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MongoDB Media and Entertainment Global

Condé Nast

Marketing Personalization

Condé Nast needed a recommendation engine that could help millions of readers across more than 70 websites find relevant content without the latency, cost, and operational complexity of maintaining separate Elasticsearch pipelines for different asset classes.

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Click-through rates across Condé Nast's websites rose to 35%.
Condé Nast operates more than 70 websites.
MongoDB Atlas Vector Search handles 1,500 queries per second for recommendations and 500 queries per second for search.
The solution reduced latency by 90% and cut operational costs by 65%.

Reported impact

Business outcome

Condé Nast reduced recommendation latency by 90%, cut operational costs by 65%, raised click-through rates to 35%, and gave engineers a stable platform they can reuse for hybrid search and other AI-driven content experiences.

  • Cost Reduction
  • Customer Experience
  • Revenue Growth

Solution

Condé Nast consolidated the content assets behind more than 70 websites on MongoDB Atlas so it could rebuild its recommendation engine without maintaining separate Elasticsearch pipelines for each content type. Articles, images, audio, and video are stored centrally, then processed through Voyage AI embedding models to create vectors that are loaded into MongoDB Atlas Vector Search. The recommendation service queries those embeddings directly from Atlas to run image-based and similarity search for 'read more' experiences, while the same managed platform absorbs around 1,500 recommendation queries per second and 500 search queries per second. By moving storage and vector retrieval onto one data platform, the team eliminated brittle external pipelines, reduced operational complexity, and created a stable base for future hybrid search and cross-asset AI experimentation.

Data flow

Content from more than 70 Condé Nast websites is centralized in MongoDB Atlas, transformed into embeddings with Voyage AI, stored in MongoDB Atlas Vector Search, and queried by recommendation services that return related content to readers through search and 'read more' experiences.

Solution architecture

3 components · 3 layers
  1. Compute
    • Voyage AI Provides the embedding models Condé Nast uses to generate high-quality vectors for recommendation search.
  2. Storage
    • MongoDB Atlas Stores Condé Nast's cross-brand content corpus and supports the managed recommendation infrastructure.
  3. Serving
    • MongoDB Atlas Vector Search Serves vector-based recommendation and similarity queries over millions of content embeddings.

Architecture clues

  • MongoDB now processes vectors directly instead of sending data to an external pipeline.
  • The content corpus spans text, audio, video, and image assets.
  • The new platform is being extended toward hybrid search across all content types.
  • The prior architecture used three separate Elasticsearch recommendation pipelines.

Evidence from the source

"For Condé Nast, Vector Search handles 1,500 queries per second for recommendations and 500 queries per second for search."
"Per early measurements, the MongoDB solution reduced latency by 90% and cut operational costs by 65%."
"The resulting embeddings—approximately 2 million vectors, with a future goal to vectorize all content—were then stored in MongoDB Atlas Vector Search."
"We already had storage on MongoDB, and it made sense to simply use MongoDB to process vectors instead of sending the data to an external pipeline."
Transparency Evidence & methodology 95/100 reference value · 79% 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 value95/100
  • 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 scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • 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
    4 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 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. MongoDB

    Condé Nast customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/conde-nast
    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 Feb 5, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Dec 19, 2025 · MongoDB · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    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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