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 * Products
   
   Zilliz Cloud
   
   Fully-managed vector database service designed for speed, scale and high
   performance.
   
   Zilliz Cloud vs. Milvus
   
   Milvus
   
   Open-source vector database built for billion-scale vector similarity search.
   
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   By Use CaseRetrieval Augmented GenerationView all use cases
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THE MOST WIDELY-ADOPTED VECTOR DATABASE.


TRUSTED BY OVER 5000 ENTERPRISES ACROSS THE GLOBE. FROM THE CREATORS OF MILVUS,
THE VECTOR DATABASE TRAILBLAZER.

Get Started Free

2 collections + 1 million vectors + multiple collaborators for free.

No credit card required.

Why Zilliz Cloud is the Top Choice for Enterprise-Level Vector Search.






UNLEASH THE POWER OF MILVUS ADVANCED VECTOR SEARCH WITH ZILLIZ CLOUD.


WHY MILVUS

Milvus is the most popular and fastest-growing open-source vector database, with
over 24000 stars on GitHub and 8 million+ downloads. Milvus supports
billion-scale vector search with ease, and is trusted by over 5,000 enterprise
users.

CompareGet Started Free


WHY ZILLIZ CLOUD

Zilliz Cloud provides a fully-managed Milvus service, made by the creators of
Milvus. It simplifies the deploying and scaling of vector search applications by
eliminating the need to construct and maintain complex infrastructure.

Get Started FreeLearn more


BUILD WITH CONFIDENCE AND SUPERCHARGE YOUR
AI APPLICATIONS.

Sign up for a free Zilliz Cloud account and grab one of our official SDKs to
start. Then, create your first collection and conduct a vector similarity search
to power your AI application in minutes, and when you are ready to launch your
app, upgrade to a pay-as-you-go plan.

Get Started Free
Python
Java
Go
Node.js

*This code is for preview purposes onlyimport numpy as np
from pymilvus import MilvusClient, DataType

dimension = 128
collection_name = "hello_milvus"
client = MilvusClient("https://in01-XXXXXXXXXXXX.aws-us-west-2.vectordb.zillizcloud.com:19540")
client.drop_collection(collection_name)

schema = client.create_schema(auto_id=True)
schema.add_field("id", DataType.INT64, is_primary=True)
schema.add_field("vector", DataType.FLOAT_VECTOR, dim=dimension)

index_param = client.prepare_index_params("vector", metric_type="L2")
client.create_collection_with_schema(collection_name, schema, index_param)

info = client.describe_collection(collection_name)
print(f"{collection_name}'s info:{info}")

rng = np.random.default_rng(seed=19530)
rows = [
    {"title": "The Catcher in the Rye", "vector": rng.random((1, dimension))[0]},
    {"title": "Lord of the Flies", "vector": rng.random((1, dimension))[0]},
    {"title": "The Hobbit", "vector": rng.random((1, dimension))[0]},
    {"title": "The Outsiders", "vector": rng.random((1, dimension))[0]},
    {"title": "The Old Man and the Sea", "vector": rng.random((1, dimension))[0]},
]

client.insert(collection_name, rows)
client.insert(collection_name,
        {"title": "The Great Gatsby", "vector": rng.random((1, dimension))[0]})

search_vec = rng.random((1, dimension))
result = client.search(collection_name, search_vec, limit=3, output_fields=["title"])
  print(result)
View Full Code


WORKS SEAMLESSLY WITH YOUR TECH STACK.

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Get Started Free


HIGH PERFORMANCE AND RELIABILITY AT ANY SCALE.


 * EASY TO USE
   
   No experience required. You can easily establish a large-scale vector
   similarity search service in minutes. Stay out of Ops and focus on your
   business logic.


 * MILVUS EXPERTISE
   
   Zilliz Cloud is built by the creators of Milvus. We have deep experience
   across diverse Milvus use cases, providing state-of-the-art vector database
   services and solutions.


 * BLAZING FAST
   
   Our ultra-fast and hardware efficient Zilliz Cloud enables a 10x faster
   vector retrieval, unparalleled by any other vector database management
   system.


 * HIGHLY SCALABLE
   
   The distributed and high-throughput nature makes Zilliz Cloud a natural fit
   for serving large scale vector data. You can scale the cluster up to 500 CU,
   serving tens of billions of items.


 * HIGH AVAILABILITY
   
   With an aim to offer unparalleled customer experience and industry-leading
   SLAs, Zilliz offers 99.9% monthly uptime for all the products on our cloud.


 * SECURITY & GOVERNANCE
   
   Data security is always our first priority. Zilliz Cloud complies with the
   SOC 2 standard and will support Role-Based Access Control (RBAC) soon.


START BUILDING BETTER VECTOR SEARCH APPLICATIONS TODAY

Deploy your vector database on AWS, GCP or Azure and start using it in just a
few minutes.

Get Started Free



WHERE CAN VECTOR DATABASES HELP?


 * RETRIEVAL AUGMENTED GENERATION (RAG)
   
   Expand LLMs' knowledge by incorporating external data sources into LLMs and
   your AI applications.


 * RECOMMENDER SYSTEM
   
   Recommend information or products to users based on their past behaviors and
   preferences.


 * TEXT/SEMANTIC SEARCH
   
   Search for semantically similar texts across vast amounts of natural language
   documents.


 * IMAGE SIMILARITY SEARCH
   
   Search for visually similar images from a vast collection of image libraries.


 * AUDIO SIMILARITY SEARCH
   
   Find similar audio results from massive amounts of audio data such as music,
   sound effects, and speeches.


 * VIDEO SIMILARITY SEARCH
   
   Search for similar videos from extensive collections of video libraries.


 * QUESTION ANSWERING SYSTEM
   
   Interactive QA chatbot that automatically answers user questions.


 * MOLECULAR SIMILARITY SEARCH
   
   Search for similar substructures, superstructures, and other structures for a
   specified molecule.


 * MULTIMODAL SIMILARITY SEARCH
   
   Query across different modalities such as texts, videos, audio, and images. 

View all use cases


WANT TO LEARN MORE ABOUT VECTOR DATABASES?

The ever-increasing amount of unstructured data requires a paradigm shift and a
new category of database management system - the vector database. But what is a
vector database exactly? Find out in this comprehensive guide.

Learn more

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