2025 — AI's New Demands on Proxy Stability

What Happened That Year

In 2025, the AI boom reshaped the proxy industry. AI companies needed massive web data for model training, demanding far higher stability than traditional scraping. This was the year many called the explosion of AI applications.

A typical large language model training run requires terabytes to tens of terabytes of web data -- collected, cleaned, and structured from the open internet. Proxy services, as a critical link in the data collection chain, directly determined the efficiency and cost of AI data acquisition.

We made several pivotal moves in 2025: extending proxy capabilities fully into the AI industry, building large-scale training data acquisition systems for AI enterprises, and establishing multi-language web collection, deduplication, denoising, and structured processing as an integrated data service. This was not simply adding a business line -- it was a reconstruction of the entire proxy service system, because AI stability requirements exceeded anything we had served before, across multiple dimensions.

Three New Challenges

AI data collection introduces three stability requirements:

1. Concurrency: AI training data collection scales to tens of thousands of concurrent connections, orders of magnitude larger than traditional scraping.
2. Success rate: Data quality directly affects model quality, requiring availability from 99% to 99.9%+.
3. Latency sensitivity: Data collection timeliness is stricter; latency variance directly impacts pipeline throughput.

These three requirements are interdependent. Maintaining high success rates under high concurrency, and controlling latency variance while maintaining high success rates -- this is an impossible-triangle-style challenge requiring deliberate architectural tradeoffs.

The Concurrency Challenge

Traditional data collection typically runs at hundreds to thousands of concurrent connections. In AI scenarios, this jumps to tens or even hundreds of thousands. An AI enterprise may run dozens of concurrent data collection tasks, each requiring hundreds to thousands of concurrent proxy connections. The peak concurrency the system must support can be 10-50 times that of traditional scenarios.

Scaling concurrency is not just about adding more nodes. When concurrency rises from 1,000 to 10,000, the bottlenecks shift from "do we have enough nodes" to "can the scheduling system keep up," "how fast can connections be established," and "how efficiently can state be synchronized." In 2025, we ran dedicated ten-thousand-level concurrency stress tests and found that beyond 5,000 concurrent connections, scheduling system CPU usage jumped from 20% to 85%, becoming the new bottleneck. We restructured the scheduling core from a single-threaded model to a multi-threaded event-driven model.

The Success Rate Challenge

AI training is far more sensitive to data quality than traditional scenarios. In a typical LLM training dataset, data quality directly determines model performance. If collected web data contains many failed or incomplete data points, that dirty data degrades the training outcome.

In traditional data collection, a 99% success rate is considered decent -- 1 failure in 100 requests is handled by a retry. In AI scenarios, 99% success means 1 failure per 100 requests, which at billions of daily requests translates to millions of failures per day. Each failure requires a retry, retries add latency, and latency reduces pipeline throughput.

More critically, AI data collection is often "full-coverage" -- it needs everything from a specific dataset. Any missing data point can leave the dataset incomplete. Success rate is not just a statistic; it directly relates to dataset completeness and quality.

The Latency Sensitivity Challenge

AI data collection latency sensitivity manifests at two levels.

First, task completion time constraints. AI model training follows strict schedules -- data collection must complete within a specified window or it disrupts the training pipeline. The proxy service must deliver predictable response times, not inconsistent performance.

Second, pipeline throughput requirements. AI data collection is typically pipelined: collect, clean, structure, ingest. Each stage's throughput must match. If the collection stage becomes a bottleneck due to proxy instability, the entire data pipeline suffers. In our 2025 operations, when proxy P95 response time exceeded 500ms, overall pipeline throughput dropped by approximately 20%.

High-Concurrency Architecture Design

To meet AI's ten-thousand-level concurrency demands, we made several key architectural upgrades in 2025:

Elastic connection pools. Traditional architectures maintain fixed-size connection pools per proxy node. At ten-thousand-level concurrency, fixed pools are either insufficient or wasteful. We introduced elastic connection pools -- pool size adjusts dynamically based on real-time traffic. When concurrent requests increase, the pool auto-expands; when they decrease, it auto-shrinks. Expansion happens in seconds, matching AI's bursty high-concurrency traffic patterns.

Full-chain asynchronous non-blocking I/O. In previous architectures, some stages still ran synchronously. At ten-thousand-level concurrency, synchronous blocking I/O causes massive thread/coroutine accumulation, leading to response time degradation. In 2025, we completed a full-chain asynchronous non-blocking I/O transformation -- from the API gateway to the scheduling system to proxy nodes to the data pipeline, all I/O operations are now asynchronous.

Distributed scheduling with shared state. When the system needs to support ten-thousand-level concurrency, single-point scheduling is no longer adequate. We introduced a distributed scheduling architecture -- multiple scheduling instances run simultaneously, coordinating through distributed locks and shared state. Each instance handles a subset of nodes and users, exchanging state through a shared Redis cluster. When one scheduling instance fails, its load is taken over by other instances.

