Zilliz Cloud Description
Searching and analyzing structured data is easy; however, over 80% of generated data is unstructured, requiring a different approach. Machine learning converts unstructured data into high-dimensional vectors of numerical values, which makes it possible to find patterns or relationships within that data type. Unfortunately, traditional databases were never meant to store vectors or embeddings and can not meet unstructured data's scalability and performance requirements.
Zilliz Cloud is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more.
Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.
Zilliz Cloud Alternatives
Milvus
A vector database designed for scalable similarity searches. Open-source, highly scalable and lightning fast. Massive embedding vectors created by deep neural networks or other machine learning (ML), can be stored, indexed, and managed. Milvus vector database makes it easy to create large-scale similarity search services in under a minute. For a variety languages, there are simple and intuitive SDKs. Milvus is highly efficient on hardware and offers advanced indexing algorithms that provide a 10x speed boost in retrieval speed. Milvus vector database is used in a variety a use cases by more than a thousand enterprises. Milvus is extremely resilient and reliable due to its isolation of individual components. Milvus' distributed and high-throughput nature makes it an ideal choice for large-scale vector data. Milvus vector database uses a systemic approach for cloud-nativity that separates compute and storage.
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Pinecone
The AI Knowledge Platform.
The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems.
Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval.
Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters.
Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.
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Chroma
Chroma is an AI-native, open-source embedding system. Chroma provides all the tools needed to embeddings. Chroma is creating the database that learns. You can pick up an issue, create PRs, or join our Discord to let the community know your ideas.
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MyScale
MyScale is a cutting-edge AI database that combines vector search with SQL analytics, offering a seamless, fully managed, and high-performance solution.
Key features of MyScale include:
- Enhanced data capacity and performance: Each standard MyScale pod supports 5 million 768-dimensional data points with exceptional accuracy, delivering over 150 QPS.
- Swift data ingestion: Ingest up to 5 million data points in under 30 minutes, minimizing wait times and enabling faster serving of your vector data.
- Flexible index support: MyScale allows you to create multiple tables, each with its own unique vector indexes, empowering you to efficiently manage heterogeneous vector data within a single MyScale cluster.
- Seamless data import and backup: Effortlessly import and export data from and to S3 or other compatible storage systems, ensuring smooth data management and backup processes.
With MyScale, you can harness the power of advanced AI database capabilities for efficient and effective data analysis.
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Pricing
Pricing Starts At:
$0
Pricing Information:
Zilliz Cloud on AWS
Compute Unit (CU) - $0.259 / hour
Storage - $0.025 / GB per month
Zilliz Cloud on Google Cloud
Compute Unit (CU) - $0.215 / hour
Storage - $0.02 / GB per month
Compute Unit (CU) - $0.259 / hour
Storage - $0.025 / GB per month
Zilliz Cloud on Google Cloud
Compute Unit (CU) - $0.215 / hour
Storage - $0.02 / GB per month
Free Trial:
Yes
Company Details
Company:
Zilliz
Year Founded:
2017
Headquarters:
United States
Website:
zilliz.com
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Product Details
Platforms
SaaS
Type of Training
Documentation
Webinars
In Person
Videos
Customer Support
Phone Support
24/7 Live Support
Online
Zilliz Cloud Features and Options
Database Software
Backup and Recovery
Creation / Development
Data Migration
Data Replication
Data Search
Data Security
Database Conversion
Mobile Access
Monitoring
NOSQL
Performance Analysis
Queries
Relational Interface
Virtualization
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