Prompt

Apify pricing for large-scale crawling

Technology / Cloud Infrastructure1 observationLast seen Oct 4, 2026

Latest observation

Oct 4, 2026OpenAI APIWeb search: off

Apify pricing for large-scale crawling usually comes down to two parts:

  1. Platform usage — compute, storage, proxy traffic, and actor runs
  2. Proxy traffic — especially important for high-volume crawling

How Apify typically charges

Apify’s pricing is generally based on:

  • Compute units / actor execution time
  • Proxy bandwidth/traffic
  • Storage/database usage
  • Plan limits or included credits depending on subscription tier

For large-scale crawling, the biggest cost drivers are usually:

  • Running crawlers longer
  • Using residential or datacenter proxies
  • Handling anti-bot protections
  • High request volume and retries

What matters most for large-scale use

If you’re crawling thousands to millions of pages, watch these closely:

1) Proxy cost

  • Datacenter proxies are cheaper, but more likely to be blocked on protected sites.
  • Residential proxies are more expensive, but better for hard targets and geo-specific crawling.

2) Compute cost

  • Efficient actors, better concurrency control, and minimizing retries can reduce cost significantly.
  • Poorly tuned crawlers can burn through resources fast.

3) Data storage

  • Large result sets can add cost if you store lots of records in datasets/key-value stores.
  • Exporting and cleaning data early helps.

Practical budgeting advice

For large-scale crawling, estimate cost using:

  • Pages to crawl
  • Average requests per page
  • Retry rate
  • Proxy type
  • Average runtime per page

A rough formula:

Total cost ≈ compute + proxy traffic + storage

How to estimate for your use case

If you tell me:

  • target number of pages
  • target websites
  • whether proxies are needed
  • expected crawl frequency
  • whether sites are easy or bot-protected

…I can help you build a rough Apify cost estimate.

Best next step

Check Apify’s current pricing page for exact rates, since they can change:

If you want, I can also help you compare:

  • Apify vs Bright Data vs Zyte
  • or calculate a monthly cost estimate for your crawler workload.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as Circuit. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

Want this interpreted for your brand?

Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.