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Milvus, the world most popular open-source vector database (42k+ GitHub stars), now offers an official fully managed service: Zilliz Cloud, built by the original Milvus team. Purpose-built for GenAI embedding workloads and trusted by 10,000+ organizations, Zilliz delivers hybrid search and sub-10 ms latency at billion-vector scale. Enjoy a monthly Free Tier (5GB storage, 2.5M vCUs) or try Serverless/Dedicated free for 30 days, cancel anytime.
4.7
Overview
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Zilliz Cloud architecture
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Milvus now offers an official fully managed cloud service: Zilliz Cloud, built by the original Milvus team, for vector search, semantic search, RAG, and GenAI applications. It provides high-performance vector storage and retrieval with hybrid search (vector + metadata filtering), delivering sub-10 ms latency at billion-vector scale for demanding, production environments. Learn more at: https://zilliz.com/?utm_source=awsmp&utm_medium=webpage
Zilliz Cloud is designed for teams that need to move beyond DIY deployments. With elastic, cloud-native scaling, high throughput, and high performance, users don't need to worry about operational overhead of managing infrastructure, making it easier to run Milvus reliably in production.
Built for real GenAI applications across modern AI stacks, Zilliz Cloud integrates seamlessly with popular frameworks like LangChain, LlamaIndex, and Haystack to power production workflows such as RAG pipelines, semantic search, and knowledge retrieval. It runs natively on major cloud platforms including AWS, GCP, and Azure.
Zilliz Cloud supports production use cases including reverse image and video search, recommendation systems, enterprise semantic search, agentic workflows, and anomaly detection across industries like legal tech, e-commerce, marketplaces, and SaaS. In real deployments, customers run multi-billion-vector indexes with sub-200ms visual search performance, achieve high relevance in text and image queries, and scale conversational AI and agentic services with low latency and cost efficiency.
Zilliz Cloud lowers the total cost of ownership for running Milvus at scale, combining high performance with elastic, pay-as-you-go pricing. It is available in Free, Serverless (PAYG), Dedicated Cluster (PAYG or contract), and BYOC plans. Get started with the monthly Free Tier (5 GB storage, 2.5 M vCUs, up to 5 collections), or try Serverless or Dedicated free for 30 days, cancel anytime. Full pricing details at https://zilliz.com/pricing?utm_source=awsmp&utm_medium=webpage
Highlights
- High Performance & Cost Efficiency: Up to 4x faster than open-source Milvus, with hot-warm-cold tiered storage and a cloud-native, index-optimized architecture that delivers a lower total cost than S3-based vector pipelines, with flexible pay-as-you-go pricing.
- Enterprise Scale & Trust: Built for true billion-scale production, featuring advanced multitenancy with 100K+ collections per cluster, elastic auto-scaling, a 99.95% SLA, global clusters for region-level resilience, and enterprise-grade security with BYOC deployment, SOC 2, ISO 27001, and GDPR compliance.
- Next-Gen AI Data Stack: Go beyond vector search with native hybrid vector, full-text, and metadata filtering, 4x faster full-text search than Elasticsearch, 10x faster filtering via JSON Shredding, integrated reranking, and seamless integration with next-generation multimodal data lakes and the big data ecosystem.
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Dimension | Cost/unit |
|---|---|
Zilliz Cloud Usage (Each unit is 0.1 cent of usage) | $0.001 |
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Zilliz Cloud does not currently offer refunds. Please refer to the Acceptable Use Policy for the refund policy. https://zilliz.com/acceptable-use-policy
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Have questions about purchasing Zilliz Cloud? Please review pricing details at https://zilliz.com/pricing or contact us at https://zilliz.com/contact-sales .
Zilliz support has been continuously praised by customers. Review the SLA at https://zilliz.com/sla for more details. For technical help, please contact support@zilliz.com .
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Updated weekly
By Weaviate
By Pinecone
Top
10
In Software Development
Top
10
In Embeddings
Top
10
In Embeddings, Generative AI, Databases
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Vector Search Performance
Sub-10 millisecond latency at billion-vector scale with support for multi-billion-vector indexes
Hybrid Search Capabilities
Native hybrid vector search combined with full-text search and metadata filtering, including JSON Shredding for 10x faster filtering
Enterprise Scalability
Advanced multitenancy supporting 100,000+ collections per cluster with elastic auto-scaling and 99.95% SLA
Cloud-Native Architecture
Hot-warm-cold tiered storage with index-optimized architecture running natively on AWS, GCP, and Azure
Framework Integration
Seamless integration with LangChain, LlamaIndex, and Haystack for RAG pipelines and semantic search workflows
Vector Similarity Search
End-to-end vector database supporting vector similarity search, hybrid search, and advanced filtered search capabilities.
Multimodal Data Support
Out-of-the-box support for multimodal media types including text, images, and other data formats.
Structured Filtering
Ability to seamlessly combine vector search with structured filtering for refined query results.
Cloud-Native Architecture
Fault-tolerant cloud-native database architecture with low-latency performance characteristics.
Multi-Language Client Support
Accessible through a variety of client-side programming languages for flexible integration.
Hybrid Search Capabilities
Combines semantic and keyword search with integrated reranking to deliver relevant results across different query types.
Low-Latency Vector Retrieval
Achieves 20-100ms search latency on billion-vector datasets with real-time indexing and purpose-built Rust engine architecture.
