• 3+ EC2 instances with proper sizing for concurrent connections ($300 to $500/month)
• Redis or a message queue for job scheduling ($50 to $100/month)
• Residential proxy subscriptions from one or more providers ($500 to $1,500/month)
• An engineer spending 10 to 20 hours per month on maintenance ($1,000 to $2,000/month)
• Monitoring infrastructure for alerting and observability ($100 to $300/month)

That totals $2,000 to $4,400/month before you write a single line of business logic. And this assumes things go smoothly. A major anti-bot change at a target site can eat an entire sprint of engineering time.

The calculation only favors self-hosting when:

• You have extremely specialized scraping needs that no API supports
• You already have a dedicated infrastructure team with spare capacity
• You need on-premises data processing for compliance reasons

For most teams building AI applications, search indexes, or data products, the total cost of ownership for self-hosting exceeds the cost of a managed API by 2x to 5x.

Making the decision

Here is a simplified decision framework:

Choose self-hosted Scrapy or Crawl4AI if: you have unusual requirements that no API handles, you already run infrastructure at scale, and you have engineers with scraping expertise on staff. Be honest about the ongoing maintenance cost.

Choose Firecrawl if: you want a clean developer experience with good AI output formats and your volume is under 100K pages per month, where the per-page premium is manageable.

Choose Apify if: you need pre-built scrapers for specific platforms (Amazon, Google Maps, social media) and the marketplace has an Actor that already solves your exact problem.

Choose Spider if: you want the lowest total cost of ownership at volume and you are building data pipelines that feed into AI applications. Spider’s included proxy network and native markdown output eliminate the two most common hidden costs (proxy management and data cleaning).

The bottom line

The real cost of scraping is rarely the per-page price on the pricing page. It is the sum of infrastructure, proxies, engineering time, failure handling, and data cleaning. Whatever tool you choose, estimate the total cost honestly — including your team’s time — before committing.