---
title: Hospitality & Travel
description: Discover how Qdrant's vector search technology can transform the hospitality and travel industry by enhancing personalization, improving content discovery, and optimizing fraud detection.
url: /hospitality-and-travel/
social_preview_image: /hospitality-and-travel/social-preview.png
sections:
  #hero-section
  - type: hero
    badge:
      title: Travel & Hospitality
      icon:
        src: /icons/outline/briefcase-business.svg
        alt: Scales
    title: Deliver fast and accurate semantic search
    description: '"Hip modern bar near the beach" doesn’t work with keyword search. Qdrant enables semantic understanding, real-time availability, and hybrid search that’s predictably fast.'
    containedButton:
      text: Talk to an Expert
      url: /contact-us/
    outlinedButton:
      text: Read the Docs
      url: /documentation/
    language: Python
    code: |
        # Hybrid venue search with availability
        results = client.query_points(
            collection_name="venues",
            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("available", match=MatchValue(True)),
                FieldCondition("city", match=MatchValue("Paris")),
                FieldCondition("rating", range=Range(gte=4.0)),
                FieldCondition("price_per_night",
                    range=Range(lte=300.0)),
            ]),
            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: Ultra-low latency
        icon:
          src: /icons/outline/filter-green.svg
          alt: Filter
      - id: 1
        title: Hybrid Search
        icon:
          src: /icons/outline/rocket-blue-small.svg
          alt: Rocket
      - id: 2
        title: Billion+ Vector Scale
        icon:
          src: /icons/outline/circle-dollar-sign.svg
          alt: Dollar
      - id: 3
        title: Native inference capability
        icon:
          src: /icons/outline/server.svg
          alt: Server

  #testimonials-section
  - type: testimonials
    testimonials:
      - id: 0
        reverse: false
        review: “With a billion+ user-generated, multimodal reviews from 100s of millions of MAUs, you need a way to bring it together.”
        author:
          name: Rahul Todkar
          role: Head of Data and AI, Tripadvisor
          avatar:
            src: /img/hospitality-and-travel/customer1.svg
            alt: Rahul Todkar avatar
        metric:
          - id: 0
            title: 2-3x
            description: Revenue
          - id: 1
            title: 1B+
            description: Reviews Indexed
        logo:
          src: /img/hospitality-and-travel/tripadvisor.svg
          alt: Tripadvisor logo
      - id: 1
        reverse: true
        review: “Since running it in production, it's probably been one of the most frictionless parts of the stack.”
        author:
          name: Patrick Lombardo
          role: Staff ML Engineer, Opentable
          avatar:
            src: /img/hospitality-and-travel/customer2.svg
            alt: Patrick Lombardo avatar
        metric:
          - id: 0
            description: Fast, Predictable Response Latency
          - id: 1
            title: ">60K"
            description: Searched with Precision Filtering
        logo:
          src: /img/hospitality-and-travel/open-table.svg
          alt: OpenTable logo
          
  #bento-cards-section
  - type: bento-cards
    subtitle: Why Teams Choose Qdrant
    title: Legacy Search Engines Weren't Built for How Travelers Think
    description: Keyword search, rigid filters, and batch pipelines break under the demands of modern travel platforms. Teams running semantic search, AI assistants, and real-time inventory hit the same walls.
    cards:
      - id: 0
        icon:
          src: /icons/outline/gauge.svg
          alt: Gauge
        title: Real-Time Updates Spike Your Latency
        description: Batch inventory updates can spike P95 latency. Availability changes for hotels and restaurants can't wait for reindexing.<br><br>Qdrant's real-time payload updates change availability, pricing, and inventory without touching the index.
      - id: 1
        icon:
          src: /icons/outline/filter-pink.svg
          alt: Filter
        title: Structured Filters Can't Capture Intent
        description: Travelers search semantically (e.g. "romantic rooftop dinner.") Even 600+ filterable attributes can still miss unstructured intent.<br><br>Qdrant's hybrid search combines semantic understanding with structured filters for amenities, location, and availability.