Scalable Infrastructure Options
Supports elastic On-Demand scaling for variable traffic and Dedicated Read Nodes for provisioned read capacity with 99.9% uptime SLA.
Security and Compliance Certifications
SOC 2 Type II and HIPAA certified with security enforced at the data layer for enterprise deployments.
AWS Ecosystem Integration
Deep integration with Amazon Bedrock, SageMaker, and 50+ popular AI frameworks and data platforms through a unified API.
Standard contract
No
No
Customer reviews
Neel Shah
Managed vector search has reduced latency and now accelerates CNN-based RAG workflows
Reviewed on May 18, 2026
Review from a verified AWS customer
What is our primary use case?
I have Milvus hosted on Zilliz Cloud and am majorly using it to manage the vector database and utilizing some of the RAG and vector features from that.
I connected Zilliz Cloud with a lot of Kubernetes clusters on Zilliz Cloud to fetch a lot of data because we have a client who wants to use CNN models to give the best result from their database. We have RAG, which is using vector embedding, and we manage everything on AWS , where some of the services are connected with Zilliz Cloud to gather everything faster.
What is most valuable?
The best feature of Zilliz Cloud is that it helps in very high-performance vector search, and it is also very scalable, with very low latency that helps provide faster results. The deployment of Milvus is very easy because it is managed there, so I did not need to take care of anything. These are the major things that I feel are very important.
Zilliz Cloud has positively impacted my organization because previously, when I was not using it, there was a little lag in the output of the search due to the lack of a proper vector search setup, and maintaining the vector search was very hard, requiring me to create a model, deploy it, and connect everything. It helped me a lot by using managed Milvus, which simplifies my management tasks.
What needs improvement?
Having more connections with all other major clouds could be helpful, and a marketplace could grow with Zilliz Cloud.
For how long have I used the solution?
I have been using Zilliz Cloud for around seven to eight months.
What do I think about the stability of the solution?
Zilliz Cloud is stable in my experience.
What do I think about the scalability of the solution?
Its scalability is very good.
How are customer service and support?
The customer support is also good.
Which solution did I use previously and why did I switch?
I have not used any different solutions before Zilliz Cloud.
What was our ROI?
The biggest return on investment I have seen is in the time saved in my current scenario.
What's my experience with pricing, setup cost, and licensing?
The pricing, setup cost, and licensing experience were pretty straightforward, and although I was not involved with the team, I felt it was smooth.
Which other solutions did I evaluate?
Before choosing Zilliz Cloud, I evaluated Weaviate and PineconeDB.
What other advice do I have?
If others do not have the bandwidth to manage the vector search and maintain that on the cloud, I recommend that they find it very easy to use Zilliz Cloud.
Zilliz Cloud is deployed in my organization on a public cloud.
I use AWS as my cloud provider.
I did not purchase Zilliz Cloud through the AWS Marketplace ; the company directly purchased it.
Zilliz Cloud helps a lot, and I also contribute to the community while creating a lot of awareness for people to use it. I would rate this review an overall eight out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Mihai B.
Exceptional Support and Scalability for Vector Similarity Search
Reviewed on Nov 15, 2025
Review provided by G2
What do you like best about the product?
Profesional technical support, great documentation and the most scalable and reliable solution for vector similarity search
What do you dislike about the product?
Nothing to dislike. Everything is going great so far
What problems is the product solving and how is that benefiting you?
Zilliz addresses the challenge of searching through embeddings created from social media creators' content on a large scale. It effectively manages the complexity involved in handling and retrieving relevant information from vast amounts of such data.
Computer Software
Stable Performance and Excellent Support from Zilliz
Reviewed on Nov 13, 2025
Review provided by G2
What do you like best about the product?
Zilliz has been pretty stable in the last year. The team was very helpful resolving issue in the initial integration period. We currently store about 50 million vectors.
What do you dislike about the product?
The cost can be somewhat high when we stored like 1 billion vectors.
What problems is the product solving and how is that benefiting you?
We use Zilliz as our RAG
Issa M.
Fast, Affordable, and Effortless to Use
Reviewed on Nov 13, 2025
Review provided by G2
What do you like best about the product?
This product is quick, affordable, straightforward, and gets the job done. It's user-friendly and can easily scale to meet growing needs.
What do you dislike about the product?
Modifying collection schemas involves a migration process, which can be quite complex and often takes a significant amount of time to manage.
What problems is the product solving and how is that benefiting you?
Zilliz serves as the primary knowledge base for our AI agent. It drives the RAG functionality for a customer support and shopping assistant AI agent that currently assists millions of e-commerce shoppers.
Computer Software
Fast and Capable Vector Database
Reviewed on Nov 09, 2025
Review provided by G2
What do you like best about the product?
Extremely fast, low-latency vector search, even at a massive scale. The cloud platform is intuitive, and the SDKs (like PyMilvus) are straightforward to integrate.
What do you dislike about the product?
The pricing for the cloud service can be a bit high for smaller projects or individual developers.
What problems is the product solving and how is that benefiting you?
We use Zilliz to power our recommendation system. It solves the critical problem of storing and searching millions of image embeddings at high speed. This benefits us by enabling real-time, accurate visual recommendations and 'similar item' search for our users, which significantly improves user engagement.