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Tencent Cloud Media and Entertainment Global Translated from Chinese

BIGO Live

Real-time Analytics

BIGO needed better recommendation-model training, large-scale storage, and elastic infrastructure that could absorb live-streaming spikes and global short-video traffic without costly manual overprovisioning.

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Clusters can be deployed in minutes.
Data centers are linked by dedicated private lines with TB-class total bandwidth.
The platform serves more than 400 million users.
The platform serves users in more than 150 countries and regions.

Reported impact

Business outcome

Tencent Cloud helped BIGO meet demanding global-concurrency requirements, deliver smoother viewing quality, reduce scaling friction, and optimize infrastructure cost for fast-changing traffic patterns.

  • Cost Reduction
  • Customer Experience
  • Scalability

Solution

BIGO moved key workloads onto Tencent Cloud so its live-streaming and short-video products could scale globally without overbuilding fixed capacity. Tencent Cloud provides the high-performance foundation for BIGO's online and offline big-data platforms, which support recommendation-model training and the platform's massive storage requirements. Cloud resources are combined with automatic scaling and traffic-scheduling services so live events, short-video bursts, and other sudden concurrency spikes can be absorbed without emergency capacity buys. Tencent also links data centers with dedicated TB-class private lines for low-latency, high-bandwidth interconnection, adds cross-availability-zone backup, and supports minute-level cluster deployment compatible with open-source stacks. The result is a globally distributed media platform that can serve hundreds of millions of users while optimizing both latency and infrastructure cost.

Data flow

User activity and content data feed BIGO's online and offline big-data platforms for recommendation-model training, while elastic cloud resources, traffic scheduling, CDN, and dedicated inter-data-center links deliver live streams and short-video traffic to global users with low latency.

Solution architecture

5 components · 3 layers
  1. Compute
    • Big Data Platform Runs BIGO's online and offline analytics environment for recommendation-model training and large-scale data processing.
    • Cloud Server Provides elastic compute capacity for streaming workloads and supporting services.
  2. Serving
    • Content Delivery Network CDN Accelerates content delivery to globally distributed end users.
  3. Orchestration
    • Auto Scaling Expands and shrinks infrastructure automatically when live and short-video traffic changes.
    • Traffic Scheduling Service Places and routes workloads quickly across clusters to meet latency and burst requirements.

Architecture clues

  • Tencent Cloud uses cross-availability-zone high availability and automatic data backup.
  • The solution combines online and offline big-data platforms.
  • Traffic scheduling is compatible with open-source systems and supports minute-level cluster deployment.

Evidence from the source

BIGO urgently needed more precise recommendation for a huge global user base and needed to analyze massive base data to optimize recommendation algorithms. (original: 急需提升终端用户精准推荐，需要对海量基础数据做模型分析以及推荐算法优化。)
Dedicated private lines totaling TB-class bandwidth connected data centers for global low-latency, high-bandwidth interconnection and faster user access. (original: 数据中心之间通过独享总计 TB 级带宽专线连接达到全球低延时高带宽的互联互通，达到网络加速的目的，提高用户的访问与接入速度，提升用户体验。)
Tencent Cloud provided high-performance infrastructure for BIGO's online and offline big-data platforms and strongly supported recommendation-model training and massive storage. (original: 腾讯云为 BIGO 大数据在线/离线平台提供高性能基础架构，很好地支持了终端用户算法推荐模型训练以及海量存储需求。)
Tencent Cloud's automatic scaling capability greatly optimized dynamic resource adjustment and cost optimization. (original: 腾讯云通过领先的自动扩缩容能力，为 BIGO 提供灵活的资源扩缩容服务，极大优化了资源动态调整以及成本优化。)
With Tencent Cloud's infrastructure and elastic scaling, BIGO products could handle massive concurrency and give more than 400 million users in over 150 countries smooth live-stream viewing. (original: 凭借腾讯云提供的基础构架和弹性扩缩容，BIGO 旗下各产品轻松应对海量并发苛刻要求，帮助全球150个国家的4亿多用户获得高质量的流畅直播观看体验。)
Transparency Evidence & methodology 75/100 reference value · 77% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • 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 transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    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. Tencent Cloud

    BIGO Live customer story

    First-party
    https://cloud.tencent.com/customer/bigo
    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
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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