ScrapingBee’s pricing structure creates friction when scaling. JavaScript rendering and geotargeting aren’t available on the $49 Freelance or $99 Startup plans. You need the $249 Business tier to access these capabilities, a 5x jump for features competitors include at entry levels.

We evaluated six alternatives based on AI-ready output formats, pricing transparency, proxy infrastructure, performance benchmarks, and integration capabilities with modern development workflows.

Whether you’re building AI applications that need markdown output, running production scrapers at scale, or just tired of unpredictable credit consumption, here’s what works better.

ScrapingBee is a web scraping API that handles proxy rotation and headless browser management through a REST API. The platform uses machine learning to automatically retry failed requests for 60 seconds, optimizing proxy and header combinations to achieve 99.9% uptime.

Quick feature overview:

• Rotating residential and premium proxy pools
• JavaScript rendering with configurable headless browsers
• AI extraction rules using natural language prompts
• Geotargeting across 30+ countries (Business tier only)
• Pre-built endpoints for Google Search, Amazon, YouTube
• Credit-based pricing: 1 to 75 credits per request depending on features
• Integration with Make and n8n for no-code workflows

ScrapingBee works well for traditional scraping pipelines that output HTML or JSON. Less suitable for AI applications requiring markdown, projects needing transparent pricing, or developers who want comprehensive testing before committing to paid tiers.

While ScrapingBee delivers strong performance on standard targets (99.11% success on Amazon, 100% on GitHub), several limitations drive developers to explore alternatives:

ScrapingBee’s pricing page displays four tiers, but essential capabilities remain locked until you reach the Business level ($249/month).

JavaScript rendering and geotargeting are completely unavailable on the $49 Freelance and $99 Startup plans. These features only activate at $249/month, forcing developers who discover their target sites require JS rendering into an immediate $200 monthly increase.

The free tier provides 1,000 credits with a 5 concurrent request limit, but lacks access to premium proxies, JavaScript rendering, or geotargeting.

This makes it difficult to evaluate the service’s actual capabilities before committing to a paid plan, as Daniyar A. notes on Software Advice: “The free tier is quite limited, making it difficult to fully evaluate the service without committing to a paid plan.”

The credit-based pricing model multiplies costs based on which features each request triggers, but the system lacks clear documentation.

“Honestly the credits system is a little bit hard to understand and there’s no one concrete place (table) with all credits and/or calculations, I believe that would be very helpful to have,” explains Ivan R., an E-learning professional on Software Advice.

Another reviewer, David G., reinforces this concern: “The pricing is less predictable since the price per request depends on which features are being used or not.”

Here’s how the multipliers actually work:

• Basic proxy without JS: 1 credit
• Basic proxy with JS: 5 credits (5x multiplier)
• Premium proxy without JS: 10 credits (10x multiplier)
• Premium proxy with JS: 25 credits (25x multiplier)
• Stealth proxy: 75 credits (75x multiplier)
• AI query addition: +5 credits

Response times become a bottleneck when scraping sophisticated targets or running high-volume operations.

Marcus S., an AI Engineer, reports on Capterra: “Sometimes (it) can be quite slow on very hard-to-scrape websites.”

Daniyar A. experienced similar issues on Software Advice: “There were also occasional issues with scraping particularly complex websites.”

While testing, I found that ScrapingBee averaged 11.9 seconds per request across multiple domains. When scraping thousands of pages, that delay translates to hours of additional runtime compared to platforms delivering sub-second performance.

Modern AI workflows require clean markdown output for RAG systems, chatbots, and ML training. ScrapingBee returns HTML and JSON that need extensive post-processing.

While ScrapingBee offers an AI extraction feature accepting natural language prompts, the underlying architecture remains HTML-focused. The platform doesn’t provide native markdown conversion, forcing teams building with LangChain, LlamaIndex, or custom LLMs to add parsing layers.

