Accelerate life sciences AI from molecular search to clinical discovery
Zilliz Cloud is a fully managed vector database for similarity search across proteins, molecules, genomic sequences, and biomedical literature â at scales from millions to tens of billions. Life sciences teams use it to move from slow sequence alignment to millisecond vector search, build RAG-powered research assistants, and unify multi-modal biological data in one system. Trusted by Biomap (50B+ protein sequences in production). SOC 2 Type II certified, HIPAA-ready.
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AI Capabilities Powering the Next Generation of Life Sciences Discovery
From protein structure search to drug-target prediction, life sciences generates some of the most complex, high-dimensional data on earth. Zilliz Cloud gives research and engineering teams the infrastructure to search, match, and discover across billions of biological data points â in milliseconds, not minutes.
Protein and Molecular Similarity Search at Billion Scale
Find structurally and functionally similar proteins or compounds across billions of sequences in milliseconds â replacing BLAST and MSA workflows that take minutes per query. Zilliz Cloud powers embedding-based similarity search that catches distant homologs traditional alignment misses â the same infrastructure Biomap uses to search 50 billion sequences.
AI Research Assistants for Biomedical Literature
Search 40M+ PubMed articles and internal research by meaning, not just keywords. RAG-powered assistants retrieve semantically relevant papers, patents, and protocols â then generate synthesized answers with citations. Find papers about 'genome modification in cardiac tissue' when you search for 'CRISPR heart.'
Multi-Modal Biological Data Integration and Search
Unify proteins, molecules, microscopy images, clinical notes, and genomic data in a single searchable vector space. Break down the data silos that force researchers to manually cross-reference UniProt, ChEMBL, PubMed, and patent databases â enabling cross-modal discovery that was previously impossible.
Accelerate Drug-Target Interaction Screening
Embed compound libraries and protein targets into shared vector space for cross-modal matching. Screen billions of candidate molecules against targets in minutes â compared to hours per compound with molecular docking. Identify non-obvious drug-target interactions and repurposing opportunities that fingerprint-based methods miss.
Content-Based Pathology and Microscopy Image Retrieval
Query tissue samples, cell images, and microscopy data by visual content â not metadata tags. Retrieve the most similar cases from your archive with their diagnoses in seconds, enabling rapid rare disease identification and comparative analysis across institutions.
Genomic Sequence Classification and Comparison
Classify and compare DNA and RNA sequences using embedding-based nearest-neighbor search â outperforming traditional ML classifiers in both speed and accuracy. Enable cross-species comparison and variant analysis at scales that alignment-based methods cannot support interactively.
Why Zilliz?
Why leading life sciences teams choose Zilliz Cloud
Life sciences AI operates under constraints most industries never face: datasets that span billions of protein sequences and millions of compounds, searches that must find functionally similar results â not just exact matches â across high-dimensional embedding spaces, and regulatory requirements (GxP, HIPAA, 21 CFR Part 11) that rule out most cloud infrastructure. On top of that, biological data is inherently multi-modal â proteins, molecules, images, clinical text, genomic sequences â and most platforms force you to search each modality separately. Zilliz Cloud was built to handle all of these simultaneously, which is why Biomap runs 50 billion protein sequence searches on it in production â with 22x faster queries than their previous MSA-based approach and sub-second response times for complex biological queries.
100K+QPS
Power concurrent searches across research teams and pipelines
Life sciences workflows run parallel: virtual screening campaigns, literature search agents, image retrieval systems, and interactive researcher queries â all hitting the same database simultaneously. Zilliz Cloud handles 100K+ queries per second so that batch screening pipelines don't slow down interactive research and discovery.
<10msLatency
Replace minutes-long BLAST searches with millisecond retrieval
Traditional sequence alignment takes 10-20 minutes per query at scale. Zilliz Cloud returns similarity results in under 10 milliseconds â turning protein and molecular search from a batch job you run overnight into an interactive tool you use hundreds of times a day. Biomap achieved 22x speedup with this approach.
10B+vectors
Search billions of sequences, compounds, and structures in one index
UniProt has 250M+ proteins. PubChem has 100M+ compounds. AlphaFold predicted 200M+ structures. Zilliz Cloud supports tens of billions of vectors â enough for your entire biological data estate in a single, searchable index without sharding or re-architecture.
-10xCost
Cut infrastructure costs without sacrificing search performance
Standalone similarity search libraries, oversized keyword search clusters, and expensive pharma software suites drain budgets without delivering production-grade vector search. Zilliz Cloud's optimized indexing and auto-scaling reduce infrastructure costs by up to 10x â purpose-built for high-dimensional biological embeddings.
Multimodal similarity search
Search across proteins, molecules, images, and text in a unified vector space. Life sciences data is inherently multi-modal â Zilliz Cloud lets you query across modalities without separate systems for each data type.
Multi-tenant architecture
Isolate research teams, projects, or therapeutic areas within a single deployment. Fine-grained access controls ensure data separation between programs while sharing infrastructure efficiently.
Automatic and elastic scaling
Scale from prototype to production without re-architecture. Handle virtual screening campaigns that spike to billions of queries, then scale back down â paying only for what you use.
Hybrid search with metadata filtering
Combine vector similarity with structured filters â search for similar proteins filtered by organism, search compounds filtered by assay type, or retrieve literature filtered by publication date. Critical for life sciences workflows where context matters.
Multi-cloud and on-premises availability
Deploy on AWS, GCP, or Azure. For pharma companies with GxP and data sovereignty requirements, BYOC and on-premises options keep data within your controlled environment.
Enterprise-grade compliance and reliability
SOC 2 Type II certified, HIPAA-ready with BAA support, 99.95% SLA. Designed for regulated industries where audit trails, access controls, and data residency are non-negotiable requirements.
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Resources
Essential reading for life sciences AI teams
Explore how leading biotech and research organizations use vector search to accelerate discovery â from molecular similarity to multi-modal data integration.