Firecrawl vs Octoparse(2026)
Firecrawl is better for teams that need purpose-built for ai/llm workflows. Octoparse is the stronger choice if no coding required. Firecrawl is freemium (from $16/month) and Octoparse is freemium (from $119/month).
Full feature breakdown, pricing details, and pros & cons below.
By Bikram NathLast updated
Affiliate disclosure: Some “Visit” links on this page are affiliate links. We may earn a commission if you sign up — at no extra cost to you. It does not affect our rankings or editorial coverage. Learn more.
Firecrawl
Firecrawl is a scraping and crawling API built for AI workflows — it crawls a site and returns clean Markdown or structured JSON ready to feed into LLMs and RAG pipelines. It handles JavaScript, proxies, and rate limits, and exposes scrape, crawl, map, and extract endpoints.
Starting at $16/month
Visit FirecrawlOctoparse
Octoparse is a no-code, point-and-click web scraping tool that lets non-developers build scrapers visually. It offers cloud extraction, scheduling, IP rotation, and pre-built task templates for popular sites — turning web pages into structured data without writing code.
Starting at $119/month
Visit OctoparseHow Do Firecrawl and Octoparse Compare on Features?
| Feature | Firecrawl | Octoparse |
|---|---|---|
| Pricing model | freemium | freemium |
| Starting price | $16/month | $119/month |
| Scrape, crawl, map, and extract endpoints | ✓ | — |
| Clean Markdown + structured JSON output | ✓ | — |
| LLM-ready output for RAG pipelines | ✓ | — |
| JavaScript rendering | ✓ | — |
| Automatic proxy + anti-bot handling | ✓ | — |
| SDKs for Python and Node | ✓ | — |
| LangChain / LlamaIndex integrations | ✓ | — |
| Visual point-and-click builder | — | ✓ |
| Cloud-based extraction | — | ✓ |
| Scheduled scraping | — | ✓ |
| IP rotation | — | ✓ |
| Pre-built task templates | — | ✓ |
| Auto-detect data fields | — | ✓ |
| Export to CSV/Excel/database/API | — | ✓ |
Firecrawl Pros and Cons vs Octoparse
Firecrawl
Octoparse
Deep dive: Firecrawl
When to choose Firecrawl
Firecrawl is the right choice when the primary use case is feeding web content into AI or LLM pipelines. Its core differentiator is outputting clean Markdown and structured JSON from any web page, eliminating the HTML-to-text preprocessing step that every RAG pipeline and fine-tuning dataset otherwise requires. The API supports four modes: scrape (single page), crawl (follow links from a starting URL), map (discover all URLs on a domain), and extract (structured data extraction with schema). The free tier includes 1,000 credits per month, making it accessible for prototyping. Choose Firecrawl when building RAG applications, training data pipelines, or any workflow that needs clean text from web pages. It is also strong for documentation crawling, where the output feeds directly into a vector database. Avoid it when the scraping target requires complex interaction like form filling, pagination with infinite scroll, or login-protected content, where a full browser automation tool like Playwright or Apify is more appropriate.
Real-world use case
A developer building a customer support chatbot uses Firecrawl to ingest an entire product documentation site into a Pinecone vector database. The crawl endpoint is pointed at the docs root URL with a maximum depth of 3, and Firecrawl returns each page as clean Markdown with metadata including the page title, description, and canonical URL. The developer chunks the Markdown output, generates embeddings via OpenAI, and upserts them into Pinecone. The entire ingestion pipeline is 60 lines of Python. The documentation site has 400 pages, consuming 400 credits on the free tier. The developer schedules a weekly re-crawl to catch documentation updates. The tradeoff: Firecrawl's Markdown output is clean but not always structurally perfect. Code blocks, nested lists, and complex tables can lose formatting nuance. The developer adds a post-processing step to fix code block language tags, adding about 10 lines of Python.
Hidden gotchas
Credits do not roll over between billing periods on monthly plans. If the team does not use all 1,000 free credits in a given month, they are lost. The crawl endpoint follows links by default, and without a URL filter or maximum page limit, a crawl of a large site can consume thousands of credits unexpectedly. The extract mode, which uses an LLM to extract structured data based on a schema, costs more credits per page than simple scraping and the LLM extraction quality varies based on page complexity. The Markdown output uses an internal HTML-to-Markdown converter that handles most common patterns well but can produce unexpected output for pages with heavy use of CSS-based content rendering, iframes, or shadow DOM components. The JavaScript rendering is enabled by default on all requests, which provides better content extraction but consumes more credits per page than a hypothetical no-JS mode. Rate limiting on the free tier caps concurrent requests, and the API does not return a retry-after header on 429 responses.