Research shows that markdown output reduces token consumption for AI models by an average of 67% compared to raw HTML. Without this optimization, developers face higher LLM API costs and slower processing times.

ScrapingBee’s support quality is widely praised, with customers noting responses often arrive within minutes. However, the broader developer ecosystem remains limited.

David G. observes on Software Advice: “I don’t see much of a developer community, public Q&A’s, around the product.”

The platform offers SDKs for Python and JavaScript, but lacks native integrations with popular AI frameworks. Competitors provide LangChain connectors, Pydantic schema validation, and dedicated endpoints for AI workflows that ScrapingBee doesn’t match.

For teams building AI-powered applications, running production scrapers at enterprise scale, or simply wanting transparent pricing without feature gates, these limitations make exploring alternatives worthwhile.

These aren’t just “other scraping tools.” Each alternative specifically addresses the limitations outlined above, from transparent pricing to AI-native features and sub-second performance.

While ScrapingBee positions itself as a general-purpose scraping API, Firecrawl is purpose-built for modern AI and LLM applications that demand clean, structured, machine-readable data.

Why Firecrawl outperforms ScrapingBee for AI teams

The fundamental difference comes down to architecture and output format.

ScrapingBee delivers HTML and JSON that require post-processing before feeding into language models. Firecrawl outputs clean markdown natively, reducing token consumption by an average of 67% and eliminating the parsing layer entirely.

When you scrape with ScrapingBee, you get raw HTML wrapped in JSON. When you scrape with Firecrawl, you get LLM-ready markdown with structured metadata, screenshots, and links extracted automatically.

Performance advantage

Firecrawl’s caching system delivers sub-second response times for previously scraped content, while fresh scrapes complete in 2-5 seconds including JavaScript rendering.

ScrapingBee averaged 11.9 seconds per request in the same testing.

One developer confirmed the speed difference: “Moved our internal agent’s web scraping tool from Apify to Firecrawl because it benchmarked 50x faster with AgentOps,” shared Alex Reibman on Twitter.

For JavaScript-heavy websites, Firecrawl’s automatic rendering maintains 99%+ data integrity without manual timeout configuration. ScrapingBee requires you to enable JavaScript rendering explicitly and set appropriate wait times for each target site.

Natural language extraction vs CSS selectors

Firecrawl’s /extract endpoint accepts plain English instructions:

from firecrawl import FirecrawlApp

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

result = app.extract_url('https://example.com/products', {
    'prompt': 'Extract product names, prices, and availability status',
    'schema': {
        'type': 'object',
        'properties': {
            'products': {
                'type': 'array',
                'items': {
                    'type': 'object',
                    'properties': {
                        'name': {'type': 'string'},
                        'price': {'type': 'number'},
                        'available': {'type': 'boolean'}
                    }
                }
            }
        }
    }
})

ScrapingBee requires CSS selectors or XPath expressions:

from scrapingbee import ScrapingBeeClient

client = ScrapingBeeClient(api_key='YOUR_API_KEY')

response = client.get(
    'https://example.com/products',
    params={
        'extract_rules': {
            'products': {
                'selector': 'div.product',
                'type': 'list',
                'output': {
                    'name': '.product-title',
                    'price': '.product-price',
                    'available': '.stock-status'
                }
            }
        }
    }
)

When the website changes its CSS classes (which happens frequently), Firecrawl’s natural language extraction continues working while ScrapingBee’s selectors break and require manual updates.

Developer-first doesn’t mean developers-only

While Firecrawl is API-native, it integrates seamlessly with no-code and low-code platforms:

Compatible with no-code tools:

• n8n: Build automated scraping workflows without coding
• Zapier: Connect Firecrawl to 6,000+ apps with triggers
• Make: Create complex automation scenarios
• LangChain/LlamaIndex: Single-line web scraping in AI pipelines
• Flowise/Langflow: Pipe web data into AI agent workflows

ScrapingBee offers Make and n8n integrations but lacks native AI framework support, requiring custom connectors for LangChain or LlamaIndex implementations.

