Build Image Similarity Search That Scales to Billions of Visuals
Deploy production-grade reverse image search and visual retrieval powered by vector embeddings. Zilliz Cloud indexes billions of image vectors with sub-10ms latency — so your users find what they see, instantly.
Image Similarity Search Powered by Zilliz Cloud
Build production-grade visual search applications that find, compare, and rank images at billion scale using Zilliz Cloud.
Visual Product Discovery
Enable shoppers to snap a photo and instantly find matching or similar products across your catalog. Convert visual intent into purchases by surfacing relevant results from millions of SKUs in milliseconds.
Style and Outfit Matching
Analyze uploaded clothing images to recommend visually similar items, complete outfits, and complementary accessories. Drive higher engagement by connecting users with styles they love across your inventory.
Trademark and Logo Detection
Scan incoming trademark applications against existing registries to flag visually similar logos. Reduce manual review time and protect intellectual property by automating image-based similarity checks at scale.
Visual Defect Inspection
Compare manufacturing images against reference datasets to detect surface defects, assembly errors, and quality anomalies. Accelerate quality assurance pipelines by matching visual patterns across millions of inspection records.
Duplicate and Near-Duplicate Detection
Identify duplicate, near-duplicate, and derivative images across large content libraries. Streamline content moderation, enforce licensing compliance, and reduce storage costs by eliminating redundant visual assets automatically.
Medical Image Retrieval
Search radiology scans, pathology slides, and clinical imagery by visual similarity to support diagnosis. Retrieve historically similar cases from massive imaging archives to assist clinicians with evidence-based decision making.
Cross-Modal Text-to-Image Search
Deploy CLIP-based embeddings to let users search images with natural language queries. Bridge the gap between text descriptions and visual content, enabling intuitive discovery across unstructured image collections.
Visual Identity Verification
Match face or document images against enrolled identity databases for authentication and fraud prevention. Process verification requests at scale with high-throughput vector matching that maintains sub-second response times.
Why Zilliz?
Why Visual Search Teams Choose Zilliz Cloud
Image similarity search at production scale demands more than a research prototype. Zilliz Cloud delivers the throughput, capacity, and precision that e-commerce, media, and enterprise teams need — searching billions of image embeddings with sub-10ms latency and native support for cosine, L2, and inner product distance metrics.
100K+QPS
Serve high-traffic visual search workloads without degradation
Visual search applications generate thousands of simultaneous similarity queries — from e-commerce product discovery to real-time content moderation. Zilliz Cloud sustains 100K+ queries per second on image embedding collections, keeping search-by-image experiences responsive under peak load.
10B+Vectors
Index entire image catalogs in one place
Product catalogs, media archives, and inspection datasets collectively contain billions of images. Zilliz Cloud indexes 10B+ image vectors in a single deployment without sharding complexity — so your team searches the full visual library, not a sampled subset.
-10xCost
Scale your image library without scaling your budget
Storing and querying billions of high-dimensional image embeddings gets expensive fast. Zilliz Cloud's vector compression and tiered storage reduce infrastructure costs by 10x compared to self-managed alternatives — making billion-scale visual search accessible to teams of any size.
< 10msLatency
Return similarity results fast enough for interactive workflows
Shoppers and users expect instant visual feedback when searching by image. Zilliz Cloud returns the nearest visual neighbors in under 10ms at the 99th percentile — fast enough to power interactive product discovery, reverse image search, and real-time visual applications without noticeable delay.
Hybrid search with dense and sparse vectors
Combine dense image embeddings with sparse text metadata in a single query. Retrieve visually similar products while filtering by brand, price, or category — no post-processing pipeline required.
Automatic and elastic scaling
Automatically scales compute and storage as your image library and query volume grow — with no capacity planning, index rebuilding, or sharding required during traffic spikes or seasonal peaks.
Native multi-tenant architecture
Built-in tenant isolation lets multiple brands, business units, or applications search separate image collections on the same platform — securely and without noisy-neighbor performance issues.
Ease of use
Go from prototype to production in hours. Ingest raw images with built-in embedding functions, manage collections through a visual console, and integrate via Python, Java, Go, or REST SDKs.
Multi-cloud flexibility
Run on AWS, Azure, or GCP in the region closest to your users. Deploy image search globally with consistent APIs and performance — no vendor lock-in, no architecture changes.
Enterprise-grade reliability and compliance
99.95% SLA with SOC 2, ISO 27001, GDPR, and HIPAA compliance — plus regional failover and BYOC support for regulated industries handling sensitive visual data.
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Resources
Deep dives into image similarity search
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