Choosing Proxies for AI Training: What Most Teams Get Wrong

It’s 2026, and you’d think the fundamental plumbing of AI development would be a solved issue. Yet, in calls with teams from seed-stage startups to established enterprises, one question surfaces with stubborn regularity: how do we actually choose and manage proxies for data collection? The conversation rarely starts there, of course. It begins with a model that’s underperforming on specific geographies, or a scraping pipeline that suddenly, mysteriously, starts returning more CAPTCHAs than data. The proxy question is the back-end headache that eventually forces its way to the front.

The instinct, especially under time pressure, is to treat it as a simple procurement problem. Find a provider, buy a package, plug in the endpoints, and move on. This is where the first, and most common, divergence between expectation and reality occurs.

The Quick Fix That Never Sticks

The most tempting path is to optimize for a single, easily measurable variable: cost. The logic seems sound—data collection is a volume game, and proxies are a recurring expense. Why pay more? Teams will often run a small-scale test with a handful of “cheap and reliable” IPs, see a 95% success rate, and sign up. The problems emerge at scale and over time.

What that initial test doesn’t capture is the behavior of the IP pool. A cheap residential proxy network might pull from devices with unpredictable uptime. An IP that works flawlessly at 2 PM local time might be offline at 2 AM. Your pipeline doesn’t fail gracefully; it times out, retries, and creates bottlenecks. Suddenly, your engineering time, which is far more expensive than any proxy subscription, is consumed by debugging connection issues and writing complex retry logic.

Another common trap is over-indexing on “high anonymity” as a binary feature. The assumption is that if a proxy is “elite” or “high-anonymity,” it’s sufficient. But anonymity isn’t the only fingerprint. Consistency matters. If your training data requires sequential interactions from the same virtual location—simulating a user session over minutes or hours—you need sticky sessions or consistent IPs from the same city or ISP. Rotating through a global pool of high-anonymity IPs can itself be a detection trigger, as it presents the statistical impossibility of a user teleporting across continents between requests.

When Scaling Up Makes Everything More Fragile

Practices that work for a proof-of-concept become liabilities when you operationalize. Manually managing a list of a few hundred proxy IPs in a spreadsheet is tedious but possible. Managing tens of thousands, with their associated success rates, geographic locations, and ASN data, is a full-time job. Teams often don’t realize they’ve built a hidden, manual infrastructure layer until it collapses.

Similarly, relying on a single proxy provider for all use cases is a scaling risk. A provider excellent for generic US web scraping might have poor coverage in Southeast Asia or might be universally blocked by a particular social media platform you suddenly need to access. Your entire data collection strategy is then held hostage by one vendor’s network limitations. Diversification isn’t just a financial concept; it’s a core reliability tactic for data pipelines.

The most dangerous assumption of all is that proxy choice is a one-time decision. The internet is an adversarial environment. Websites update their defense mechanisms. Proxy networks get detected and blacklisted. The legal landscape for data collection shifts. The proxy solution that worked perfectly in Q1 2026 might be wholly inadequate by Q3. Yet, most teams lack a process for continuous, automated evaluation of their proxy health, treating it as set-and-forget infrastructure like a server.

Shifting from Tools to Systems

The turning point for many teams comes when they stop asking “which proxy service should we buy?” and start asking “what does our data collection system need to be reliable and representative?”

This shifts the focus to criteria that matter in production:

• Success Rate Over Time, Not a Point in Time: It’s not about a 5-minute test. It’s about measuring success rates, latency, and timeout percentages over weeks, across different target sites and at different times of day. This data should feed back into automatically deprioritizing underperforming IP subnets.
• Geographic & Contextual Precision: Do you need an IP from “the UK,” or more specifically from London on a Virgin Media ISP? The specificity of your training data requirements should dictate the granularity of your proxy selection. A model training on local retail trends needs finer location data than one analyzing global news sentiment.
• Integration Overhead: How much engineering effort is required to integrate, rotate, and manage the proxies? A service with a simple API that handles automatic rotation and provides detailed request logs saves weeks of developer time compared to a bare list of IP:port combos.
• Ethical and Legal Sourcing: This has moved from a niche concern to a mainstream requirement. The provenance of proxy IPs matters. Networks that are transparent about consent and don’t rely on exploitative SDKs buried in free mobile apps mitigate long-term reputational and legal risk.

This is where a systematic approach replaces a tactical one. For example, some teams now maintain a small internal dashboard that tracks key metrics per proxy source and per target domain. They might use a primary provider like Bright Data for its reliability and granular geographic control in core markets, while supplementing with a specialist provider for a particularly difficult region or domain. The system is designed to fail over, to compare, and to provide data for the next procurement decision.

The Role of Managed Infrastructure

In this context, tools like Bright Data aren’t just a proxy vendor; they function as a managed infrastructure layer that abstracts away a set of nasty problems. When you need a specific city-ISP combination for a week-long data collection job, you can programmatically request it without having to build a relationship with a local telecom. Their networks are built for the scale and pattern of machine, not human, access, which changes the reliability profile significantly.

The value isn’t in the list of features, but in the reduction of cognitive load and operational toil. It allows the team to focus on what data to collect and how to train the model, rather than on why the data stream dried up overnight because an entire subnet was blacklisted.

The Uncertainties That Remain

Even with a systematic approach, uncertainties persist. The arms race between data collectors and website defenders guarantees that no solution is permanent. Regulations like the GDPR and evolving case law around terms-of-service violations and computer fraud create a shifting legal fog. The most honest advice is to build for adaptability. Your proxy management layer should be as swappable and modular as possible.

Furthermore, the line between “public” data for model training and private or copyrighted material is being redrawn in courts and legislatures globally. A reliable proxy gets you the data; it doesn’t tell you if you should be collecting it. That’s a separate, and increasingly critical, judgment call.

FAQ (Questions We’ve Actually Been Asked)

Q: Should we just use datacenter proxies? They’re fast and cheap. A: For large-scale, generic HTML collection from sites with minimal anti-bot measures, they can work. But for anything mimicking human interaction—especially on platforms like social media, travel aggregators, or e-commerce—their collective IP ranges are often the first to be blocked. They are a tool for a specific, limited job.

Q: Is rotating proxies after every request always the best strategy? A: No, it’s often the opposite. It creates an easily detectable pattern. For many tasks, maintaining a session from a single IP for a logical sequence of actions (search, click, view) is more “human” and less likely to trigger alarms. Match the pattern to the real-user behavior you’re simulating.

Q: How do we even start evaluating providers? A: Don’t start with their sales page. Define 2-3 of your most critical, representative data collection tasks. Get trials from a few providers. Run these same tasks concurrently over 48-72 hours. Measure not just success rate, but also consistency of response times, completeness of data returned, and the clarity of the logs when something fails. Let your specific use case be the judge.

Q: We have a small budget. Is this even a solvable problem for us? A: It is, but it requires more creativity. You might focus your spend on a small number of high-quality, reliable residential or mobile IPs for your most critical targets, and use open-source, self-hosted rotating proxy solutions (with extreme caution and ethical consideration) for less critical, bulk collection. The key is to be intentional—don’t let budget constraints push you into the most chaotic and unmanageable part of the market.

The core lesson, repeated across teams, is this: proxies are not a commodity. They are a dynamic, critical component of your data pipeline’s health. Choosing them is less about finding a single right answer and more about building a system that can ask, and answer, the right questions over time.