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Huawei Cloud Software / SaaS China Translated from Chinese

Meitu

Marketing Personalization

Meitu needed to scale AIGC image and video products for both consumer and productivity use cases while controlling GPU cost, accelerating model iteration, and keeping global data movement and user experience reliable.

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data-transfer efficiency improved by 50%
latency reduced by 30%
monthly active users exceeded 100 million
monthly use exceeded 21 billion times

Reported impact

Business outcome

Meitu increased the scale and profitability of its AIGC business, improved global data-transfer performance, and adapted more business modules and models to Ascend while supporting rapid product growth.

  • Productivity Improvement
  • Revenue Growth
  • Scalability
  • Speed to Market

Solution

Meitu used Huawei Cloud to turn AI image and video products from isolated features into a scalable AIGC production platform. The company first moved its core business onto Huawei Cloud's cloud-native distributed infrastructure, then used global one-network connectivity, Cloud Connect, and migration services to build a worldwide compute-and-storage network across cloud and IDC sites. As AIGC workloads expanded, the bottleneck shifted from CPU to GPU, so Meitu adopted Ascend Cloud Service plus ModelArts, CCE, and related tooling to support training, deployment, and inference for more than 19 models across 6+ business modules. ModelArts provides the end-to-end AI workflow from data processing through model deployment, while the global network layer moves large files and training data between sites with lower latency. These capabilities support consumer image apps as well as productivity products such as AI Product Images inside Meitu Design Studio.

Data flow

Core business and content data move across Meitu's global cloud and IDC network through Cloud Connect and migration services, then enter ModelArts-driven AI workflows for data processing, model training, management, and deployment on Ascend compute. Outputs from those models are served into consumer image apps and productivity products such as AI Product Images.

Solution architecture

5 components · 3 layers
  1. Compute
    • Ascend Cloud Service Supplies diversified AI compute for training and inference as GPU demand grows.
  2. Serving
    • Cloud Connect Builds the cross-cloud and cross-IDC high-speed network used to move data and connect global sites.
    • AI Product Images Applies generative AI to e-commerce creative production as a business productivity tool.
  3. Orchestration
    • cloud-native distributed infrastructure Hosts Meitu's core businesses and provides the cloud foundation for AIGC workloads.
    • ModelArts Provides end-to-end AI development, training, management, and deployment for Meitu's models.

Architecture clues

  • AIGC demand shifted Meitu's core compute need from CPU to GPU.
  • Ascend adaptation covers more than 6 business modules and more than 19 models.
  • Meitu built a global compute-and-storage network across cloud and IDC sites.
  • ModelArts is used for end-to-end AI development and deployment.

Evidence from the source

"AI Product Images in Meitu Design Studio uses generative AI as an end-to-end productivity solution for e-commerce marketing materials. (original: 全新升级的“美图设计室”核心功能之一的“AI商品图”，帮助电商客户在商品营销物料制作流程中降本增效，为其提供端到端解决方案。)"
"Huawei Cloud provided Ascend AI cloud services, ModelArts, CCE, and full-stack tooling so Meitu could adapt 6+ business modules and 19+ models, diversify compute, and support more than 200 million users. (original: 高性价比的多样化AI算力，提供全链路云化工具ModelArts、CCE等... 完成6+业务模块，19+模型在昇腾云服务适配，满足2亿多用户对存储和性能的要求，实现算力多元化。)"
"Meitu built a global one-network compute-and-storage layer with Cloud Connect and migration services, raising network-transfer efficiency by 50%. (original: 通过云连接CC、迁移服务Mgc等云服务产品，快速构建起跨云-IDC、跨站点的高速、优质、稳定的网络能力... 提升网络传输效率50%；)"
"Meitu continues to expand AI applications on top of Huawei Cloud's solid compute foundation and cloud experience. (original: 美图公司致力于打造优秀的影像与设计产品... 依托华为云坚实的算力底座以及丰富的用云经验，美图持续开拓AI应用助推业务再增长。)"
"The global storage-and-compute network improved data-transfer speed by 50% and reduced network latency by 30%. (original: 通过和华为云的合作，美图构建起覆盖全球的高速存算网，数据传输速率提升50%，传输速率稳定，网络时延降低30%。)"
Transparency Evidence & methodology 100/100 reference value · 81% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence81%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency10/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Huawei Cloud

    卓越运营客户成功故事-美图-华为云

    First-party
    https://www.huaweicloud.com/cases/prime-insight-meitu.html
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Jul 8, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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