When to choose Firecrawl over ScrapingBee

Choose Firecrawl if you’re:

• Building AI applications (chatbots, RAG systems, ML models, AI agents)
• Need LLM-ready markdown without post-processing
• Working with JavaScript-heavy, dynamic websites
• Want sub-second response times for cached content
• Require automatic adaptation to website changes
• Scaling beyond occasional scraping to production workflows
• Using LangChain, LlamaIndex, or other AI frameworks
• Want to test thoroughly before committing (Playground access)

Choose ScrapingBee if you’re:

• Running traditional scraping pipelines that output HTML/JSON
• Don’t require AI-optimized outputs or markdown conversion
• Comfortable with CSS selector maintenance

For teams building AI-powered products, Firecrawl isn’t just faster. It’s purpose-built for the workflow, delivering production-ready markdown that feeds directly into your models without the configuration overhead, maintenance burden, or architectural limitations that affect HTML-based scrapers.

Apify is a full-stack web scraping and automation platform that combines a powerful API with a marketplace of 4,000+ pre-built scrapers called Actors.

Why Apify outperforms ScrapingBee for flexibility and scale

The core difference is architectural approach. ScrapingBee is a unified API with limited pre-built endpoints for specific sites. Apify operates as a complete cloud platform where you can use ready-made Actors, build custom scrapers, or combine both approaches.

While ScrapingBee limits you to its pre-configured Google, Amazon, and YouTube scrapers, Apify’s marketplace provides specialized Actors for Instagram, TikTok, LinkedIn, Twitter, Facebook, Google Maps, and hundreds of other platforms. Each Actor is maintained by the community or Apify team, handling site-specific anti-bot measures automatically.

Apify’s marketplace eliminates the need to build most scrapers from scratch. All plans, including the free tier, provide complete access to Actors in the Apify Store.

For example, scraping Instagram with ScrapingBee requires:

1. Understanding Instagram’s HTML structure
2. Writing CSS selectors or XPath
3. Handling Instagram’s anti-bot measures manually
4. Maintaining selectors when Instagram updates

With Apify’s Instagram Scraper Actor, you:

1. Select the Actor from the marketplace
2. Input the profiles or hashtags to scrape
3. Run the Actor
4. Receive structured JSON output

Popular Actors include:

• Instagram Scraper: Extract posts, profiles, hashtags, stories
• LinkedIn Scraper: Collect company data, job listings, profiles
• Google Maps Scraper: Extract business listings with reviews and photos
• Amazon Product Scraper: Get product details, prices, reviews at scale
• Twitter Scraper: Collect tweets, profiles, trends
• TikTok Scraper: Extract videos, user profiles, hashtags

Each Actor handles the platform-specific challenges (authentication, rate limiting, anti-bot measures) that would consume weeks of development time.

Developer flexibility

Unlike ScrapingBee’s API-only approach, Apify lets you code, host, and run custom solutions on Apify’s infrastructure.

Build Actors locally or directly in the platform’s Web IDE using Node.js or Python. Deploy your code to Apify’s cloud, where it runs on scalable infrastructure with automatic proxy rotation, browser management, and data storage.

This means you can:

• Start with a marketplace Actor for 80% of your needs
• Customize it for your specific use case
• Deploy your modified version to Apify’s cloud
• Scale to millions of requests without managing servers

ScrapingBee offers no equivalent. You get the API as-is with no ability to deploy custom scraping logic to their infrastructure.

When to choose Apify over ScrapingBee

Choose Apify if you:

• Need pre-built scrapers for popular platforms (Instagram, TikTok, LinkedIn, Google Maps)
• Want API-first architecture for programmatic control
• Require full cloud operation without desktop software
• Need to deploy and host custom scraping logic
• Want flexibility to write custom scrapers when marketplace doesn’t have what you need
• Building workflows that integrate with modern tools (Zapier, Make, Airbyte)
• Scaling beyond concurrent request limits
• Need data stored in cloud (datasets) vs downloading per request

Apify’s combination of marketplace Actors and custom deployment capability delivers more flexibility at comparable or lower costs for platform-specific scraping. The trade-off is added complexity in understanding compute units vs ScrapingBee’s simpler per-request model.

