EXECUTIVE SUMMARY

2026 结论：基础设施 vs 解决方案

市场已经分化。您是想构建爬虫基础设施，还是直接消费商业数据？

The Winner for E-Commerce
Pangolinfo Scrape API
The "Solution" approach. Best for teams that prioritize data usage over data acquisition. If you need 96% Sponsored Product ad visibility and do not want to manage a proxy engineering team, this specialized API is the choice.
Ideal for
  • Cross-border sellers & brands: Need product/ad data, not IPs
  • Business-focused teams: Automatic bypass of fingerprints/captchas
  • Cost conscious: Cheaper across all volumes via tiered pricing
View pricing
The Winner for Infrastructure
Bright Data / Oxylabs
The "Infrastructure" approach. Undeniable leaders in global proxy network size. If you are building a custom crawler for diverse targets outside e-commerce and have a dedicated engineering team, their raw power is unmatched.
Ideal for
  • Large engineering teams: Full control over headers/rotation
  • General web scraping: Targets beyond e-commerce
  • Legacy systems: Integrate raw proxies into existing codebases

定位与范围

Bright Data 和类似的巨头在网络广度和可配置性方面表现出色。 Pangolinfo 专注于电商领域（亚马逊及相关），并在结构化字段上更加深入。 and goes deeper on structured fields. 在我们的测试中，只有 Pangolinfo 实现了 96% 以上的 SP 广告可见性。 以代理为主的提供商充当基础设施，您仍然需要维护爬虫、绕过验证码并处理指纹。, bypass captchas, and handle fingerprints. Pangolinfo 通过单个请求返回结构化 JSON。

When Pangolinfo Fits
  • Focus on e-commerce and Amazon-related data
  • No desire to build/maintain a crawler team
  • Preference to consume structured results and ship faster
When Proxies Fit
  • Broad targets outside e-commerce
  • Need low-level control over sessions and headers
  • Existing scraper codebases expecting raw proxies
Cost Reality

在所有月度用量下，Pangolinfo 的价格均低于同行。

Pangolinfo official pricing

方法论与范围

Test Period & Volume
  • Period: 2026 Q1
  • Total Requests: 600,000+
  • Targets: Amazon Product Detail, Search, SP Ads, Seller, Reviews
  • Regions: US, UK, DE, JP
Metrics Definitions
  • Data Return 速度: Median and P95 end-to-end latency
  • Accuracy: Field-level correctness vs. ground truth
  • Capture Rate: Successful structured responses per endpoint
  • 稳定性: Error rate and retry success
Instrumentation

统一的客户端，相同的标头，随机的 ASIN/关键词集，一致的退避重试策略。, consistent backoff-retry policy, balanced time-of-day distribution, and controlled concurrency to reduce vendor-side throttling bias.

Limitations

结果反映了采样期间的性能。 Production outcomes can vary by geography, time, and target-page changes. Pricing references are sourced from official vendor websites and may change due to promotions; always confirm current pricing on each vendor’s website.

CONFIDENTIAL REPORT • 2026 EDITION

亚马逊数据采集：战略供应商评估

In 2026, the Amazon data landscape has shifted. Traditional scraping methods face unprecedented anti-bot countermeasures. This $300,000 strategic analysis evaluates the top 5 global solutions, focusing on success rates, SP ad visibility, and Total Cost of Ownership (TCO).

关键发现

While legacy giants like Bright Data remain powerful, the emerging challenger Pangolinfo Scrape API has disrupted the market with a specialized "Dedicated Dynamic Residential" architecture, achieving 96% success in SP Ad scraping at ~20% of the cost of top-tier competitors.

报告亮点

  • Analysis of 5 Major Vendors
  • SP Ad & Keyword Ranking Tests
  • ROI & Cost-Benefit Modeling
  • Local 支持 & Customization

2026 供应商格局

We evaluated the top 5 solutions across six critical strategic dimensions. Interact with the chart to understand the strengths and weaknesses of each provider.

