landing_page/qdrant-landing/content/industries/e-commerce.md

---
title: AI-Powered Vector Search for Smarter Shopping & Personalized E-Commerce
description: ''
url: /e-commerce/
social_preview_image: /e-commerce/social-preview.png
sections:
  #hero-section
  - type: hero
    badge:
      title: E-commerce
      icon:
        src: /icons/outline/shopping-cart-blue.svg
        alt: Shopping cart
    title: Don’t Accept Slow Search
    description: Slow search, results missing intent, and infrastructure that can't handle traffic spikes shouldn’t be expected.<br>Qdrant enables fast, accurate results.
    containedButton:
      text: Talk to an Expert
      url: /contact-us/
    outlinedButton:
      text: Read the Docs
      url: /documentation/
    language: Python
    code: |
        # Hybrid search with business-logic reranking
        results = client.query_points(
            collection_name="products",
            prefetch=[
                Prefetch(query=dense_emb, using="dense", limit=100),
                Prefetch(query=sparse_emb, using="sparse", limit=100),
            ],
            query=FusionQuery(fusion=Fusion.RRF),
            query_filter=Filter(must=[
                FieldCondition("in_stock", match=MatchValue(True)),
                FieldCondition("category", match=MatchValue("shoes")),
                FieldCondition("price", range=Range(lte=150.0)),
            ]),
            limit=20,
        )
    steps:
      - id: 0
        title: Step 1
        description: Embed - Parse + Embed Document
        icon:
          src: /icons/outline/square-code.svg
          alt: Code
      - id: 1
        title: Step 2
        description: Search - Semantic Search + Strict Filter
        icon:
          src: /icons/outline/filter-blue-small.svg
          alt: Filter
      - id: 2
        title: Step 3
        description: Rank - Rank + Rerank (Optional)
        icon:
          src: /icons/outline/list.svg
          alt: List
      - id: 3
        title: Step 4
        description: Result - Evidence-based Match
        icon:
          src: /icons/outline/circle-check.svg
          alt: Check
    badges:
      - id: 0
        title: Predictable low latency
        icon:
          src: /icons/outline/rocket-green.svg
          alt: Rocket
      - id: 1
        title: Hybrid Search
        icon:
          src: /icons/outline/locate-fixed-blue.svg
          alt: Locate fixed
      - id: 2
        title: Multimodal Search
        icon:
          src: /icons/outline/image-blue.svg
          alt: Image
      - id: 3
        title: Optimize cost at scale
        icon:
          src: /icons/outline/circle-dollar-sign.svg
          alt: Dollar

  #testimonials-section
  - type: testimonials
    testimonials:
      - id: 0
        reverse: false
        review: “Qdrant cut retrieval time by 90%. That made it possible to stay under our latency SLA.”
        author:
          name: Kshitiz Parashar
          role: AI Engineer, Alhena
          avatar:
            src: /img/e-commerce/customer1.svg
            alt: Kshitiz Parashar avatar
        metric:
          - id: 0
            title: 90%
            description: Latency Improvement
          - id: 1
            icon: 
              src: /icons/outline/chart-no-axes-combined-green.svg
              alt: Chart
            description: Scaled Multitenancy
        logo:
          src: /img/e-commerce/alhena.svg
          alt: Alhena logo
      - id: 1
        reverse: true
        review: “Vector search is a key for modern AI infrastructure. Not just for fraud detection, but as a foundation for new AI systems.”
        author:
          name: Shardul Aggarwal
          role: SDE-III, Trust & Safety, Flipkart
          avatar:
            src: /img/e-commerce/customer2.svg
            alt: Shardul Aggarwal avatar
        metric:
          - id: 0
            title: 99%+
            description: Reduction in fraud detection time
        logo:
          src: /img/e-commerce/flipkart.svg
          alt: Aracor logo
          
  #bento-cards-section
  - type: bento-cards
    subtitle: Why Teams Choose Qdrant
    title: Semantic and Multimodal Need Native Vector Search
    description: Many teams that come to us are already running vector search. But they hit a wall at filter performance, cost, or scale. Here's what they’re saying.
    cards:
      - id: 0
        icon:
          src: /icons/outline/gauge-orange.svg
          alt: Gauge
        title: Search Latency Kills Conversion
        description: Traditional solutions show 150-200ms+ latency for vector search. Every 100ms delay costs measurable revenue. Qdrant can deliver sub-50ms P95 with hybrid search at 1000+ QPS.
      - id: 1
        icon:
          src: /icons/outline/circle-alert.svg
          alt: Circle alert
        title: Keyword Search Fails Your Shoppers
        description: '"Blue T-shirt with yellow buttons" returns nothing. "Evening dress accessories" gets zero results. Qdrant''s hybrid search combines semantic understanding with keyword matching and metadata filters to eliminate zero-result pages.'