Scrape.do positions itself as a performance-first web scraping API with aggressive caching and transparent opt-in pricing.

The critical difference lies in default settings. ScrapingBee enables JavaScript rendering by default, consuming 5 credits per request unless you explicitly disable it. This catches developers off guard when credits disappear 5x faster than expected.

Scrape.do takes the opposite approach. All parameters are disabled by default. You opt in to rendering or premium proxies explicitly, and the credit multiplier applies only when you choose to enable features.

No surprise 5x burn rate because you forgot to flip a switch.

Scrape.do doesn’t offer AI extraction features. You’ll need to parse responses with BeautifulSoup or similar libraries. The platform provides custom scrapers for a handful of popular domains (Amazon, Google), but there’s no plain-English instruction interface like ScrapingBee’s AI engine or Firecrawl’s natural language extraction.

For teams prioritizing speed and cost over AI-powered extraction, Scrape.do delivers the best performance-to-price ratio among tested alternatives.

When to choose Scrape.do over ScrapingBee

Choose Scrape.do if you:

• Want transparent pricing without hidden multipliers
• Prefer opt-in parameters vs remembering to disable defaults
• Need reliable performance without surprise stealth proxy costs

Browse.AI is a visual web scraper that lets non-technical users extract data through point-and-click selection, with built-in monitoring for website changes.

Why Browse.AI outperforms ScrapingBee for business users

The fundamental difference is interface philosophy.

ScrapingBee requires API integration and CSS selector knowledge, even with its AI extraction features. Browse.AI provides a Chrome extension and visual interface where you click the data you want, and it automatically generates the scraper.

For business users who need data but don’t want to write API calls, Browse.AI removes the technical barrier entirely.

The robot training advantage

Browse.AI uses “robots” that you train by demonstrating what data to extract. The workflow:

1. Install the Chrome extension
2. Navigate to your target page
3. Click the data points you want (prices, titles, descriptions)
4. Browse.AI auto-detects patterns and builds the scraper
5. Run on-demand or schedule for automatic execution

The platform handles pagination automatically, works with infinite scroll, and adapts when websites change their layout. Chris C., a Partner Development Specialist, explains on G2: “Everything is no-code, so as a non-technical person I felt empowered to be able to do anything I needed with a bit of learning and testing.”

ScrapingBee requires you to identify CSS selectors or write AI extraction rules for each data point, then handle pagination logic manually in your code.

Built-in monitoring vs custom implementation

Browse.AI’s monitoring feature tracks specific data points and sends alerts when they change. Set up monitors for competitor prices, product availability, or job listings, and receive email notifications automatically.

For example, monitoring competitor pricing:

• Train a robot to extract prices from competitor product pages
• Set monitoring frequency (hourly, daily, weekly)
• Receive alerts when prices change
• Export historical data to track trends

With ScrapingBee, you’d need to build this infrastructure yourself, including storage for historical data, change detection logic, and notification systems.

Template marketplace

Browse.AI provides 150+ pre-built robots for popular websites:

• E-commerce: Amazon, eBay, Etsy, Airbnb
• Social Media: LinkedIn, Twitter (limited)
• Business: Yelp, Google Maps, Crunchbase
• Real Estate: Zillow, Realtor.com, Compass
• Job Boards: Indeed, LinkedIn Jobs, AngelList

These templates work immediately without training. Enter your search parameters and start extracting.

ScrapingBee’s pre-built endpoints cover only Google Search, Amazon, and YouTube, requiring custom scraper development for other platforms.

