Prompt
What should I use: Bright Data or Zyte for website crawling?
Latest observation
If your main goal is website crawling at scale, both Bright Data and Zyte are strong options, but they fit slightly different needs.
Quick recommendation
-
Use Bright Data if you want:
- very broad proxy/network coverage
- strong control over IP rotation, geotargeting, and scraping infrastructure
- more of a platform for building your own crawling stack
-
Use Zyte if you want:
- a more turnkey scraping/crawling solution
- easier integration for data extraction workflows
- managed scraping tools and services with less infrastructure work
How they differ
Bright Data
Best when you need:
- large proxy pools
- residential, mobile, datacenter proxies
- advanced anti-bot bypass infrastructure
- flexible crawling at high volume
- more DIY control
Tradeoff:
- can be more complex to set up and manage
- you may need to build more of the crawler/extraction logic yourself
Zyte
Best when you need:
- managed scraping APIs and tools
- less operational overhead
- easier structured extraction from websites
- a service oriented more toward data delivery than just proxy access
Tradeoff:
- less flexible than building everything yourself
- may be better suited for teams that want speed and simplicity over deep infrastructure control
Simple rule of thumb
- Choose Bright Data if you have a technical team and want maximum flexibility.
- Choose Zyte if you want a more guided, managed crawling experience.
Consider these factors
Before deciding, ask:
- Do you need just access, or also extraction?
- Do you want to manage crawling logic yourself?
- How important is anti-bot success rate?
- Do you need compliance, logging, and enterprise support?
- What’s your budget and expected crawl volume?
My practical take
- For raw crawling infrastructure: Bright Data
- For simpler scraping automation: Zyte
If you want, I can also compare them on:
- pricing
- ease of use
- success rate / anti-bot performance
- best choice for your specific website or use case
Brands
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 dotdotPR. 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.
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