  #case-studies-section
  - type: case-studies
    title: Why People Migrate to Qdrant
    caseStudies:
      - id: 0
        title: “We couldn’t  provide high quality, fast retrieval ”
        description: Write performance with legacy, Java-based search engines couldn’t keep up.
      - id: 1
        title: “Vector search must adapt to different workloads”
        description: Fine-tuning configurations at the collection level is crucial for varied AI applications
      - id: 2
        title: “RAM Usage Spiked Costs”
        description: Quantization and memory mapping  enables customers to reduce RAM usage, meeting cost goals

  #get-contacted-section
  - type: get-contacted
    title: Evaluating Migration?
    description: Our solutions engineers do technical deep-dives with Travel and Hospitality tech teams weekly.
    contactUs:
      text: Book a Session
      url: /contact-us/
      
  #bento-cards-section
  - type: bento-cards
    title: What you can build with Qdrant
    description: From AI concierges to semantic property search, travel and hospitality teams combine Qdrant's retrieval primitives to deliver experiences keyword search never could.
    cards:
      - id: 0
        icon:
          src: /icons/outline/search-blue.svg
          alt: Search
        title: AI-Powered Discovery
        description: '"Find me a romantic rooftop restaurant with a view in Rome" returns ranked results that understand ambiance, cuisine, and location intent. RAG over reviews grounds AI responses in real guest experiences.'
        chips:
          - id: 0
            title: Hybrid Search
            link: /documentation/search/hybrid-queries/
          - id: 1
            title: Payload Filters
            link: /documentation/search/filtering/
          - id: 2
            title: Reciprocal Rank  Fusion (RRF)
            link: /documentation/search/hybrid-queries/#reciprocal-rank-fusion-rrf
      - id: 1
        icon:
          src: /icons/outline/file-text-blue.svg
          alt: File
        title: Semantic Venue Search
        description: "\"Competition lap pool\" or \"hip modern bar\" aren't filterable fields. Dense embeddings over reviews and descriptions surface properties matching intent."
        chips:
          - id: 0
            title: Semantic-Search
            link: /documentation/search/text-search/#semantic-search
      - id: 2
        icon:
          src: /icons/outline/heart-handshake.svg
          alt: Handshake
        title: Personalized Recommendations
        description: Capture dining style, travel patterns, and review history. Surface personalized recommendations, updated with every interaction.
        chips:
          - id: 0
            title: Recommend API
            link: /documentation/search/explore/#recommendation-api
          - id: 1
            title: Discovery API
            link: /documentation/search/explore/#discovery-api

  #architecture-section
  - type: architecture
    title: How It Works Under the Hood
    description: Architecture patterns with API examples for travel and hospitality search, recommendations, and multi-tenancy.
    sections:
      - id: 0
        title: Hybrid Search
        description: Guest intent is part structured (dates, location, price) and part unstructured ("romantic," "family-friendly," "hip vibe"). This pattern fuses semantic and keyword retrieval with structured inventory filters in a single query.
        link:
          href: /documentation/search/hybrid-queries/?q=hybrid#hybrid-search
          text: View Full Example
        steps:
          - id: 0
            title: Dense Vectors
            description: Semantic understanding of reviews and descriptions
          - id: 1
            title: Sparse Vectors (BM25/SPLADE)
            description: For exact terms (e.g. cuisine, chain, amenity names)
        language: Python
        code: |
          # Hybrid venue search with availability
          results = client.query_points(
              collection_name="venues",
              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("available", match=MatchValue(True)),
                  FieldCondition("city", match=MatchValue("Paris")),
                  FieldCondition("rating", range=Range(gte=4.0)),
                  FieldCondition("price_per_night",
                      range=Range(lte=300.0)),
              ]),
              limit=20,
          )
      - id: 1
        title: RAG-Powered AI Concierge
        description: "Ground AI assistants in real guest reviews and venue data. This is the pattern OpenTable uses for Concierge: vectorize review chunks, retrieve relevant context per question, and generate grounded answers that reflect actual guest experiences."
        link:
          href: /blog/case-study-opentable/
          text: View Full Example
        steps:
          - id: 0
            title: Hybrid retrieval for reviews
            description: Dense + sparse captures vibe and specifics
          - id: 1
            title: Scoped filtering
            description: Retrieve only reviews for what’s being asked about
          - id: 2
            title: Real-time payload updates
            description: Quickly Index New Reviews to Avoid Stale Recommendations
        language: Python
        code: |
          # RAG: retrieve review context for AI assistant
          review_chunks = client.query_points(
              collection_name="review_chunks",
              prefetch=[
                  Prefetch(query=question_emb, using="dense",
                      limit=50),
                  Prefetch(query=question_sparse, using="sparse",
                      limit=50),
              ],
              query=FusionQuery(fusion=Fusion.RRF),
              query_filter=Filter(must=[
                  FieldCondition("venue_id",
                      match=MatchValue("restaurant_456")),
              ]),
              limit=10,
          )
          