Browse.AI connects with 7,000+ applications through:

• Zapier: Trigger workflows when data is extracted or changes detected
• Google Sheets: Auto-sync scraped data to spreadsheets
• Webhooks: Push data to your systems in real-time
• REST API: Programmatic access for developers
• Make.com, Pabbly Connect: Visual automation builders

For business users building workflows without code, these native integrations eliminate the need for custom development.

However, Browse.AI struggles with some highly protected sites and may require manual intervention when websites implement aggressive anti-bot measures. The Chrome extension dependency means you can’t run it purely server-side like API-first solutions.

Customer support quality varies. While some users praise fast responses, others experienced delays.

When to choose Browse.AI over ScrapingBee

Choose Browse.AI if you:

• Need a visual interface without API integration
• Business user without coding background
• Want built-in monitoring and change alerts
• Scraping common sites with available templates
• Need data flowing into Google Sheets or business apps
• Prefer point-and-click over CSS selectors
• Want to test thoroughly with a free tier

Scrapy is a Python web scraping framework that provides a complete architecture for building, deploying, and maintaining web crawlers at scale.

Why Scrapy outperforms ScrapingBee for developers

The core difference is control and cost.

ScrapingBee is a managed service where you pay per request and work within their API constraints. Scrapy is an open-source framework where you own the infrastructure, pay only for hosting, and customize every aspect of the scraping pipeline.

For developers comfortable with Python who need maximum flexibility or have budget constraints, Scrapy offers capabilities that paid services can’t match.

Asynchronous architecture advantage

Scrapy uses Twisted, an asynchronous networking library, to handle multiple requests simultaneously without blocking. This makes it exceptionally fast for large-scale projects.

When scraping 10,000 pages:

• Scrapy: Sends hundreds of concurrent requests, completing in minutes
• ScrapingBee: Limited by concurrent request caps (10-300 depending on plan tier)

Scrapy’s asynchronous engine automatically manages request queuing, retries, and throttling. You define the concurrency level and download delays, and Scrapy handles the rest.

Scrapy provides:

Built-in middleware system:

• Request/response processing pipelines
• Automatic cookie handling
• User-agent rotation
• Custom header injection

Data processing:

• Item pipelines for cleaning and validating
• Multiple export formats (JSON, CSV, XML, database)
• Built-in XPath and CSS selectors

Spider management:

• Command-line tools for running and managing spiders
• Built-in debugging and logging
• Statistics collection

Extensibility:

• Custom middleware development
• Plugin architecture
• Integration with any Python library

ScrapingBee handles proxies and JavaScript rendering but offers limited control over the request pipeline, data processing, or export formats.

When to choose Scrapy over ScrapingBee

Choose Scrapy if you:

• Comfortable with Python development
• Scraping millions of pages monthly (cost savings)
• Need complete control over scraping logic
• Building complex crawlers with custom processing
• Want zero vendor lock-in
• Have infrastructure to host scrapers
• Scraping sites that don’t require advanced anti-bot bypassing
• Building long-term scraping infrastructure

Scrapy offers unmatched flexibility and cost-efficiency at scale, but requires technical expertise and infrastructure management that many teams prefer to avoid.

Octoparse is a desktop application for web scraping that uses visual point-and-click configuration, designed for users who need powerful scraping without coding.

Why Octoparse outperforms ScrapingBee for analysts

The defining difference is the workflow approach.

ScrapingBee requires API integration into your applications or scripts. Octoparse provides a standalone desktop application where you visually configure scrapers and export data directly to spreadsheets or databases.

For data analysts, market researchers, and business intelligence teams who work primarily with Excel and don’t write code, Octoparse removes the development requirement entirely.

