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

title, description, url, social_preview_image, sections, build, cascade

title	description	url	social_preview_image	sections	build	cascade
AI-Powered Vector Search for Smarter Shopping & Personalized E-Commerce		/e-commerce/	/e-commerce/social-preview.png	type badge title description containedButton outlinedButton language code steps badges hero title icon E-commerce src alt /icons/outline/shopping-cart-blue.svg Shopping cart Don’t Accept Slow Search Slow search, results missing intent, and infrastructure that can't handle traffic spikes shouldn’t be expected.<br>Qdrant enables fast, accurate results. text url Talk to an Expert /contact-us/ text url Read the Docs /documentation/ Python # 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 title description icon 0 Step 1 Embed - Parse + Embed Document src alt /icons/outline/square-code.svg Code id title description icon 1 Step 2 Search - Semantic Search + Strict Filter src alt /icons/outline/filter-blue-small.svg Filter id title description icon 2 Step 3 Rank - Rank + Rerank (Optional) src alt /icons/outline/list.svg List id title description icon 3 Step 4 Result - Evidence-based Match src alt /icons/outline/circle-check.svg Check id title icon 0 Predictable low latency src alt /icons/outline/rocket-green.svg Rocket id title icon 1 Hybrid Search src alt /icons/outline/locate-fixed-blue.svg Locate fixed id title icon 2 Multimodal Search src alt /icons/outline/image-blue.svg Image id title icon 3 Optimize cost at scale src alt /icons/outline/circle-dollar-sign.svg Dollar type testimonials testimonials id reverse review author metric logo 0 false “Qdrant cut retrieval time by 90%. That made it possible to stay under our latency SLA.” name role avatar Kshitiz Parashar AI Engineer, Alhena src alt /img/e-commerce/customer1.svg Kshitiz Parashar avatar id title description 0 90% Latency Improvement id icon description 1 src alt /icons/outline/chart-no-axes-combined-green.svg Chart Scaled Multitenancy src alt /img/e-commerce/alhena.svg Alhena logo id reverse review author metric logo 1 true “Vector search is a key for modern AI infrastructure. Not just for fraud detection, but as a foundation for new AI systems.” name role avatar Shardul Aggarwal SDE-III, Trust & Safety, Flipkart src alt /img/e-commerce/customer2.svg Shardul Aggarwal avatar id title description 0 99%+ Reduction in fraud detection time src alt /img/e-commerce/flipkart.svg Aracor logo type subtitle title description cards bento-cards Why Teams Choose Qdrant Semantic and Multimodal Need Native Vector Search 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. id icon title description 0 src alt /icons/outline/gauge-orange.svg Gauge Search Latency Kills Conversion 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 icon title description 1 src alt /icons/outline/circle-alert.svg Circle alert Keyword Search Fails Your Shoppers "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. type title caseStudies case-studies Here’s Why Clients Migrate to Qdrant id title description 0 “We used Postgres to ship. It was a short-term answer.” 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 title description 1 “Our search latency is unpredictable” Java-based solutions have 200ms+ latency for vector search, directly costing revenue. Qdrant delivers faster, more predictable latency. id title description 2 “Filters destroy our recall.” Pre-filtering and Post-filter both have tradeoffs. Qdrant’s one-stage filtering eliminates this dilemma. type title description contactUs get-contacted Evaluating Migration? Our solutions engineers do technical deep-dives with E-commerce search teams. text url Book a Session /contact-us/ type title description cards bento-cards What you can build with Qdrant From product discovery to fraud detection, e-commerce teams combine Qdrant's retrieval primitives to solve problems generic search engines can't. id icon title description chips 0 src alt /icons/outline/search-blue.svg Search Product Search & Discovery "Blue T-shirt with yellow buttons" returns relevant results instead of zero matches. Dense vector similarity understands product intent. id title link 0 Hybrid Search /documentation/search/hybrid-queries/ id title link 1 Payload Filters /documentation/search/filtering/ id title link 2 RRF Fusion /documentation/search/hybrid-queries/#reciprocal-rank-fusion-rrf id icon title description chips 1 src alt /icons/outline/file-text-blue.svg File text Personalized Recommendation Item-to-item similarity vectors surface "You might also like" recommendations with fast latency. id title link 0 Recommendations API /documentation/search/explore/#recommendation-api id title link 1 Filtering /documentation/search/filtering/ id icon title description chips 2 src alt /icons/outline/heart-handshake.svg Handshake Inventory & Catalog Intelligence Find similar/duplicate listings across millions of SKUs using image and text embeddings. Flipkart uses this for fraud detection. id title link 0 Recommendation API /documentation/search/explore/#recommendation-api id title link 0 Discovery API /documentation/search/explore/#discovery-api type title description sections architecture Common E-commerce Patterns Architecture patterns with API examples for e-commerce search, recommendations, and multi-tenancy. id title description link steps language code 0 Hybrid Product Search Pipeline Combine semantic understanding, keyword matching, and business rules in a single query. href text /articles/sparse-embeddings-ecommerce-part-1/ View Full Example id title description 0 Dense Vectors (e.g. OpenAI, Cohere) for semantic product understanding id title description 1 Sparse Vectors (BM25/SPLADE) for exact keyword matching Python # 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 title description link steps language code 1 Multitenant Marketplace Architecture Isolate each seller's catalog within a single shared cluster. href text /documentation/manage-data/multitenancy/ View Full Example id title description 0 Per-tenant HNSW indexes Via payload indexing id title description 1 Tenant Promotion Move large tenants to dedicated shards id title description 2 Custom shard keys For geo or time partitioning Python # 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 title description link steps language code 2 Real-Time Recommendations Engine 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. href text /documentation/search/explore/?q=recommendation#recommendation-api View Full Example id title description 0 Item-to-Item Similarity via dense vectors id title description 1 Business rules (Margin, inventory) via metadata filters id title description 2 Recommend API For behavioral matching Python # 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], ) type title logos logos Powering E-Commerce Applications For id icon 0 src alt /img/e-commerce/flipkart-light.svg Flipkart logo id icon 1 src alt /img/e-commerce/alhena-light.svg Alhena logo id icon 2 src alt /img/e-commerce/meesho-light.svg Meesho logo id icon 3 src alt /img/e-commerce/convo-search-light.svg ConvoSearch logo id icon 5 src alt /img/e-commerce/bazaarvoice-light.svg Bazaarvoice logo type testimonials testimonials id reverse review author metric logo 0 true “Qdrant transformed our recommendation engine capabilities, making us indispensable to our clients.” name role avatar Shardul Aggarwal CEO, ConvoSearch src alt /img/e-commerce/customer3.svg Shardul Aggarwal avatar id title description 0 50%+ Latency Improvement from 100ms to 10ms id title description 1 60% Increase revenue for Convosearch clients src alt /img/e-commerce/convo-search.svg ConvoSearch logo type title questions faq FAQs id question answer 0 Can Qdrant Handle Our Traffic Spikes During Sales Events? 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 question answer 1 What Deployment Options Work for Multi-Region E-Commerce? 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 question answer 2 How Does Multi-Tenancy Work for Marketplace Platforms? 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 question answer 3 Can We Combine Image and Text Search in One Query? 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 question answer 4 How Does Qdrant Compare to Legacy search engines for E-Commerce Search? 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. type title description button cta-banner Talk to an expert about <br><span>E-commerce</span> retrieval. Let’s discuss your catalog size, traffic patterns, and current stack. text url Talk to an Expert /contact-us/	render always	build list publishResources render local false never