  #case-studies-section
  - type: case-studies
    title: Here’s Why Clients Migrate to Qdrant
    caseStudies:
      - id: 0
        title: “We used Postgres to ship. It was a short-term answer.”
        description: Postgres is fast to start, but can’t scale. Users deal with manual partitioning, latency spikes, and climbing storage. Qdrant is proven at scale.
      - id: 1
        title: “Our search latency is unpredictable”
        description: Java-based solutions have 200ms+ latency for vector search, directly costing revenue. Qdrant delivers faster, more predictable latency.
      - id: 2
        title: “Filters destroy our recall.”
        description: Pre-filtering and Post-filter both have tradeoffs. Qdrant’s one-stage filtering eliminates this dilemma.

  #get-contacted-section
  - type: get-contacted
    title: Evaluating Migration?
    description: Our solutions engineers do technical deep-dives with E-commerce search teams.
    contactUs:
      text: Book a Session
      url: /contact-us/
      
  #bento-cards-section
  - type: bento-cards
    title: What you can build with Qdrant
    description: From product discovery to fraud detection, e-commerce teams combine Qdrant's retrieval primitives to solve problems generic search engines can't.
    cards:
      - id: 0
        icon:
          src: /icons/outline/search-blue.svg
          alt: Search
        title: Product Search & Discovery
        description: '"Blue T-shirt with yellow buttons" returns relevant results instead of zero matches. Dense vector similarity understands product intent.'
        chips:
          - id: 0
            title: Hybrid Search
            link: /documentation/search/hybrid-queries/
          - id: 1
            title: Payload Filters
            link: /documentation/search/filtering/
          - id: 2
            title: RRF Fusion
            link: /documentation/search/hybrid-queries/#reciprocal-rank-fusion-rrf
      - id: 1
        icon:
          src: /icons/outline/file-text-blue.svg
          alt: File text
        title: Personalized Recommendation
        description: 'Item-to-item similarity vectors surface "You might also like" recommendations with fast latency.'
        chips:
          - id: 0
            title: Recommendations API
            link: /documentation/search/explore/#recommendation-api
          - id: 1
            title: Filtering
            link: /documentation/search/filtering/
      - id: 2
        icon:
          src: /icons/outline/heart-handshake.svg
          alt: Handshake
        title: Inventory & Catalog Intelligence
        description: Find similar/duplicate listings across millions of SKUs using image and text embeddings. Flipkart uses this for fraud detection.
        chips:
          - id: 0
            title: Recommendation API
            link: /documentation/search/explore/#recommendation-api
          - id: 0
            title: Discovery API
            link: /documentation/search/explore/#discovery-api

  #architecture-section
  - type: architecture
    title: Common E-commerce Patterns
    description: Architecture patterns with API examples for e-commerce search, recommendations, and multi-tenancy.
    sections:
      - id: 0
        title: Hybrid Product Search Pipeline
        description: Combine semantic understanding, keyword matching, and business rules in a single query.
        link:
          href: /articles/sparse-embeddings-ecommerce-part-1/
          text: View Full Example
        steps:
          - id: 0
            title: Dense Vectors
            description: (e.g. OpenAI, Cohere) for semantic product understanding
          - id: 1
            title: Sparse Vectors
            description: (BM25/SPLADE) for exact keyword matching
        language: Python
        code: |
          # Hybrid search with business-logic reranking
          results = client.query_points(
              collection_name="products",
              prefetch=[
                  Prefetch(query=dense_emb, using="dense", limit=100),
                  Prefetch(query=sparse_emb, using="sparse", limit=100),
              ],
              query=FusionQuery(fusion=Fusion.RRF),
              query_filter=Filter(must=[
                  FieldCondition("in_stock", match=MatchValue(True)),
                  FieldCondition("category", match=MatchValue("shoes")),
                  FieldCondition("price", range=Range(lte=150.0)),
              ]),
              limit=20,
          )
      - id: 1
        title: Multitenant Marketplace Architecture
        description: Isolate each seller's catalog within a single shared cluster.
        link:
          href: /documentation/manage-data/multitenancy/
          text: View Full Example
        steps:
          - id: 0
            title: Per-tenant HNSW indexes
            description: Via payload indexing
          - id: 1
            title: Tenant Promotion
            description: Move large tenants to dedicated shards
          - id: 2
            title: Custom shard keys
            description: For geo or time partitioning
        language: Python
        code: |
          # Payload-based multi-tenancy
          client.create_collection(
              "marketplace",
              vectors_config=VectorParams(size=1536, distance="Cosine"),
              hnsw_config=HnswConfigDiff(payload_m=16, m=0),
              on_disk_payload=True,
          )
          
          # Create per-tenant index
          client.create_payload_index(
              "marketplace", "tenant_id",
              field_schema=PayloadSchemaType.KEYWORD,
              is_tenant=True,  # enables per-tenant HNSW
          )
          
