AI DATA SCRAPING INFRASTRUCTURE

Infrastructure forproduction-scale AI datasets

Move public video, image, code, text, and document data into your collection pipeline with unmetered traffic, 10Gbps+ per-project capacity, and target-specific proxy pools.

Assess high-bandwidth capacity View architecture

Unmetered project traffic10Gbps+ per-project capacityTarget-specific proxy poolsHTTP(S) and SOCKS access

AI dataset pipelinecapacity ready

AI Data Scraping Infrastructure123Proxy data network

Video and audioMULTIMODAL

Image datasetsIMAGE

Public codeCODE

Text and documentsCORPUS

Public sourcesSOURCE

123ProxyPROXY

CollectorsWORKERS

PROJECT CAPACITY10Gbps+

BILLINGUnmetered

POOLCustom

10Gbps+Per-project capacity

UnmeteredPredictable transfer cost

Custom poolsTarget-specific routing

24/7Enterprise support

AI Data Scraping Infrastructure

Bottlenecks Workloads Architecture Proxy strategy Validation Developers FAQ

01 / Workload

Start with data shape,then plan network capacity

Object size, request count, location requirements, and retry behavior determine the right proxy and bandwidth model.

Video and multimodal

Sustained transfer for large media objects.

Image datasets

High concurrency and object-level recovery.

Public code

Repositories, archives, and historical objects.

Text and documents

Web pages, PDFs, and public documents.

02 / Scenarios

Choose by workload,not by proxy label

Each solution page explains architecture and validation while routing capacity decisions to high-bandwidth proxy IP.

Video and multimodal

Video, audio, captions, and public metadata.

Open video solution

Large-scale images

Concurrent image collection and recovery.

Open image solution

Public code

Repositories, archives, and historical objects.

Open code solution

Web text and documents

Pages, PDFs, forums, and public documents.

Open text solution

YouTube Data API

Submit public video metadata, caption, and comment jobs through an API.

Open API product

03 / Architecture

Scale by useful data throughput

Measure successfully stored data, retries, completeness, and completion time rather than request count alone.

Define the dataset

Document public sources, object types, and expected scale.

Build a representative sample

Measure response sizes, success, and retry behavior.

Plan pools and workers

Allocate proxy capacity by target and collector.

Scale by output

Track useful bytes written to customer storage.

Data scraping architecture123Proxy network

Public dataSOURCE

Proxy networkROUTE

CollectorsWORKERS

StorageOUTPUT

Capacity10Gbps+

Cost modelUnmetered

BoundaryProxy infrastructure

04 / Proxy strategy

Use high-bandwidth proxy IP as the primary transfer layer

Large AI datasets are constrained first by aggregate throughput and transfer cost. Residential proxies are added only when a workload needs explicit geographic identity.

High-bandwidth proxy IP

Unmetered project traffic10Gbps+ per-project capacityTarget-specific proxy poolsHTTP(S) and SOCKS access

View high-bandwidth proxy IP Contact sales

Primary productHigh-bandwidth proxy IPUnmetered project capacity

Capacity10Gbps+Validated with representative targets

AccessHTTP(S) / SOCKSWorks with existing collectors

05 / Validation

Validate with reproducible production metrics

Capacity tests should explain cost, completion time, and dataset quality.

Useful throughput: Successfully stored bytes per unit time
Task success: Completed objects relative to the manifest
Retry amplification: Extra requests caused by failures
Completeness: Files, fields, checksums, and relationships
Completion window: Time required to finish the planned batch
Proxy efficiency: Useful output relative to network and worker cost

Validate all capacity assumptions with representative public targets, real response sizes, retry behavior, and the required completion window.

06 / Developer access

Keep data logic in your collection system

Existing collectors continue to use standard proxy protocols. 123Proxy supplies routing and capacity; customers own parsing, storage, and business rules.

Standard proxy configurationExplicit timeout and retry policyMeasure useful data throughput

Python / streaming downloadIntegration example

import requests

proxy = "http://user:pass@proxy.123proxy.cn:9000"
with requests.get(
    "https://target.example/public-object",
    proxies={"http": proxy, "https": proxy},
    timeout=60,
    stream=True,
) as response:
    response.raise_for_status()

07 / FAQ

Questions to answer before production

Validate workload boundaries, useful throughput, retry behavior, and dataset completeness with representative public targets.

Is this a proxy product or a managed dataset service?

These pages describe proxy infrastructure for AI data workloads. Managed collection or dataset delivery requires a separately scoped enterprise project.

Why is high-bandwidth proxy the primary recommendation?

Large media, images, archives, and documents are constrained first by aggregate throughput and transfer cost.

Does 10Gbps+ apply to one request?

No. It is aggregate capacity for one project across targets, connections, and workers.

When are residential proxies needed?

Add them when a public workload requires explicit country or regional identity.

Can you deliver training datasets directly?

Dataset delivery is not included by default and must be separately scoped.

How should a PoC start?

Use representative URLs, object sizes, retry behavior, and a target completion window.

Are browser collectors supported?

Yes, through standard proxy protocols, but browser resource concurrency and worker capacity must be measured.

What are the compliance boundaries?

Collect only lawfully accessible public data and follow applicable terms, intellectual-property, privacy, and legal requirements.

Assess proxy capacity with a real data workload

Share representative targets, data types, expected scale, and the required completion window.

Assess high-bandwidth capacity Contact sales