Cloud-native service for Milvus
Zilliz simplifies the process of deploying and scaling vector search applications by eliminating the need to create and maintain complex data infrastructure.
Get $400 with your 30-day trial
Powerful, flexible support for embeddings generated by multiple Machine Learning algorithms- Lightning-fast queries on any size data set
- Cost-effective storage of vectors
- Zero ops overhead with serverless architecture
Why Zilliz Cloud?
Built on Milvus and optimized for performance
Zilliz Cloud is built on the popular open-source vector database, Milvus. The same creators of Milvus have used their experience with over a thousand enterprise Milvus users across various industries to create state-of-the-art vector database services.
Elastic and Scalable
Cost-effectively scale with serverless clusters that instantly provision and scale to match your budget and needs. Scale your compute and storage resources as far as you need to support vector search to tens of billions of vectors.
Pay-as-you-go
Scale up or down when needed and pay only for what you use to lower the cost of storing your embeddings with the most budget-friendly vector database. New sign-ups receive $400 to spend during their first 30 days. Access scaled discounts for high-volume workloads with committed usage.
Multi-Cloud (AWS, GCP)
Zilliz Cloud offers consistent management, security, and governance experience across all clouds. You don’t need to invest in reinventing processes for every cloud platform you use to support your data and AI efforts. Instead, your teams can focus on putting all your data to work to build new similarity search capabilities.
Cloud Native Resiliency
Zilliz Cloud is always on the latest version, secure with the latest patches, and supported by a world-class operations team. Build on the Milvus strength with reliability & resiliency built-in, 99.99% uptime SLA, and zero data corruption.
Enterprise Security & Governance
Zilliz Cloud provides full data encryption in transit, complies with the SOC 2 standards, and will support Role-Based Access Control (RBAC) soon.
Key features
Vector database features
Where can vector database help?
Image similarity search
Images made searchable and instantaneously return the most similar images from a massive database.
Video similarity search
By converting key frames into vectors and then feeding the results into Milvus, billions of videos can be searched and recommended in near real time.
Audio similarity search
Quickly query massive volumes of audio data such as speech, music, sound effects, and surface similar sounds.
Molecular similarity search
Blazing fast similarity search, substructure search, or superstructure search for a specified molecule.
Text search engine
Help users find the information they are looking for by comparing keywords against a database of texts.
DNA sequence classification
Accurately sort out the classification of a gene in milliseconds by comparing similar DNA sequence.
Question answering system
Interactive digital QA chatbot that automatically answers user questions.
Recommender system
Recommend information or products based on user behaviors and needs.
Anomaly detection
Identifies data points, events, and/or observations that deviate from a dataset's normal behavior.
Frequently asked questions
A CU is a group of hardware resources for serving your indexes and search requests. You can simply consider a CU as a fully-managed physical node for deploying search service.
The "performance-optimized Compute Unit" option suits low latency or high throughput similarity searches. This option works best for high-search performance scenarios.
The "capacity-optimized Compute Unit" option suits data volumes that are five times larger than the performance-optimized CU option but at the cost of higher search latency. This option works best for increased storage capacity scenarios.
A performance-optimized CU can serve 5 million 128-dimensional vectors. A capacity-optimized CU can fit 25 million 128-dimensional vectors.
Since your collection's schema may differ from the ones in the simple guide above, we highly recommend you test the actual requirements against different CU types.
Yes. By default, the upper limit of resource usage in the free trial is 4 CUs per database. However, If you need to test a large dataset that requires more resources, please contact us at support@zilliz.com. We will try to help you complete your Proof of Concept (POC).
Please send your issues to us via support@zilliz.com. We will reply you within 24 hours.
After the free trial, your data will be automatically backed up and moved to the Recycle Bin, and the instance will be deleted. Data in the Recycle Bin will be retained for 30 days. You can upgrade to enterprise users by binding a credit card or your AWS Marketplace account. Within these 30 days, you can safely recover your data and rebuild your services. If you have not completed the PoC during the trial period, you can also contact us to extend the trial period.
After the free trial, your data will be automatically backed up and moved to the Recycle Bin, and the instance will be deleted. Data in the Recycle Bin will be retained for 30 days. You can upgrade to enterprise users by binding a credit card or your AWS Marketplace account. Within these 30 days, you can safely recover your data and rebuild your services. If you have not completed the PoC during the trial period, you can also contact us to extend the trial.