September 18, 2025
AI Data Labeling in 2025: How Proxies Improve Data Collection and Model Training
Behind every high-performing AI model is a massive amount of labeled data. From image recognition to sentiment analysis, AI data labeling transforms raw content into structured datasets that machine learning systems can learn from.
Most conversations about labeling focus on annotation accuracy or tool selection. But before any label is added, there is a harder challenge: collecting the right data at scale. Much of this data is restricted by geography, blocked by anti-bot systems, or hidden behind CAPTCHAs.
This is where residential proxies and mobile proxies come in. By routing traffic through real user devices, they allow AI teams to gather global, unbiased datasets safely and efficiently.
Most conversations about labeling focus on annotation accuracy or tool selection. But before any label is added, there is a harder challenge: collecting the right data at scale. Much of this data is restricted by geography, blocked by anti-bot systems, or hidden behind CAPTCHAs.
This is where residential proxies and mobile proxies come in. By routing traffic through real user devices, they allow AI teams to gather global, unbiased datasets safely and efficiently.
What Is AI Data Labeling?
AI labeling (or data annotation) is the process of attaching metadata to raw content so algorithms can recognize and categorize it.
Common annotation tasks include:
Common annotation tasks include:
- Image classification: labeling photos of cats, cars, or medical scans
- Object detection: bounding boxes for vehicles, people, or products
- Sentiment analysis: tagging tweets as positive, negative, or neutral
- Named Entity Recognition (NER): identifying names, dates, and organizations in text
- Speech labeling: tagging audio files for training voice assistants
Why Proxies Are Essential for AI Data Collection
Training data pipelines depend on constant scraping and content aggregation. Without proxies, teams face:
- Geo-restrictions blocking access to regional datasets
- IP bans and CAPTCHAs slowing crawlers and annotation workflows
- Biased datasets when access is limited to a few regions
- Security exposure when scrapers reveal their true infrastructure
- Masking scraper IPs to avoid bans
- Rotating IPs to mimic natural human traffic
- Unlocking geo-specific data sources worldwide
- Preserving anonymity and infrastructure security
How Proxies Improve AI Labeling Pipelines
Proxies make every stage of data preparation smoother:
Data Collection at Scale
Data Collection at Scale
- Rotate IPs to bypass bans and collect from diverse regions.
- Use residential proxies in multiple countries to capture cultural nuance and multilingual data.
- Mask origin infrastructure to prevent attribution and reduce security risks.
- Keep persistent sessions active for sites that require logins or multi-step navigation.
- Build more representative datasets by accessing sources across different continents.
Best Proxy Types for AI Data Labeling
Why Ping Network Is the Best Option
Traditional providers lease small IP pools from ISPs or datacenters. Ping Network is different. It is a decentralized bandwidth layer powered by real devices worldwide.
Key benefits for AI teams:
Key benefits for AI teams:
- Residential and mobile IPs from 150+ countries
- Automatic IP rotation and sticky session control
- API-first design, compatible with Python, Puppeteer, Playwright, and Selenium
- Low latency and high throughput, ideal for continuous scraping
- Decentralized supply model, ensuring authentic and sustainable IP diversity
Best Practices for AI Labeling with Proxies
- Rotate IPs frequently to prevent detection
- Respect robots.txt and scrape responsibly
- Use sticky sessions for workflows that require login persistence
- Distribute traffic across regions to reduce dataset bias
- Monitor block rates and adjust proxy settings dynamically
FAQ: Proxies for AI Labeling
Q: Why do AI labeling teams need proxies?
A: Without proxies, crawlers face bans, rate limits, and geo-blocks. Proxies enable global, safe data access.
Q: Which proxies work best for data labeling?
A: Residential proxies are best for protected and geo-locked data. Datacenter proxies are good for open sources. Mobile proxies are critical for mobile-only datasets.
Q: Are proxies legal for AI data collection?
A: Yes, when used responsibly and in compliance with data privacy laws like GDPR and CCPA.
Q: How do proxies reduce bias in datasets?
A: By unlocking sources from multiple regions, proxies make datasets more diverse and representative.
Q: Can Ping Network scale for enterprise-level annotation?
A: Yes. Its decentralized design supports massive data pipelines with authentic, trusted IPs.
Final Thoughts
AI labeling is only as good as the data behind it. Proxies unlock that data by bypassing restrictions, preventing bans, and enabling global, unbiased collection.
For teams building large language models, computer vision systems, or domain-specific AI, proxies are no longer optional—they are the backbone of modern data labeling pipelines.
👉 Book a Call
👉 Read the Docs
For teams building large language models, computer vision systems, or domain-specific AI, proxies are no longer optional—they are the backbone of modern data labeling pipelines.
👉 Book a Call
👉 Read the Docs