Scales Travel & Hospitality

Deliver fast and accurate semantic search

"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.

# 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,
)
Filter Ultra-low latency
Rocket Hybrid Search
Dollar Billion+ Vector Scale
Server Native inference capability

“With a billion+ user-generated, multimodal reviews from 100s of millions of MAUs, you need a way to bring it together.”

Rahul Todkar avatar

Rahul Todkar

Head of Data and AI, Tripadvisor

2-3x

Revenue


1B+

Reviews Indexed


“Since running it in production, it's probably been one of the most frictionless parts of the stack.”

Patrick Lombardo avatar

Patrick Lombardo

Staff ML Engineer, Opentable

Fast, Predictable Response Latency


>60K

Searched with Precision Filtering


Why Teams Choose Qdrant

Legacy Search Engines Weren't Built for How Travelers Think

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.

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Real-Time Updates Spike Your Latency

Batch inventory updates can spike P95 latency. Availability changes for hotels and restaurants can't wait for reindexing.

Qdrant's real-time payload updates change availability, pricing, and inventory without touching the index.

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Structured Filters Can't Capture Intent

Travelers search semantically (e.g. "romantic rooftop dinner.") Even 600+ filterable attributes can still miss unstructured intent.

Qdrant's hybrid search combines semantic understanding with structured filters for amenities, location, and availability.

Why People Migrate to Qdrant

Evaluating Migration?

Our solutions engineers do technical deep-dives with Travel and Hospitality tech teams weekly.

Book a Session

What you can build with Qdrant

From AI concierges to semantic property search, travel and hospitality teams combine Qdrant's retrieval primitives to deliver experiences keyword search never could.

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AI-Powered Discovery

"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.

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Semantic Venue Search

"Competition lap pool" or "hip modern bar" aren't filterable fields. Dense embeddings over reviews and descriptions surface properties matching intent.

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Personalized Recommendations

Capture dining style, travel patterns, and review history. Surface personalized recommendations, updated with every interaction.

How It Works Under the Hood

Architecture patterns with API examples for travel and hospitality search, recommendations, and multi-tenancy.

Hybrid Search

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.

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Dense Vectors

Semantic understanding of reviews and descriptions

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Sparse Vectors (BM25/SPLADE)

For exact terms (e.g. cuisine, chain, amenity names)

# 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,
)

RAG-Powered AI Concierge

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.

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Hybrid retrieval for reviews

Dense + sparse captures vibe and specifics

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Scoped filtering

Retrieve only reviews for what’s being asked about

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Real-time payload updates

Quickly Index New Reviews to Avoid Stale Recommendations

# 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}"
)

Geo-Partitioned Search

Custom shard keys partition inventory by region so queries stay local, with cross-region discovery when users browse globally.

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Custom shard keys by region

Geo-hash or country code — queries stay local to the shard

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Cross-region discovery

Search across shards when users browse globally

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Time-based sharding for seasonal inventory

Easy deletion of expired listings without reindexing

# 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,
)

Powering Search For

OpenTable logo
Tripadvisor logo
Sprinklr logo
OpenTable logo
Tripadvisor logo
Sprinklr logo
OpenTable logo
Tripadvisor logo
Sprinklr logo

“Qdrant not only delivered on our performance requirements, but also kept costs in check”

Raghav Sonavane avatar

Raghav Sonavane

Associate Director ML, Sprinklr

90%

Faster Indexing Time


30%

Lower Retrieval Costs


FAQs

How Does Qdrant Handle Real-Time Availability Updates Without Latency Spikes?
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.
Can Qdrant Scale to Billions of Review Embeddings?
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.
How Does Hybrid Search Work for Travel Discovery?
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.
What Deployment Options Are There?
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.

Talk to an expert about Travel and Hospitality retrieval.

We'll show you the architecture that fits.

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Rocket flying over globe illustration