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Zilliz

If you're frustrated by managing complex infrastructure for large-scale vector search and AI integrations, Zilliz offers a fully managed, enterprise-grade vector database solution that simplifies deployment.

Streamline AI Data Management

With Zilliz, experience high performance and scalability for billion-scale vector search, empowering your AI applications to deliver faster, more accurate insights.

Achieve Seamless AI Integration

Its cloud-agnostic architecture and robust security make Zilliz the perfect tool to upgrade your AI infrastructure without hassle, helping you focus on your core projects and innovations.

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How Zilliz Works In 3 Steps?

  1. 1. Sign up for Zilliz Cloud

    Create a free account to access scalable vector database features and start managing your enterprise AI data.
  2. 2. Integrate SDKs for deployment

    Download SDKs (Python, Java, Go, Node.js) to connect your applications with Zilliz's high-performance vector search platform.
  3. 3. Perform vector searches

    Create collections and execute similarity searches to enable efficient AI-driven retrieval and analysis.

Customer Reviews for Zilliz

Overall Analytics

Comprehensive review insights and historical performance

Very Positive (2) 4.5/5 2 reviews 100% recommend — Monthly growth
6-month timeline
Most helpful
Charlotte Taylor
Charlotte Taylor

As a data scientist at a mid-sized tech firm, I needed a reliable way to handle billion-scale image embeddings for our visual search app. Zilliz's fully managed Milvus service simplified deployment tremendously—within hours, I had a robust, scalable setup without the hassle of managing infrastructure. Its high-performance vector search made retrieval speeds lightning-fast, which was crucial for user experience. The built-in embedding pipelines streamlined our AI workflow, especially during model updates. My only wish is that multi-cloud deployment could offer more seamless migrations between providers, but overall, it's been a game changer for our project.

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Showing 1 - 2 of 2 reviews .

User avatar for Charlotte Taylor

Charlotte Taylor

4.0
Recommends

Good for Large-Scale Recommendations, But Room for Improvement

Used for 1-3 months

What I liked

  • Supports billion-scale vector search suitable for large recommender systems
  • Multi-cloud deployment offers flexible hosting options
  • Built-in embedding pipelines simplify integration for multimodal data
  • High performance retrieval improves responsiveness

What could be better

  • Initial setup documentation could be more comprehensive for complex, multimodal datasets
  • Limited built-in governance tools make compliance harder for sensitive data

I'm a freelance developer working on a multimodal video recommendation system for a startup. Zilliz's support for billion-scale vector search was impressive—deploying their managed service meant I didn't need to worry about scaling issues. The multi-cloud options allowed us to host across different environments to optimize latency. However, I found the setup documentation somewhat sparse, especially around fine-tuning indexing parameters for multimodal data. Also, integrating the embedding pipelines was straightforward, but I wish there were more built-in tools for advanced governance and data privacy. Still, for large-scale projects, Zilliz offers a robust backbone.

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User avatar for Charlotte Taylor

Charlotte Taylor

5.0
Recommends

Streamlined AI Data Management with Milvus Integration

Used for over year

What I liked

  • Fully managed Milvus service simplified deployment, saving me days of setup time
  • Billion-scale vector search handles our large image dataset efficiently
  • Built-in embedding pipelines streamline our AI workflows
  • High performance retrieval speeds improve user experience

What could be better

  • Multi-cloud deployment could be more flexible; moving datasets between clouds isn't as straightforward as I'd like
  • Lack of detailed granular access controls in the current security features

As a data scientist at a mid-sized tech firm, I needed a reliable way to handle billion-scale image embeddings for our visual search app. Zilliz's fully managed Milvus service simplified deployment tremendously—within hours, I had a robust, scalable setup without the hassle of managing infrastructure. Its high-performance vector search made retrieval speeds lightning-fast, which was crucial for user experience. The built-in embedding pipelines streamlined our AI workflow, especially during model updates. My only wish is that multi-cloud deployment could offer more seamless migrations between providers, but overall, it's been a game changer for our project.

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Direct Comparison

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Zilliz VS Zillion

Zilliz: Features, Advantages & FAQs

Explore everything you need to know about Zilliz

Core Features
  • Fully managed Milvus service: simplifies deployment
  • Billion-scale vector search: handles large datasets
  • High performance: faster retrieval
  • Highly Scalable: supports up to 100 billion items
  • Security & Governance: ensures data protection
  • Built-in Embedding Pipelines: streamlines AI workflows
  • Multi-Cloud availability: flexible deployment options
Advantages
  • Handles billion-scale data
  • High availability with 99.95% uptime
  • Multi-cloud support
  • Security certifications such as SOC2 and ISO27001
  • Fast integration via SDKs and APIs
  • Supports large enterprises with scalable architecture
Use Cases
  • Retrieval Augmented Generation (RAG)
  • Recommender System
  • Text/Semantic Search
  • Image Similarity Search
  • Audio Similarity Search
  • Video Similarity Search
  • Multimodal Similarity Search

Frequently Asked Questions

What is a Compute Unit (CU)?

A Compute Unit (CU) represents the compute capacity allocated for your vector database workloads, impacting performance and scalability.

How many CUs do I need for a collection?

The number depends on your dataset size and performance needs; higher CUs improve speed and capacity.

Does Zilliz offer a free trial?

Yes, Zilliz offers a free plan with 5 GB storage, suitable for learning and prototyping, with options to upgrade.

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