Limited Training Data Sources
Without samples from diverse countries and network environments, models learn from a narrow distribution, leading to poor generalization.
United States
5,996,626 IPs
Should AI projects use dynamic residential or static proxies?
For large-scale data collection, search monitoring, and content scraping, dynamic residential proxies are usually the better fit. For fixed exits, long sessions, and stable login environments, static proxies are more suitable.
Can you support high-concurrency model data scraping?
Yes. You can combine different proxy types based on task scale and use scheduling strategies to control region, frequency, and concurrency for pre-training collection or real-time data processing.
Are proxies suitable for AI search and Agent scenarios?
Yes. Whether simulating search results from different regions or letting Agents execute web tasks across multiple environments, proxies provide access that closely mirrors real user conditions.
How do I start integrating proxies into my AI workflow?
Start with a free trial to verify task success rates and exit quality, then choose a plan based on your data volume, request frequency, and stability requirements.