Pricing breakdown
The free plan includes 1,000 credits per month. The Hobby plan at per month includes 3,000 credits. The Standard plan at per month includes 100,000 credits. The Growth plan at per month includes 500,000 credits. A scrape request costs 1 credit. A crawl request costs 1 credit per page crawled. The extract endpoint costs 5 credits per page. A team crawling 5 documentation sites with an average of 200 pages each weekly would consume 4,000 credits per month, fitting on the Hobby plan. Adding extract mode to 500 pages per month increases consumption by 2,500 credits, pushing the total to 6,500 credits and requiring the Standard plan.
Deep dive: Octoparse
When to choose Octoparse
Octoparse is the right choice when the scraping team consists of non-developers who need a visual, point-and-click interface to extract data from websites without writing code. It is strongest for business analysts, marketing teams, and operations teams that need to collect competitor pricing, product catalogs, or lead lists and export the results to Excel or a database. The auto-detect feature attempts to identify data fields on a page automatically, and pre-built task templates for common sites like Amazon, eBay, and Yellow Pages allow extraction to start within minutes. Choose Octoparse when the team has no coding skills and the scraping targets are relatively standard e-commerce or directory pages. Avoid it when the scraping requires custom logic, when the targets use heavy anti-bot protection, or when the team needs an API-first approach that integrates into existing data pipelines.
Real-world use case
A procurement team at a mid-size retailer uses Octoparse to scrape product prices and availability from 15 supplier websites weekly. Each website has a pre-configured task that navigates to the product listing page, paginates through results, and extracts the product name, price, and stock status. The team schedules tasks to run every Monday morning via the cloud scheduler, and results are exported to a shared Google Sheet. The team has no developers and built all 15 tasks using the visual editor in about 2 hours total. The tradeoff: the monthly cost at per month for the Standard plan is higher than API-based tools like ScrapingBee or Firecrawl for the same volume. The team accepts this because they cannot write code and the visual builder eliminates their dependency on engineering.
Hidden gotchas
The pricing structure is confusing: Octoparse lists different prices on different parts of their website, and the actual cost depends on whether the plan is billed monthly, annually, or via the legacy pricing model. The Standard plan at per month includes cloud-based extraction with limited concurrency, and the Professional plan at per month adds more concurrent tasks and advanced features. The desktop application runs on Windows only, which excludes Mac-only teams from local extraction. The cloud extraction service has a queue system that can delay task execution during peak hours, meaning scheduled tasks may not start at the exact configured time. Anti-bot handling is basic compared to Bright Data or Apify: Octoparse rotates IP addresses and adds delays between requests, but sites with sophisticated bot detection like Cloudflare or Akamai will block many extraction attempts. The auto-detect feature works well for simple table-based pages but produces poor results on modern JavaScript-rendered sites with complex DOM structures.
Pricing breakdown
The free plan allows limited local extraction with no cloud features. The Standard plan at per month includes cloud extraction and scheduling. The Professional plan at per month adds more concurrency, advanced scheduling, and API access. A team running 15 scheduled tasks weekly with an average of 500 pages per task, totaling 30,000 pages per month, fits on the Standard plan. For comparison, the same volume on ScrapingBee would cost approximately per month for basic HTML extraction, and on Apify approximately per month using HTTP-based crawlers.
Should You Use Firecrawl or Octoparse?
For most teams, Firecrawl is the better default: it offers purpose-built for ai/llm workflows and is freemium (from $16/month). Choose Octoparse instead if no coding required matters more than credits do not roll over on monthly plans. There is no universal winner — the right pick depends on your budget, team size, and whether you value purpose-built for ai/llm workflows or no coding required more.
Choose Firecrawl if…
- •Purpose-built for AI/LLM workflows
- •Clean Markdown output saves prep work
- •Free tier with 1,000 credits/month
Choose Octoparse if…
- •No coding required
- •Friendly for non-developers and analysts
- •Free plan available