          # Query scoped to tenant
          client.query_points(
              "marketplace",
              query=embedding,
              query_filter=Filter(must=[
                  FieldCondition("tenant_id", match=MatchValue("brand_123"))
              ]),
              limit=20,
          )
      - id: 2
        title: Real-Time Recommendations Engine
        description: User interactions update vectors in real time. No batch processing delays. Combine item similarity, user profiles, and contextual signals with business rules for margins, inventory, and promotions.
        link:
          href: /documentation/search/explore/?q=recommendation#recommendation-api
          text: View Full Example
        steps:
          - id: 0
            title: Item-to-Item
            description: Similarity via dense vectors
          - id: 1
            title: Business rules
            description: (Margin, inventory) via metadata filters
          - id: 2
            title: Recommend API
            description: For behavioral matching
        language: Python
        code: |
          # Real-time recommendation with business rules
          results = client.recommend(
              collection_name="products",
              positive=[last_viewed_id, last_purchased_id],
              negative=[returned_item_id],
              query_filter=Filter(must=[
                  FieldCondition("in_stock", match=MatchValue(True)),
                  FieldCondition("margin", range=Range(gte=0.25)),
              ]),
              strategy=RecommendStrategy.BEST_SCORE,
              limit=12,
          )
          
          # Update user vector on interaction (real-time)
          client.set_payload(
              "users",
              payload={"last_active": datetime.now().isoformat()},
              points=[user_id],
          )

  #logos-section
  - type: logos
    title: Powering E-Commerce Applications For
    logos:
      - id: 0
        icon:
          src: /img/e-commerce/flipkart-light.svg
          alt: Flipkart logo
      - id: 1
        icon:
          src: /img/e-commerce/alhena-light.svg
          alt: Alhena logo
      - id: 2
        icon:
          src: /img/e-commerce/meesho-light.svg
          alt: Meesho logo
      - id: 3
        icon:
          src: /img/e-commerce/convo-search-light.svg
          alt: ConvoSearch logo
      - id: 5
        icon:
          src: /img/e-commerce/bazaarvoice-light.svg
          alt: Bazaarvoice logo

  #testimonials-section
  - type: testimonials
    testimonials:
      - id: 0
        reverse: true
        review: “Qdrant transformed our recommendation engine capabilities, making us indispensable to our clients.”
        author:
          name: Shardul Aggarwal
          role: CEO, ConvoSearch
          avatar:
            src: /img/e-commerce/customer3.svg
            alt: Shardul Aggarwal avatar
        metric:
          - id: 0
            title: 50%+
            description: Latency Improvement from 100ms to 10ms
          - id: 1
            title: 60%
            description: Increase revenue for Convosearch clients
        logo:
          src: /img/e-commerce/convo-search.svg
          alt: ConvoSearch logo

  #faq-section
  - type: faq
    title: FAQs
    questions:
      - id: 0
        question: Can Qdrant Handle Our Traffic Spikes During Sales Events?
        answer: Yes. Qdrant's horizontal scaling with auto-sharding handles 4x-100x traffic spikes without manual intervention. Add nodes and the operator auto-distributes shards. Quantization (scalar for 4x compression, binary for 32x) keeps memory costs predictable even at peak load.
      - id: 1
        question: What Deployment Options Work for Multi-Region E-Commerce?
        answer: Qdrant supports managed cloud, BYOC (any cloud with Kubernetes), hybrid cloud, on-prem, and edge deployments. SOC2 and GDPR compliant. EU-based company. Multi-AZ deployment with zero-downtime upgrades for 99.99% availability.
      - id: 2
        question: How Does Multi-Tenancy Work for Marketplace Platforms?
        answer: Qdrant supports payload-based multi-tenancy that scales to 100k+ tenants in a single collection. Per-tenant HNSW indexes with disabled global indexing prevent cross-tenant interference. Large tenants can be promoted to dedicated shards. This avoids the file descriptor limits of collection-per-tenant approaches.
      - id: 3
        question: Can We Combine Image and Text Search in One Query?
        answer: Yes. Qdrant's multi-vector support lets you store and search across dense embeddings (semantic), sparse embeddings (keyword), image embeddings (CLIP/ColPali), and user behavior embeddings simultaneously. Prefetching enables parallel multi-modal retrieval with RRF fusion for balanced ranking.
      - id: 4
        question: How Does Qdrant Compare to Legacy search engines for E-Commerce Search?
        answer: Legacy Java-based search engines were built for text search and added vector capabilities as a bolt-on. Qdrant is purpose-built for vector workloads with native hybrid search (dense + sparse vectors + metadata filters in a single query). E-commerce teams report significant improvements when migrating.

  #cta-banner-section
  - type: cta-banner
    title: Talk to an expert about <br><span>E-commerce</span> retrieval.
    description: Let’s discuss your catalog size, traffic patterns, and current stack.
    button:
      text: Talk to an Expert
      url: /contact-us/

build:
  render: always
cascade:
  - build:
      list: local
      publishResources: false
      render: never
---