Why are datacenter proxies a good fit for AI training?

Datacenter proxies are fast and cost-effective, which matters when you are collecting millions of public pages on a budget. Their low latency and high throughput suit speed-sensitive crawling on sources with lighter defences, making them ideal for bulk dataset assembly.

Can I collect region-specific data for my models?

Yes. By routing through IPs in different locations, you can access publicly available, region-specific content and bypass geo-restrictions on public pages. This helps you capture local language and market variation that improves how well your model generalises.

How do proxies help with large-scale crawling?

Spreading requests across a wide IP pool keeps your crawl rate steady and avoids overloading any single address. Combined with Evomi's fast response times and reliable uptime, this lets long-running ingestion jobs complete without constant manual intervention.

Use Cases

AI Training Proxies

Datacenter Proxies for AI Training Data Collection

Gather large, varied datasets from publicly available web sources to train and fine-tune your models, with high-throughput proxies built for the volume that machine learning pipelines demand.

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Diverse Data

Source varied public datasets from across regions

Model quality depends on the breadth of what it learns from. With proxies spanning many countries, you can collect publicly available text, images, and structured data from regional sources that would otherwise be hard to reach. Pulling examples from different markets and languages helps reduce sampling bias and gives your training set the variety that generalisation needs, all in line with each source's terms of service.

High Throughput

Keep crawlers moving during multi-million page jobs

Training pipelines rarely deal in small samples. When you are crawling millions of public pages, distributing requests across a large pool of rotating IPs keeps your collection steady and prevents a single address from carrying the full load. That means your data ingestion stays consistent overnight and across long-running jobs, so your team spends time on model work rather than babysitting stalled scrapers.

Sub-second speeds

Evomi's datacenter proxies respond in milliseconds, so data ingestion keeps pace with your training schedule.

Reliable connections

Stable, high-uptime routes keep long crawling jobs running without dropped sessions interrupting your dataset builds.

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