Octoparse vs Zyte(2026)
Octoparse is better for teams that need no coding required. Zyte is the stronger choice if built by the creators of scrapy. Octoparse is freemium (from $119/month) and Zyte is paid (from from $0.13/1K requests).
Full feature breakdown, pricing details, and pros & cons below.
By Bikram NathLast updated
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Octoparse
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 OctoparseZyte
Zyte (formerly Scrapinghub) is a web scraping platform from the team behind the open-source Scrapy framework. Its Zyte API unifies proxies, headless browser rendering, and anti-ban handling with usage-based, per-successful-response pricing tiered by site difficulty.
Starting at from $0.13/1K requests
Visit ZyteHow Do Octoparse and Zyte Compare on Features?
| Feature | Octoparse | Zyte |
|---|---|---|
| Pricing model | freemium | paid |
| Starting price | $119/month | from $0.13/1K requests |
| 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 | ✓ | — |
| Zyte API (proxies + browser + anti-ban) | — | ✓ |
| Smart proxy management | — | ✓ |
| Headless browser rendering | — | ✓ |
| Automatic ban detection | — | ✓ |
| Scrapy Cloud hosting | — | ✓ |
| Per-successful-response billing | — | ✓ |
| Difficulty-tiered pricing | — | ✓ |
Octoparse Pros and Cons vs Zyte
Octoparse
Zyte
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.
Deep dive: Zyte
When to choose Zyte
Zyte is the right choice when the team already uses Scrapy or has Python-based scraping infrastructure and wants managed proxy and anti-ban services without switching frameworks. Built by the creators of Scrapy, Zyte provides the Zyte API, which wraps proxy management, browser rendering, and anti-bot bypass into a single endpoint, and Scrapy Cloud, which hosts Scrapy spiders in the cloud with scheduling, monitoring, and log viewing. The pay-per-successful-response model is a genuine differentiator: the team only pays for requests that return a 200-status response, eliminating the billing risk of failed requests consuming credits. Choose Zyte when the team has existing Scrapy spiders and wants to scale them without managing server infrastructure, or when the target sites vary in difficulty and the team wants pricing that reflects actual difficulty rather than a flat per-request rate. Avoid it when the team does not use Python, when a no-code visual builder is required, or when the team needs structured output formats like Markdown for LLM ingestion.
Real-world use case
A data team at a price comparison startup runs 50 Scrapy spiders on Zyte's Scrapy Cloud platform. Each spider scrapes a different e-commerce site for product prices and availability, running on a configurable schedule from every 2 hours to daily depending on the site's update frequency. The spiders use the Zyte API for proxy management and anti-bot handling, with automatic escalation from datacenter to residential proxies when the target site's protection level requires it. The difficulty-tiered pricing means simple sites cost /bin/zsh.13 per 1,000 requests while heavily protected sites cost up to per 1,000 requests. The team processes approximately 2 million pages per month across all spiders, with an average cost of /bin/zsh.80 per 1,000 requests, totaling about ,600 per month. The tradeoff: the per-request cost is unpredictable until the spider has run against each target site long enough to establish the difficulty tier.
Hidden gotchas
The difficulty-tiered pricing model means costs can vary 30x between easy and hard sites. A spider that scrapes simple HTML pages might cost /bin/zsh.13 per 1,000 requests, while the same spider pointed at a Cloudflare-protected site could cost per 1,000 requests. The difficulty assessment is automatic and not transparent: teams cannot predict which tier a new target site will fall into without running test requests first. Scrapy Cloud uses a proprietary deployment format that requires the shub CLI tool and does not support standard Docker containers, locking the team into Zyte's deployment pipeline. Scrapy Cloud's job monitoring dashboard shows runtime metrics but does not provide cost breakdowns per spider per run, making cost attribution across spiders and projects manual. The Zyte API's browser rendering mode is significantly more expensive than HTTP-only mode, and some target sites that appear to require JavaScript actually serve the required data in the initial HTML response, so testing with HTTP-only first can save substantial costs. Spider-level concurrency and download delays must be tuned per target site, and the default settings can trigger rate limiting or bans on targets that expect slower request patterns.
Pricing breakdown
Zyte API pricing is per successful response, starting at /bin/zsh.13 per 1,000 for easy sites (simple HTML, no protection) and scaling to per 1,000 for the hardest tier (heavy anti-bot, browser rendering, residential proxies required). Scrapy Cloud is priced per compute unit, with the free tier including 1 concurrent spider and limited storage. The Professional plan at per month includes more concurrency and longer data retention. A team running 500,000 easy-tier requests and 100,000 hard-tier requests per month would pay approximately for easy requests plus for hard requests, totaling per month on the Zyte API alone, plus the Scrapy Cloud hosting fee.
Should You Use Octoparse or Zyte?
For most teams, Octoparse is the better default: it offers no coding required and is freemium (from $119/month). Choose Zyte instead if built by the creators of scrapy matters more than monthly pricing is steep vs api tools. There is no universal winner — the right pick depends on your budget, team size, and whether you value no coding required or built by the creators of scrapy more.
Choose Octoparse if…
- •No coding required
- •Friendly for non-developers and analysts
- •Free plan available
Choose Zyte if…
- •Built by the creators of Scrapy
- •Pay only for successful responses
- •Very cheap entry tier for simple sites