- [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
# Apify vs. Parallel: a scraper marketplace against a retrieval API
Apify is the largest name in web data by a distance, and it is not really a search API at all. It is a marketplace and runtime for Actors: thousands of prebuilt scrapers, plus the infrastructure to write and deploy your own. One of those Actors happens to do agent-style web search, which is where the comparison with Parallel starts. The two answer different questions. Apify answers "how do I get data out of this specific site?" Parallel answers "what does the web say about this?"
Tags:Comparison
Reading time: 6 min## **What Apify is**
An Actor is a serverless cloud program that scrapes, crawls, or automates something. You take JSON in, do work that runs from seconds to hours, and write structured results to a dataset. Apify Store carries tens of thousands of them, most written by third parties, covering specific targets: Amazon listings, Google Maps places, Instagram profiles, LinkedIn, Zillow, and long-tail sites nobody else has bothered with.
That catalogue is the product. If your job is "get every listing off this niche marketplace," someone has probably already written and maintained the scraper, and you rent it instead of building it. Nothing in the LLM-native search category comes close to that coverage, and it is worth being clear that Parallel does not compete with it.
Two Actors sit closest to Parallel's territory. RAG Web Browser queries Google, scrapes the top N results with a real browser, and returns clean Markdown for an LLM; it can run in Standby mode as a persistent HTTP server or an MCP endpoint, which cuts the cold-start penalty. Website Content Crawler turns a site into Markdown or JSON for RAG pipelines.
Parallel's surface is fixed rather than a marketplace: Search, Extract, Task, Responses, FindAll, Entity Search, and Monitor. You cannot deploy custom code to it, and there is no store.
## **Pricing: three meters against one**
Apify's model is a subscription that buys platform credit, which usage then draws down across three separate meters:
- - Compute units: 1 CU is 1 GB of memory for 1 hour, at roughly $0.20 per CU on the free plan and less on higher tiers
- - Proxy traffic: residential billed per gigabyte, datacenter per IP
- - Rented Actors: third-party Actors draw against the same credit, and some add per-result or per-event charges of their own
Plans run from Free (around $5 in monthly credit, 25 concurrent runs, no card) through Starter near $29 a month and Scale near $199, with Business above that. Published figures vary between Apify's own page and third-party trackers, so confirm before committing.
The practical consequence is that the plan price is a budget, not a bill. A per-search cost on Apify depends on how much memory the Actor was given, how long the page took to render, whether a retry fired, and whether the target needed residential proxies. That is unavoidable when you are renting compute rather than buying an answer, but it makes forecasting genuinely hard.
Parallel charges per request with one meter: Search at **$1 per 1,000** in Turbo and $5 per 1,000 in Basic and Advanced with 10 results and excerpts included; Extract at $1 per 1,000 URLs; the Task API at $5 to $2,400 per 1,000 runs; Responses at $10 to $250 per 1,000; Monitor at $3 per 1,000 executions; Entity Search at $5 per 1,000; FindAll at a fixed cost plus $0.03 to $1.00 per match. There is $5 in free credit every month, applied automatically.
_Note: For the latest pricing, always check official documentation._
## **Latency**
An Actor run is a container that boots, does work, and writes to a dataset. Even a fast one is seconds, and a cold start adds more. Apify's answer for latency-sensitive use is Standby mode, where the Actor stays warm as an HTTP server and handles requests in parallel; that helps considerably, and it is the mode to use if you put RAG Web Browser on a request path.
Parallel Turbo returns in about 200ms because it serves from an index rather than launching a browser. For an agent making several searches inside one user turn, that difference compounds.
## **Agent integration**
Apify has invested heavily here and it shows. The MCP server at mcp.apify.com handles OAuth, exposes Actor discovery and invocation as tools, and lets an agent find and run a scraper it has never seen before. Documentation ships as llms.txt and llms-full.txt. There are JavaScript and Python clients, a CLI, and framework integrations for CrewAI, LangChain, and LlamaIndex. Actor search and docs tools work without authentication at all.
Giving an agent the ability to discover and run any of tens of thousands of scrapers at runtime is a genuinely different capability from calling a fixed search endpoint, and for open-ended tasks it is a strong argument.
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}, )``` ## **Reliability and maintenance**
One thing to weigh with a marketplace: most Actors are maintained by third parties, so quality and upkeep vary. When a target site changes its markup, how fast the scraper gets fixed depends on whoever wrote it. Apify publishes success rates and user counts per Actor, which helps, but it is a different reliability model from a first-party API with an SLA.
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. Default rate limits are 600 requests per minute for Search, Extract, and Entity Search, 300 for Monitor, and 300 per hour for FindAll runs.
## **When to use each**
Choose Apify when the target is a specific site. Nothing else gives you tens of thousands of maintained scrapers for named platforms, the ability to write and deploy your own Actor when none exists, and an MCP server that lets an agent pick the right one at runtime. If your pipeline is "pull structured records out of these fifteen named sources," this is the platform, and the question of which search API to use barely arises.
Choose Parallel when the target is the open web and the consumer is a model. A 200ms search at $1 per 1,000 requests with excerpts included is a different shape of product from renting compute by the gigabyte-hour, and one flat meter is far easier to forecast than three. Task, Responses, FindAll, Entity Search, and Monitor cover research, list building, and change tracking with per-request pricing throughout.
These are complements more often than substitutes. A common shape is Apify for the named-source ingestion jobs that run on a schedule, and an LLM-native search API on the live agent path where latency and token cost decide the experience.
**Related reading: **Bright Data SERP API vs. Parallel[Bright Data SERP API vs. Parallel] · Firecrawl vs. Parallel[Firecrawl vs. Parallel] · ScrapingBee vs. Parallel[ScrapingBee vs. Parallel].
By Parallel
July 27, 2026