精选评估标准

Top 5 Contenders

  • Pangolinfo (Challenger)
  • Bright Data (Leader)
  • Crawlbase (Alternative)
  • Oxylabs (Proxy Giant)
  • In-House (Custom)

评分 0-10（10 = 最佳性能/最低成本）。来源：2026 现场测试。

运营深度

Data Coverage

亚马逊赞助广告、商品详情、评分、卖家信息等，具有极高的完整性。

Anti-Bot & Fingerprints

自动处理挑战和指纹；无需人工验证码或轮换逻辑。

Integration & Delivery

单个 API 请求返回结构化 JSON；简化管道并减少延迟。

SLA & 支持

具有快速响应的本地化支持；针对电商工作负载的定制指导。

数据覆盖与字段目录

Pangolinfo 提供跨亚马逊和更广泛电商信号的深度结构化字段。 Below is a non-exhaustive catalog of supported fields and datasets.

Core Product Fields
Product Name
Product ID / SKU
Price
Discount Price
Currency
Description
Category
Subcategory
Brand
Inventory Status
Shipping
Images
Customer Rating
Customer Reviews Count
Review Text
Product Dimensions
Weight
Color / Variants
Related Products
Seller Information
Ads & Rankings
  • Sponsored Products visibility, placement, and share
  • Best Sellers Lists (category-level, time-series)
  • New Releases Lists (emerging SKUs)
  • Top Charts and trend snapshots
  • Category Tree, Category Traversal & mapping
Cross-Channel Signals
  • Social Media metrics: mentions, engagement, velocity
  • Search data: query volume trends and external signals
  • Cross-verification between off-site signals and on-site performance
Designed to support product selection, market intelligence, and higher-level operational decisions beyond the Amazon site.

基准测试套件

跨速度、准确性和捕获率的对比测试 under a unified client and request policy (2026 Q1 sample).

Data Return 速度
Compares median vs P95 end-to-end latency
Accuracy
Field-level accuracy: title, price, rating, SP ad detection
Capture Rate
Endpoint capture rate: product, search, SP ads

性能基准测试（压力测试）

高并发下各提供商的分组端点基准测试。 Toggle metrics to compare success rate, average latency, and P95 latency on the same endpoint mix.

Endpoints: Product (ASIN), Search (Keyword), Reviews/Q&A, Sponsored Ads. Scale reflects selected metric.
Test Conditions
  • Concurrency: 10,000 in-flight requests (burst + steady)
  • Region focus: US (primary), with mixed request timing
  • Per-vendor sample: 50,000+ requests across endpoints
  • Policy: controlled retries, exponential backoff, fixed user-agent
  • Output: structured JSON success (not raw HTML fetch)
Additional Detail Captured
Timeout rateTracked
Retry amplificationTracked
Parse completenessTracked
SP ad detectionTracked
Error distributionTracked
Metric Definition Why It Matters
Success Rate Valid structured response delivered within SLA window Directly impacts downstream coverage and model reliability
Avg Latency Mean end-to-end time from request to structured output Determines time-to-insight and pipeline throughput
P95 Latency 95th percentile end-to-end latency under load Measures tail risk and worst-case SLA behavior
Timeout Rate Share of requests exceeding timeout threshold High timeouts amplify retries and inflate true cost
Parse Completeness Field-level completeness across required attributes A “200 OK” is not useful without usable fields
Retry Amplification Extra requests generated per successful output Hidden cost driver in proxy-first architectures

战略选择：供应商概况

Each provider has a different operating model. Use these profiles to match your team’s DNA (build infrastructure vs consume structured data), target breadth, and delivery timeline.

