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Google Cloud Media and Entertainment United States

Picsart

Application Refactoring

Picsart democratizes visual content with intuitive, AI-powered photo and video editing tools for billions. Leveraging Google Cloud, Gemini platform, and Gemini models, they seamlessly scale and rapidly deploy advanced automated editing features.

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"With Gemini Omni, we're thrilled to bring this powerful, granular technology to 130 million Picsart users worldwide."
90% of users discover Picsart organically
From family inspiration to 2.5 billion installs: Picsart's AI-powered creative journey
Over 2.5 billion global installs across devices

Reported impact

Business outcome

From family inspiration to 2.5 billion installs: Picsart's AI-powered creative journey Over 2.5 billion global installs across devices

  • Customer Experience
  • Scalability
  • Speed to Market

Solution

Picsart democratizes visual content with intuitive, AI-powered photo and video editing tools for billions. Leveraging Google Cloud, Gemini platform, and Gemini models, they seamlessly scale and rapidly deploy advanced automated editing features. Today, Picsart has scaled into a creative content powerhouse, boasting over 2.5 billion installs worldwide. The platform empowers a highly diverse global user base, ranging from Gen Z teenagers and social media influencers to solopreneurs selling items on Etsy. However, transitioning from simple manual photo filters to automated asset generation created massive behind-the-scenes technical challenges. Supporting token-intensive, video-based workloads required an incredibly agile backend ecosystem. Picsart needed infrastructure capable of rapid, near-real-time scaling to handle intense spikes in usage without inflating costs.

Solution architecture

6 components · 3 layers
  1. Compute
    • Gemini Flash Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Compute Engine Runs containerized or virtualized application workloads with the elasticity needed for production scale.
    • Google Kubernetes Engine (GKE) Runs containerized or virtualized application workloads with the elasticity needed for production scale.
    • Google Cloud GPUs Provides the cloud foundation that replaces legacy infrastructure and supports scalable modernization.
  2. Storage
    • Cloud Storage Stores source documents, media, or raw files that feed the automated workflow.
  3. Orchestration
    • Gemini platform Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.

Architecture clues

  • Delivering an agentic experience for creatives
  • Looking forward into 2026, Picsart is fully leaning into a rapidly shifting, agentic ecosystem where creators operate as versatile generalists.
  • They adopted Google Kubernetes Engine (GKE) to right-size their applications dynamically in real time within a containerized framework.

Evidence from the source

From family inspiration to 2.5 billion installs: Picsart's AI-powered creative journey
Over 2.5 billion global installs across devices
Picsart democratizes visual content with intuitive, AI-powered photo and video editing tools for billions. Leveraging Google Cloud, Gemini platform, and Gemini models, they seamlessly scale and rapidly deploy advanced au
To resolve these architectural hurdles, Picsart chose to build its core environment on Google Cloud nearly nine years ago, establishing a trusted, single-vendor relationship. To store the massive daily influx of user med
Today, Picsart has scaled into a creative content powerhouse, boasting over 2.5 billion installs worldwide. The platform empowers a highly diverse global user base, ranging from Gen Z teenagers and social media influence
Transparency Evidence & methodology 86/100 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 value86/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 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. Google Cloud

    Picsart customer story

    First-party
    https://cloud.google.com/customers/picsart
    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 19, 2026 · 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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