Traffic control and priority queuing. When resources are limited, high-priority tasks need preferential treatment. We introduced task-priority-based traffic control -- AI training data collection tasks are classified as high priority and receive resource allocation priority. When system resources are tight, low-priority tasks are gracefully degraded (e.g., extended wait times) rather than directly rejected.

Multi-Language Web Collection

AI models require multi-language training data. In 2025, our proxy service expanded to cover 20+ languages.

Language coverage challenge. Websites in different languages are distributed across different regions with vastly different network infrastructure. We addressed this through node deployment and routing strategy: deploy localized nodes in each language's primary region (Japanese content via Japanese nodes, Arabic content via Middle Eastern nodes), and configure task-specific routing strategies to ensure optimal paths to target websites.

Character encoding and content parsing. Different languages use different character encodings (UTF-8, GBK, Shift-JIS, EUC-KR, etc.). The proxy system must correctly identify and handle these encodings. We added automatic character encoding detection and conversion in the data forwarding layer, ensuring content is correctly decoded regardless of its original encoding.

Anti-scraping strategy differences. Anti-scraping approaches vary by language. Western websites favor CDN-based protection (Cloudflare, Akamai). East Asian websites tend toward IP blacklists and frequency limiting. Some government websites use CAPTCHA or JavaScript challenges. The proxy system must employ different countermeasures for different language regions.

Data Deduplication, Denoising, and Structuring

AI training data quality directly affects model performance. In 2025, we integrated data quality processing into the proxy service -- no longer just providing a data channel, but performing quality processing within the data flow.

Deduplication strategy. Web data contains substantial duplicate content -- the same article republished across sites, similar product descriptions, news aggregator content identical to original sources. We introduced content fingerprinting in the data pipeline, computing a hash for each collected piece of content and retaining only one copy of identical or highly similar content. Deduplication operates at two levels: exact deduplication (identical content) and fuzzy deduplication (over 90% similarity), implemented using SimHash.

Denoising strategy. Web data contains substantial noise -- advertisements, navigation bars, footers, unrelated recommendations. If not cleaned, this noise contaminates training data. We added DOM-structure-based content extraction modules in the post-processing stage, automatically identifying and removing noisy elements to extract core content. For irregularly structured pages, we use ML models for content recognition, achieving over 85% accuracy.

Structuring. AI models need structured training data -- not just raw text but data with clear fields and labels. We provide data annotation and structuring services: automatically classifying collected content (news, reviews, product descriptions, blog posts), extracting key fields (title, author, publication date, body, tags), and outputting in user-defined schema formats. This dramatically reduces AI enterprises' data processing overhead.

AI vs. Traditional Scraping: Requirement Comparison

Our 2025 operations highlighted fundamental differences between AI enterprises and traditional scraping customers:

These differences mean AI-focused proxy services cannot simply scale up existing offerings -- they require specialized adaptation across architecture, data pipeline, and service processes. An typical AI customer evaluates proxies not just on raw performance but on "effective data acquisition cost" -- the cost per TB of usable training data after deduplication and denoising.

How to Judge Proxy Stability (Part 9): High Concurrency Performance

In 2025, evaluating proxy stability requires testing under high concurrency:

• Connectivity rate at 100, 1,000, 10,000 concurrent connections
• Whether response time significantly increases under load, and P95 values
• Whether connection limits exist and what happens beyond those limits

We recommend a graduated test approach: start at 100 concurrent connections, step up to 500, 1,000, 5,000, and 10,000, running at least 5 minutes at each level. Plot a curve of concurrency versus P50/P95 response time. In a well-architected system, response time should grow slowly and linearly. If there is an inflection point where response time spikes sharply, that indicates the system's design ceiling.

Also test recovery -- after a high-concurrency test ends, does the service return to normal immediately? A healthy service should. If there is hysteresis (response times do not recover promptly), there may be resource leaks or cache invalidation issues.

High-Concurrency Stress Test Data

In 2025, we ran dedicated high-concurrency stress tests on the proxy system. Here are the stability metrics at different concurrency levels:

As concurrency increased from 1,000 to 10,000, connectivity dropped approximately 0.7 percentage points and P95 response time rose approximately 300ms. This level of degradation is acceptable for most AI scenarios -- AI data collection tasks are typically not real-time, and a few hundred milliseconds of additional latency does not affect final data quality. The truly important metric is connectivity at high concurrency: as long as it stays above 98%, retry mechanisms can compensate for failed requests.

Beyond 10,000 concurrent connections, however, all metrics degraded significantly faster. This indicated that the system's architectural ceiling was around 10,000-15,000 concurrent connections. For AI clients consistently operating at 10,000+ concurrency, further architecture optimization and resource investment would be needed.

