Real-world AI, Built with Zilliz
See how teams use Zilliz and Milvus to ship RAG, search, recommendations, and AI agents in production. Find the story that matches your AI stack
Exa builds entity search engine for AI agents
- Business & Productivity AI
- Semantic Search
- From Pinecone
- <200ms search latency
- Exa’s neural search latency reduced from seconds to under 200ms with Zilliz's hybrid search
- High reliability
- Near-zero operational incidents, freeing engineering time for product work
- Zero Downtime for schema changes
- New filterable fields and metadata can be added without rebuilding indexes or taking collections offline
OpenEvidence powers medical AI
- Medical AI
- From Pinecone
- From Elasticsearch
- < 10ms
- Search Latency With High Recall
- 4,000 QPS
- Vector Search Traffic At Peak Load
- 800,000+
- Verified Clinicians Served with Quality Answers
- 25M+
- Consultations Every Month
Filevine: Transforming legal case management with vector search
- Legal / Financial / Edu AI
- RAG
- Semantic Search
- From Milvus
- From Qdrant
- 30 billion vectors
- Managed across millions of documents
- Semantic Search Architecture
- Connecting concepts across documents even when exact terminology differs.
- 60-80%
- Time saved on consuming, digesting, and identifying relevant data points
- Enterprise-Grade Security
- Zilliz's SOC 2 Type 2 certification provided the confidence necessary for handling sensitive legal information.
MiniMax scales real-time AI and trillion-scale deduplication
- AI Models / Robotics
- 30ms latency at 5,000+ QPS
- for real-time recommendations
- 3–5× cost reduction
- in training data deduplication workflows
- 2× faster in LLM data preprocessing
- compared to legacy MapReduce systems
- Petabyte-scale data deduplication
- using native MinHash + LSH engine
Reddit scales ANN search
- Media / Entertainment / AdTech
- Semantic Search
- ~340M
- Post vectors in Milvus 2.4 benchmark
- 100 QPS
- Query load tested during ingestion
- Ingest isolation
- Less query impact during ingest
- Auto rebalance
- When adding replicas
Consensus builds agentic literature search across 400M+ scholarly sources
- Business & Productivity AI
- 400M+
- Scholarly sources searchable
- ~45 ms
- P99 dense retrieval latency in production
- 14% higher
- precision in search results after adding semantic search
- 4× larger
- vectors for just 2.5× the cluster size, with no latency penalty
Airtable built and scaled vector infrastructure
- Business & Productivity AI
- Semantic Search
- Low-latency queries
- predictable performance is critical for user trust
- High-throughput writes
- bases change constantly, and embeddings must stay in sync
- Horizontal scalability
- the system must support millions of independent bases
Plaud powers billion-scale agent memory
- Business & Productivity AI
- RAG
- < 200 ms
- Average recall latency
- 10B+
- Vector chunks per region, with room to grow
- In days
- Billion-scale historical data backfill, no service interruption
- Zero downtime
- Schema changes and cluster upgrades on an always-on app
Meituan built a 10B-scale retrieval platform to power 100+ use cases
- E-commerce / Retail AI
- 10B+ vectors
- In production, self-run on Milvus
- 2B+ in one collection
- The largest single workload, grown without re-architecting
- P99 8.1 ms
- ~1,000 QPS and >97% recall on a 100M-vector, 768-dim benchmark
- 100+ use cases
- On one shared Milvus platform: RAG, agent memory, and more
A world-leading music platform searches 100M+ songs by sound and lyrics
- Media / Entertainment / AdTech
- Multimodal Search
- From Elasticsearch
- 100M+ tracks
- The entire catalog embedded and searchable, including the long tail
- ~10 ms P99
- A match across billions of audio embeddings, at hundreds of searches a second
- By sound and meaning
- Find a song from a hum, a clip, or a paraphrased lyric, not just its title
Bosch achieves 80% cost reduction and optimized search efficiency
- Energy / Industrial AI
- 80%
- Reduction in Data Collection Costs
- ~$1.4M
- Reduction in Annual Storage Costs
- Millisecond-level
- Retrieval for Billions of Data Points with a Scalable Architecture
Trip.com Group makes hotel search seamless for travelers worldwide
- Media / Entertainment / AdTech
- Smarter, Intent-Driven Search
- Delivers accurate, multilingual hotel results that understand traveler intent—not just keywords.
- Faster Innovation, Lower Overhead
- Eliminates cluster management and scaling tasks, freeing teams to focus on innovation.
- <100 ms Latency Optimization
- Delivers lightning-fast, real-time hotel search with P99.9 response times under 100 ms.
- Seamless Global Scalability
- Powers real-time search across hundreds of millions of listings with enterprise reliability and low latency.
“Zilliz gave us real-time retrieval for our AI search system at scale with tight latency targets. It freed up engineering cycles and let us focus on improving reasoning on the model side, not managing infrastructure.”
“Zilliz Cloud has helped us create a strong foundation behind the scenes as we continue to grow and serve hundreds of thousands of clinicians.”
“Zilliz helps our teams deliver fast, reliable retrieval across complex legal workflows, so customers can find the context they need when it matters.”
“Milvus and Zilliz make it practical to power fast semantic retrieval across meeting intelligence workflows.”
“Zilliz supports high-scale search experiences where latency and reliability are critical.”
“Zilliz gives product teams a strong retrieval foundation for building AI-native workflows on structured and unstructured data.”
“Zilliz helps us match people with the right opportunities through fast, relevant AI search.”