Real-world AI, Built with Zilliz
Entity search for agents
- Productivity
- Semantic Search
- From Pinecone
- <200ms
- Search latency
- 0
- Operational incidents
- 0
- Downtime for schema changes
Medical AI search
- Healthcare
- From Pinecone
- From Elasticsearch
- <10ms
- High-recall search
- 4K QPS
- Peak query load
- 800K+
- Verified clinicians
- 25M+
- Monthly consultations
Legal case management with vector search
- Legal
- RAG
- From Milvus
- From Qdrant
- 30B
- Vectors managed
- Semantic
- Cross-document search
- ↓ 60–80%
- Review time saved
- Security
- Protection for sensitive legal data
Real-time AI and trillion-scale deduplication
- AI Models
- 30ms
- 5K+ QPS recommendations
- ↓ 3–5×
- Dedup cost reduction
- ↑ 2×
- Faster LLM preprocessing
- PB
- Training-data dedup scale
Choosing a vector database for ANN search at Reddit
- Media
- Semantic Search
- ~340M
- Milvus 2.4 test vectors
- 100 QPS
- Query load during ingest
- Ingest isolation
- Less query impact during ingest
- Auto rebalance
- When adding replicas
Agentic literature search
- Productivity
- 400M+
- Scholarly sources
- ~45ms
- P99 dense retrieval
- ↑ 14%
- Search precision gain
- ↑ 4×
- Vector capacity; 2.5× cluster
Vector infrastructure at scale
- Productivity
- Semantic Search
- Low latency
- Trusted search
- Fast writes
- Embeddings in sync
- Scale out
- Millions of bases
Billion-scale agent memory
- Productivity
- RAG
- <200ms
- Avg recall latency
- 10B+
- Vectors per region
- Days
- Billion-scale backfill
- 0
- Schema & upgrade downtime
10B-scale retrieval platform for 100+ use cases
- Ecommerce
- 10B+
- Production vectors
- 2B+
- Vectors per collection
- 8.1ms
- P99; 100M × 768D; ~1K QPS
- 100+
- Use cases on one platform
Search across 100M+ songs by sound and lyrics
- Media
- Multimodal Search
- From Elasticsearch
- 100M+
- Tracks searched
- ~10ms
- P99 audio search
- Multimodal
- Hum, clip or lyric search
Efficient search at lower cost
- Energy
- ↓ 80%
- Cost reduction
- ↓ ~$1.4M
- Annual storage savings
- Millisecs
- Billions of data points
Hotel search for travelers worldwide
- Ecommerce
- Intent-led
- Multilingual hotel search
- Less ops
- No cluster management
- <100ms
- P99.9 hotel search
- Global scale
- Millions of hotel listings
“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.”