AI Enterprise Case Studies

In 2025 we established deep cooperation with several AI enterprises. Here are two representative cases:

Case 1: Large language model training data procurement. This enterprise needed to collect 50+ TB of multi-language web data for training their next-generation LLM. Data requirements covered 12 languages including Chinese, English, Japanese, French, German, and Arabic, with each language needing 3-5 TB of effective data. They integrated proxy service and data cleaning into a single procurement contract -- we provided not just proxy nodes but the full pipeline from collection through cleaning to structured output.

The technical challenge: peak concurrency exceeded 20,000, covering target websites in 12 languages simultaneously, with a 45-day delivery window. We adopted a partitioned collection strategy -- dividing the data by language into sub-tasks, each using a dedicated proxy node pool and cleaning pipeline. At peak, we deployed over 500 proxy nodes and 30 cleaning service instances. The project delivered in 42 days with a data effectiveness rate of 91% (91% of collected data passed quality gates as valid training data).

Case 2: High-frequency data updates for an AI application company. This enterprise needed daily updates of domain-specific web data for continuous model fine-tuning. Unlike traditional one-time full collection, this incremental update model required higher stability and predictability -- each day's collection task had to complete within a designated time window or it would disrupt the next model fine-tuning schedule.

We designed a scheduled task plus incremental collection scheme: tasks launched at a fixed time daily, prioritizing content updated since the last collection. Incremental collection volume was 5-10% of each full collection, significantly reducing collection and cleaning workload. We also established a dedicated node pool for this client, ensuring collection tasks were unaffected by other users' traffic fluctuations. After launch, daily task completion rate stayed above 99.5%, and proxy issues never once delayed a model fine-tuning schedule.

AI Data Collection Cost Analysis

The total cost of AI data collection includes more than just the proxy service fee. Understanding the full cost structure helps AI enterprises make better procurement decisions.

Proxy service fees are the most visible cost, charged by traffic or bandwidth. In AI scenarios, data volumes are huge, making this the primary expense. But comparing proxy unit prices alone is misleading -- subsequent data quality processing costs must also be considered.

Retry costs are the most commonly underestimated hidden cost. At 99% proxy success rate, each million requests generates 10,000 retries. Retries not only increase proxy fees but also extend collection time and increase system operational burden. Improving proxy success rate from 99% to 99.5% may slightly increase proxy cost, but cuts retries in half, significantly improving overall collection efficiency.

Data cleaning costs are unique to AI scenarios. Raw web data must go through deduplication, denoising, and structuring before becoming usable training data. The computational resources for this processing are substantial. Our integrated cleaning service packages this cost into the proxy service, reducing per-unit data cleaning cost through scale.

Time costs are especially critical in AI scenarios. Delays in the model training cycle can mean missing market windows. Cutting data collection and cleaning time by 50% translates to weeks of additional model iteration time for AI enterprises. This time value is the most easily overlooked but potentially most valuable component of proxy service pricing.

What We Built That Year

• Proxy capabilities extended to the AI industry for large-scale training data acquisition and cleaning
• Multi-language web collection, deduplication, denoising, and structured processing services
• Elastic connection pool and distributed scheduling architecture supporting ten-thousand-level concurrency
• Multi-language coverage expanded to 20+ languages
• Data cleaning pipeline: automated collection, deduplication, denoising, and structuring
• Quality gate mechanism ensuring controllable data quality
• Deep cooperation with multiple AI enterprises on customized data collection solutions
• Shift from proxy service provider to data service provider

AI Data Quality Assurance System

In 2025, we established an AI-oriented data quality assurance system ensuring deliverable data is verifiable and traceable.

Verifiability. Each data delivery includes a quality report covering: raw data volume, post-deduplication volume, post-denoising volume, effective data ratio, data freshness (gap between collection and delivery dates), and completeness (target website coverage). AI enterprises can verify data quality against this report.

Traceability. Every delivered data item carries provenance information: original URL, collection time, proxy node, and processing records during cleaning. When AI enterprises need to verify or supplement specific data, they can trace back to the source.

Continuous monitoring. We periodically compare delivered data against original source websites, checking for omissions or processing-induced data quality issues. When such issues are found, we trace the root cause (collection coverage or cleaning stage issue) and correct it in subsequent services.

Looking Ahead to 2026

2025 represented a comprehensive upgrade of proxy capabilities for the AI era. In 2026, as the AI industry matures further, enterprise demands would shift from technical capability to service assurance -- SLA systems, enterprise partnership models, and industry solutions would become the new focus. The competition in proxy services was evolving from "who has better technology" to "who provides more reliable service."

One Piece of Advice

When selecting proxies for AI data collection, do not test only under low concurrency. A service that remains stable under high concurrency is the one that truly works. Also, when evaluating AI data collection proxies, do not just look at the proxy's raw metrics -- look at the effective data acquisition cost: the total cost per TB of usable data after removing duplicates and noise. That perspective better reflects the proxy service's true value in AI scenarios.