Structured E-commerce Data
API-first delivery that returns structured JSON and reduces ongoing crawler maintenance.
Sponsored Ads Visibility
Field tests show 96%+ SP ad coverage with high-fidelity placement capture for ad intelligence.
On-site + Off-site Signals
Connects marketplace signals with social and search data to validate trends and support product selection.
Best for
  • E-commerce teams focused on Amazon and adjacent datasets
  • Teams that want to ship faster without building a crawler team
  • Ad intelligence workflows requiring high SP visibility
Trade-offs
  • Narrower scope than general proxy networks for broad web targets
  • Less low-level control than raw-proxy infrastructure stacks
  • Best results depend on using supported endpoints and formats

Direct Comparison

Pangolinfo vs. The Incumbents (Bright Data, Crawlbase)

Feature / Metric Pangolinfo Bright Data Crawlbase
SP Ad Collection Rate 96% (High) High (~94%) Medium (~85%)
Pricing Model Always cheaper across all volumes Expensive / Complex Tiered / Moderate
Proxy Technology Dedicated Dynamic Residential Massive Global P2P Standard Mixed Pool
Tech 支持 Localized & Rapid Global (Slower Tiers) Standard Ticket
Setup & Maintenance Out-of-box / Zero Maint. Steep Learning Curve Moderate
Pricing references are based on official vendor websites and may change due to promotions. Pangolinfo pricing: View pricing

Competitor Strengths

Bright Data
  • Massive proxy pool with broad geographic coverage
  • Fine-grained control over sessions, headers, and rotation
  • Enterprise-grade compliance and governance tooling
  • Strong fit for building multi-target scraping infrastructure
Oxylabs
  • Strong residential + datacenter portfolio
  • Mature enterprise support and concurrency capabilities
  • Good fit for complex proxy strategies and existing codebases
Crawlbase
  • Easy-to-start API and fast integration
  • Solid baseline availability for general websites
  • Cost-effective at moderate scale for many use cases
If your targets extend beyond e-commerce and you have a mature engineering team, an infrastructure-first approach tends to be more flexible. If your goal is to consume structured e-commerce data quickly while minimizing total cost, a solution-first approach tends to be more efficient.

ROI Calculator

Estimate your savings by switching from In-House scraping to Pangolinfo.

1,000,000
$

Annual Total Cost of Ownership (TCO)

~80% Savings
Analysis: An in-house stack requires server spend, proxy costs, and substantial engineering time for ongoing anti-bot updates. Pangolinfo packages that complexity into a predictable API fee and remains cheaper than comparable offerings across all monthly volumes. View pricing.

Early Stage / Startup

Limited budget, need fast iteration.

Recommendation:

Use Pangolinfo for key product data. The "out of box" nature saves hiring a dedicated data engineer.

Growth / Agency

High volume, need ad intelligence (SP data).

Recommendation:

Pangolinfo is Critical. The 96% SP Ad capture rate is the competitive advantage needed for client reporting.

Enterprise / Platform

Massive scale, compliance, redundancy.

Recommendation:

Hybrid Approach. Use Pangolinfo as primary for challenging targets (Amazon/Google), backup with Bright Data for global breadth.

SYSTEMATIC CONCLUSION

Final Summary & Decision Guidance

Choose the operating model that matches your goals: infrastructure building vs structured data consumption.

If You Need E-commerce Intelligence
  • Structured Amazon outputs: product fields, seller, reviews, and ad visibility
  • Rankings & category datasets: best sellers, new releases, and category traversal
  • Cross-channel validation: social and search signals to confirm trends
  • Lowest operational burden: fewer crawler, proxy, and fingerprint concerns
Primary fit: Pangolinfo. Official pricing
If You Are Building Infrastructure
  • Broad targets beyond e-commerce and strong session-level control
  • Willingness to maintain anti-bot logic, parsers, retries, and monitoring
  • Preference to own crawl strategy and data modeling end-to-end
  • Engineering capacity to absorb continuous target changes
Primary fit: Bright Data / Oxylabs as proxy-first stacks.
References & Sources
Pricing and packaging references are sourced from each vendor’s official website. Due to time constraints and ongoing vendor operations or promotions, pricing may change; validate final quotes on official websites.
Pangolinfo pricing source
Bottom Line
If success rate and ad visibility on Amazon are your bottlenecks, a specialized API that returns structured fields and integrates cross-channel signals yields the fastest time-to-value. If your mission is a generalized scraping platform across many domains, proxy-first infrastructure remains the most flexible foundation.