Power media & entertainment AI from content discovery to rights management
Zilliz Cloud is a fully managed vector database that powers content recommendation, video similarity search, and audio matching for media platforms â with sub-200ms latency at billion-asset scale. Media teams use it to personalize content feeds, detect duplicate videos, and identify copyrighted audio across catalogs of hundreds of millions of assets. Proven in production at Orfium (250M+ vectors for music rights matching), BIGO/Likee (700M+ vectors, <200ms video deduplication), and Shopee Video. SOC 2 Type II certified.
AI Capabilities for the Next Generation of Media & Entertainment
Every major media challenge is fundamentally a similarity problem: find content a viewer will enjoy, find videos that duplicate protected material, find audio that matches a copyrighted track, find scenes that violate content policies. Zilliz Cloud gives media platforms the infrastructure to solve these with true semantic similarity â at interactive latency and billion-asset scale.
Recommend Content by Meaning, Not Just Watch History
Encode viewer behavior and content attributes into embeddings and find semantically similar items via nearest-neighbor search â surfacing content that matches taste profiles rather than relying solely on collaborative filtering. Move from shallow popularity-driven feeds to deep personalization that captures nuance in genre, mood, narrative style, and visual aesthetics across millions of catalog items.
Find Visually Similar Videos Across Massive Libraries
Extract frame-level embeddings from video content and search for visually or semantically similar clips across your entire catalog. Power scene-level search for editors, enable 'more like this' discovery for viewers, and identify re-uploads or derivative content â all from a single vector index that handles hundreds of millions of video assets with sub-second response times.
Identify Music and Audio Across Every Platform
Convert audio tracks into spectral embeddings that capture tonal, rhythmic, and harmonic fingerprints. Match exact recordings, cover versions, and remixes across platforms like YouTube, TikTok, and broadcast radio â ensuring rights holders receive accurate attribution and royalty payments even when audio is altered, compressed, or layered over other content.
Detect Duplicate and Near-Duplicate Content at Upload Scale
Embed newly uploaded content and search against your existing library to catch exact copies, re-edits, cropped versions, and re-encoded variants before they reach viewers. Process millions of daily uploads in real time â the same architecture BIGO uses to deduplicate video across 700M+ vectors on Likee, completing each search in under 200 milliseconds.
Protect Intellectual Property Without Blocking Legitimate Content
Build content matching systems that compare uploads against libraries of protected material at the frame, audio, and text level. Detect partial copies, re-edits, and derivative works using semantic similarity rather than exact hash matching â catching infringement that perceptual hashing misses while letting transformative and fair-use content through with configurable similarity thresholds.
Classify Unsafe Content by Meaning, Not Keywords
Embed visual and textual content semantically and measure distance from policy-violating concepts rather than scanning for flagged keywords or pixel patterns. Catch harmful content that evades keyword filters and perceptual hashes â including AI-generated material, context-dependent violations, and novel forms of abuse â while dramatically reducing false positives on legitimate creative content.
Why Zilliz?
Why media platforms choose Zilliz Cloud
Media and entertainment platforms face three infrastructure challenges that most databases cannot handle simultaneously: sub-second latency for interactive content discovery, scale to index billions of video frames, audio segments, and user profiles, and real-time updates as new content is uploaded and viewer behavior evolves continuously. On top of that, the explosion of short-form video, AI-generated content, and cross-platform distribution has made duplicate detection and rights management exponentially harder â keyword matching and perceptual hashing can no longer keep pace. Orfium runs their music rights matching on Zilliz Cloud with 250M+ vectors, replacing Elasticsearch infrastructure that could not scale. BIGO processes video deduplication across 700M+ vectors on Milvus with <200ms search latency on their Likee platform.
<200msSearch Latency
Return similarity results fast enough for interactive content experiences
Viewers expect instant recommendations. Editors need real-time search across media libraries. Rights systems must match content as it is uploaded. Zilliz Cloud delivers sub-200ms vector search â proven in BIGO's production deduplication system processing millions of daily video uploads across 700M+ vectors.
1B+Vectors
Index billions of frames, audio segments, and user profiles
A single hour of video generates thousands of frame embeddings. A music catalog of millions of tracks produces hundreds of millions of audio fingerprint vectors. Zilliz Cloud supports tens of billions of vectors in a single index â your entire content library, user base, and rights catalog searchable together.
100K+QPS
Handle recommendation and search queries at streaming-platform throughput
Peak streaming hours generate massive concurrent recommendation requests. Content upload pipelines run deduplication checks on every new asset. Zilliz Cloud handles 100K+ queries per second so that personalization, moderation, and rights matching all operate within your latency budget without contention.
-10xCost
Replace fragmented point solutions with unified vector infrastructure
Media companies typically run separate systems for recommendations, content matching, moderation, and rights detection â each with its own scaling costs. Zilliz Cloud provides the semantic matching layer for all of these workloads in a single managed service, at a fraction of the cost of stitching together proprietary tools.
Multimodal similarity search
Match across video frames, audio fingerprints, text metadata, and thumbnails in a single query. CLIP-style cross-modal search enables finding content by description, matching audio to video, and powering rich search experiences that span media types.
Hybrid search with metadata filtering
Combine semantic vector similarity with structured filters â search for similar videos filtered by language, region, and content rating, or match audio filtered by genre and release date. One query, both signals.
Real-time index updates
New content is uploaded every second. Zilliz Cloud supports hundreds of millions of daily vector updates without performance degradation â keeping recommendation models, content indexes, and rights databases current, not stale.
Automatic and elastic scaling
Media traffic spikes are routine â premieres, viral moments, live events, seasonal peaks. Scale compute up for high-traffic periods and back down automatically, paying only for what you use.
Multi-tenant architecture
Serve multiple content catalogs, studios, publishers, or regions from a single deployment. Fine-grained isolation ensures one tenant's content, models, and user data never leak to another.
Enterprise-grade compliance and reliability
SOC 2 Type II certified, GDPR compliant, 99.95% SLA. BYOC deployment available for media companies with strict data residency and content licensing requirements under GDPR, CCPA, COPPA, and regional broadcast regulations.
Trusted by AI Builders
Learn how industry leaders and startups build AI applications using Zilliz Cloud/Milvus Vector Database
Contact Sales
Build AI Applications with your Favoriate Tools
Resources
Essential reading for media & entertainment AI teams
Explore how media platforms use vector search for content recommendation, video deduplication, and audio matching â with production case studies and technical architecture guides.