          # Feed to LLM with grounded context
          context = "\n".join([r.payload["text"]
              for r in review_chunks.points])
          answer = llm.generate(
              f"Based on reviews: {context}\n"
              f"Question: {user_question}"
          )
      - id: 2
        title: Geo-Partitioned Search
        description: Custom shard keys partition inventory by region so queries stay local, with cross-region discovery when users browse globally.
        link:
          href: /documentation/search/filtering/#geo
          text: View Full Example
        steps:
          - id: 0
            title: Custom shard keys by region
            description: Geo-hash or country code — queries stay local to the shard
          - id: 1
            title: Cross-region discovery
            description: Search across shards when users browse globally
          - id: 2
            title: Time-based sharding for seasonal inventory
            description: Easy deletion of expired listings without reindexing
        language: Python
        code: |
          # Geo-partitioned collection
          client.create_collection(
              "global_venues",
              vectors_config=VectorParams(
                  size=1536, distance="Cosine"),
              sharding_method=ShardingMethod.CUSTOM,
          )
          
          # Create region shard
          client.create_shard_key(
              "global_venues",
              shard_key="europe_west",
          )
          
          # Upsert with region routing
          client.upsert(
              "global_venues",
              points=[PointStruct(
                  id=1,
                  vector=venue_embedding,
                  payload={
                      "region": "europe_west",
                      "city": "Barcelona",
                      "type": "hotel",
                      "available": True,
                  },
              )],
              shard_key_selector="europe_west",
          )
          
          # Query scoped to region
          client.query_points(
              "global_venues",
              query=embedding,
              shard_key_filter=["europe_west"],
              limit=20,
          )

  #logos-section
  - type: logos
    title: Powering Search For
    logos:
      - id: 0
        icon:
          src: /img/hospitality-and-travel/open-table-light.svg
          alt: OpenTable logo
      - id: 1
        icon:
          src: /img/hospitality-and-travel/tripadvisor-light.svg
          alt: Tripadvisor logo
      - id: 2
        icon:
          src: /img/hospitality-and-travel/sprinklr-light.svg
          alt: Sprinklr logo

  #testimonials-section
  - type: testimonials
    testimonials:
      - id: 1
        reverse: false
        review: “Qdrant not only delivered on our performance requirements, but also kept costs in check”
        author:
          name: Raghav Sonavane
          role: Associate Director ML, Sprinklr
          avatar:
            src: /img/hospitality-and-travel/customer3.svg
            alt: Raghav Sonavane avatar
        metric:
          - id: 0
            title: 90%
            description: Faster Indexing Time
          - id: 1
            title: 30%
            description: Lower Retrieval Costs
        logo:
          src: /img/hospitality-and-travel/sprinklr.svg
          alt: Sprinklr logo

  #faq-section
  - type: faq
    title: FAQs
    questions:
      - id: 0
        question: How Does Qdrant Handle Real-Time Availability Updates Without Latency Spikes?
        answer: Qdrant separates payload updates from vector indexing. When a hotel room sells out or a restaurant table fills, you update the availability payload field directly. This doesn't trigger reindexing, so your search latency stays flat. Teams processing millions of inventory changes per day use this to keep results accurate to the second without the P95 spikes batch reindexing causes.
      - id: 1
        question: Can Qdrant Scale to Billions of Review Embeddings?
        answer: Yes. Travel platforms generate massive embedding volumes from reviews, listings, and images. Qdrant's quantization options (scalar for 4× compression, binary for 32×) and disk offloading keep this manageable at scale. Tripadvisor, for example, uses Qdrant to unlock insights from over a billion user-generated contributions and hundreds of millions of images across its AI-powered travel platform.
      - id: 2
        question: How Does Hybrid Search Work for Travel Discovery?
        answer: Travel search is part structured (dates, location, price, rating) and part unstructured ("hip modern bar" or "romantic rooftop dinner"). Qdrant's hybrid search fuses dense vectors (semantic understanding from reviews and descriptions), sparse vectors (BM25 keyword matching for exact terms), and metadata filters (location, availability, price) in a single query using RRF fusion.
      - id: 3
        question: What Deployment Options Are There?
        answer: Qdrant supports managed cloud, BYOC (any cloud with Kubernetes), hybrid cloud, on-prem, and edge. Custom shard keys enable geo-partitioned architectures where queries stay region-local for low latency. Multi-AZ deployment on Premium tier provides a 99.9% uptime SLA. Qdrant is SOC2 and GDPR compliant, and an EU-based company.
        
  #cta-banner-section
  - type: cta-banner
    title: Talk to an expert about <span>Travel and Hospitality</span> retrieval.
    description: We'll show you the architecture that fits.
    button:
      text: Talk to an Expert
      url: /contact-us/

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