- [OpenClaw vs Claude Code: which AI agent should you actually use?](https://parallel.ai/articles/openclaw-vs-claude-code-which-ai-agent-should-you-actually-use)
Reading time: 12 min
July 27, 2026
# ScrapingBee vs. Parallel: a scraping API with a search feature, or a search API
ScrapingBee's job is getting a page when the page is difficult: proxy rotation, JavaScript rendering, stealth modes, screenshots, and a set of dedicated endpoints for named targets including Google Search. It has been doing that reliably for years and is a common default for developers who need a fetch layer that does not break. Parallel starts from retrieval rather than fetching. The two meet at exactly one point (both can hand an agent web results) and diverge everywhere else, including in how they bill.
Tags:Comparison
Reading time: 4 min## **The products**
ScrapingBee is an API-first scraping platform. The core call fetches a URL through rotating proxies with optional JavaScript rendering and hands back HTML, a screenshot, or extracted output. Around that sit premium and stealth proxy tiers for harder targets, extraction rules and an AI Query mode for turning pages into structured JSON, browser scenarios for click-and-scroll flows, geotargeting, and dedicated APIs for Google Search, Amazon, Walmart, and YouTube. It integrates with Make, n8n, and Zapier, and the company is based in France.
Parallel's surface is Search, Extract, Task, Responses, FindAll, Entity Search, and Monitor. There are no proxies, no stealth modes, no browser scenarios, and no screenshots: if a site is actively blocking you, Parallel has no answer and ScrapingBee does.
## **The search comparison**
ScrapingBee's Google Search API is a SERP endpoint: it runs the query, handles the rendering and unblocking, and returns structured results. What comes back is Google's ranking and Google's description snippets, which means an agent that needs to reason usually has to fetch the underlying pages next. ScrapingBee will do that too (that is its core product) but it is a second call per result, on the same credit meter.
Parallel's Search API takes a natural-language objective and returns ranked URLs with excerpts pulled from the page bodies and selected against that objective, in one call. Three modes set the trade: Turbo at ~200ms and $1 per 1,000 requests, Basic at ~1s and $5 per 1,000, and Advanced at ~3s and $5 per 1,000, which is the default. Extract handles full pages at $1 per 1,000 URLs when excerpts are not enough.
## **Pricing**
ScrapingBee sells monthly plans measured in API credits and concurrency: Freelance at $49 for 250,000 credits and 50 concurrent requests, Startup at $99 for 1,000,000 and 100, Business at $249 for 3,000,000 and 200, and Business+ at $599 for 8,000,000 and 400, with custom enterprise terms above that. There are 1,000 free credits to start, no card required.
The headline credit count is not the useful number. A request consumes more credits when you turn on JavaScript rendering, premium or stealth proxies, screenshots, or the dedicated APIs, and those are exactly the features you enable on the targets that need them. Two teams on the same plan can get very different effective volumes. Model cost by request type rather than by plan.
Parallel charges per request with no plan and no credits to forecast:
| Product | Price |
|---|---|
| Search | $1 per 1,000 in Turbo, $5 per 1,000 in Basic and Advanced, 10 results with excerpts included |
| Extract | $1 per 1,000 URLs, with JavaScript rendering and PDFs included rather than surcharged |
| Task API | $5 to $2,400 per 1,000 runs |
| Responses API | $10 to $250 per 1,000 |
| Monitor | $3 per 1,000 executions |
| Entity Search | $5 per 1,000 |
| FindAll | Fixed cost plus per match |
| Free credits | $5 every month, applied automatically |
The structural difference is that ScrapingBee's multipliers price difficulty, and Parallel's flat rates do not. That favours ScrapingBee on easy targets and Parallel on hard ones, with the caveat that Parallel simply cannot reach the hardest targets at all.
_Note: For the latest pricing, always check official documentation._
## **Throughput**
ScrapingBee meters concurrent requests and ties them to the plan, from 50 on Freelance to 400 on Business+. That is the right unit for bulk fetching, where you want as many pages in flight as possible.
Parallel meters requests per minute and does not tie them to spend: 600 for Search, Extract, and Entity Search, 300 for Monitor, 300 per hour for FindAll runs, with GET polling excluded and custom limits available on enterprise plans. That suits many short independent lookups rather than a sustained crawl.
## **Developer experience and compliance**
ScrapingBee is deliberately simple to call and well documented, with a request builder and no-code integrations for teams that live in Make or n8n. It expects you to think in endpoints, parameters, rendering trade-offs, retries, and output parsing: good if you want control, friction if you just want an answer.
Parallel ships Python and TypeScript SDKs, an MCP server, and a playground, with the Responses API OpenAI SDK-compatible:
12345678910111213from parallel import Parallel
client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])
search = client.beta.search(
objective="your goal",
mode="turbo",
excerpts={"max_chars_per_result": 10000},
)``` from parallel import Parallel client = Parallel(api_key=os.environ["PARALLEL_API_KEY"]) search = client.beta.search( objective="your goal", mode="turbo", excerpts={"max_chars_per_result": 10000}, )``` Parallel is SOC 2 Type 2 certified, offers a Data Processing Addendum and zero data retention, and commits contractually to not training on customer data, with a public status page and trust center.
## **When to use each**
Choose ScrapingBee when fetching is the hard part. Sites behind anti-bot protection, pages that need a click-through before the data appears, screenshots, geotargeted rendering, and the dedicated Amazon or Walmart endpoints are all things it does and Parallel does not. If your pipeline is a fetch layer with search as an occasional side quest, this is the better-shaped product.
Choose Parallel when finding and compressing is the hard part. Turbo returns page-body excerpts in about 200ms at $1 per 1,000 requests, so there is no second fetch call and no credit multiplier to reason about, and the input tokens your model pays for drop accordingly. Task, Responses, FindAll, Entity Search, and Monitor cover research, list building, and change tracking that a scraping API leaves to you.
The clean split, if you need both: ScrapingBee for the named difficult targets, an LLM-native search API for the open web an agent reads in real time.
**Related reading: **Firecrawl vs. Parallel[Firecrawl vs. Parallel] · Bright Data SERP API vs. Parallel[Bright Data SERP API vs. Parallel] · Apify vs. Parallel[Apify vs. Parallel].
By Parallel
July 27, 2026