Template marketplace eliminates configuration

Octoparse provides 600+ pre-built templates covering:

• E-commerce: Amazon, eBay, Alibaba, Walmart, Target
• Social Media: Twitter, Facebook, Instagram, LinkedIn (limited)
• Business Directories: Yelp, Yellow Pages, Google Maps
• Real Estate: Zillow, Realtor.com, Trulia
• Job Sites: Indeed, LinkedIn Jobs, Glassdoor
• Travel: TripAdvisor, Booking.com, Airbnb

These templates require zero configuration. Enter search parameters (product name, location, category) and click “Run.” The scraper executes immediately.

ScrapingBee’s limited pre-built endpoints require custom API development for most scraping targets.

When to choose Octoparse over ScrapingBee

Choose Octoparse if you:

• Are a data analyst or researcher without coding skills
• Need a visual interface for configuring scrapers
• Want to scrape locally without ongoing costs
• Prefer desktop software over API integration
• Exporting directly to Excel, CSV, or databases
• Need pre-built templates for common sites
• Want a genuine free tier for small projects
• Building scheduled scraping workflows without code

Octoparse serves the large market of business users who need powerful scraping capabilities without learning to code, though this comes at the cost of the flexibility and integration capabilities that API-first solutions provide.

If you’re scraping basic HTML pages on a small scale and don’t need JavaScript rendering, ScrapingBee works fine. But if you’re:

• Building AI applications that need markdown, not HTML soup
• Tired of $200 surprise jumps when you discover JS rendering is feature-gated
• Dealing with credit multipliers that make budgeting impossible
• Waiting 11+ seconds per request when competitors deliver in 2-5s
• Working with LangChain, LlamaIndex, or custom LLMs

…then Firecrawl solves these problems. All features are available on all tiers. 1 credit per page regardless of JavaScript. Native markdown output that reduces LLM token costs by 67%. Sub-second cached responses.

Test your actual URLs in the Playground at https://www.firecrawl.dev/playground before committing to anything. No signup required.

1. Is ScrapingBee worth the cost?

ScrapingBee works well for traditional HTML scraping with responsive support. However, feature-gating (JS rendering requires $249 tier), unpredictable credit multipliers (1x-75x), and 11.9s average response times make alternatives like Firecrawl better for AI applications and budget-conscious teams.

2. How do I scrape websites for AI applications?

Use Firecrawl for native markdown output, reducing LLM token consumption by 67% versus HTML. It provides natural language extraction that adapts when websites change, native LangChain/LlamaIndex integration, and sub-second cached responses. Traditional HTML scrapers require extensive post-processing for AI workflows.

3. Why do users switch from ScrapingBee to other tools?

Users switch due to feature-gating (JS rendering locked behind $249 tier), unpredictable credit multipliers (1x-75x causing budget overruns), lack of AI-optimized markdown output, and slow 11.9s response times. Alternatives like Firecrawl provide all features at entry tiers with transparent pricing.

4. Can I use web scraping APIs for commercial projects?

Yes, most APIs (Firecrawl, ScrapingBee, Apify, Browse.AI, Scrapy) permit commercial use. However, you must respect target websites’ terms of service, robots.txt files, and implement reasonable rate limiting. Verify scraping legality for specific sites, especially for copyrighted content redistribution.

5. Does ScrapingBee work with JavaScript websites?

Yes, but JS rendering is unavailable on $49 Freelance and $99 Startup tiers, requires Business tier ($249+), and consumes 5x credits per request. Firecrawl provides automatic JS rendering on all tiers for 1 credit per page with smart waiting detection.

6. What’s better than ScrapingBee for scraping at scale?

Firecrawl outperforms ScrapingBee with 2-5s response times versus 11.9s, native markdown output reducing LLM tokens by 67%, and transparent 1-credit-per-page pricing across all tiers. At 500K pages monthly, Firecrawl costs $333 versus ScrapingBee’s $249-599 depending on feature usage.

7. Can I use Firecrawl without coding experience?

Yes, Firecrawl integrates with no-code platforms like Zapier, Make, n8n, and Bubble.io. You can also test scraping visually in the Playground before using any code or automation tools.