Websites using Milvus

Okay, let s break down Milvus, covering its overview, revenue model (though a bit indirect), alternatives, pricing (mostly open-source), and customer care.

Milvus Overview

• What is it? Milvus is an open-source vector database designed for storing, indexing, and managing massive embedding vectors generated by machine learning models. It enables fast similarity searches over these high-dimensional vectors. Think of it as a specialized database for AI applications where you need to find similar items quickly.

• Key Features:

  • Scalability: Designed to handle billions of vectors, scaling horizontally across multiple nodes.
  • High Performance: Uses optimized indexing techniques (like HNSW, IVF, Annoy) to achieve low-latency search results.
  • Real-time Search: Suitable for applications that require near real-time updates and search.
  • Cloud-Native: Built to be deployed and managed on Kubernetes and other cloud platforms.
  • Vector Indexing: Supports a variety of indexing algorithms, allowing you to choose the best one for your specific data and performance requirements.
  • APIs: Provides APIs (Python, Go, Java, REST) for easy integration with your applications.
  • SQL-like Interface (Milvus Data Manipulation Language - DML): Makes it easier to query and manage vector data.
  • Data Persistence: Stores vector data on object storage services like AWS S3, Azure Blob Storage, and MinIO.

• Use Cases:

  • Image Retrieval: Finding similar images based on image embeddings.
  • Natural Language Processing (NLP): Semantic search, recommendation systems, question answering.
  • Recommender Systems: Suggesting relevant products, articles, or content based on user preferences.
  • Fraud Detection: Identifying fraudulent transactions based on behavioral patterns.
  • Drug Discovery: Finding similar molecules based on chemical structure embeddings.

Revenue (and the Commercial Angle)

• Milvus Open Source: The core Milvus database is open-source under the Apache 2.0 license. This means you can download, use, modify, and distribute it for free.

• Zilliz (The Company Behind Milvus): Zilliz is the company that initially created and continues to develop Milvus. Their revenue model centers around:

  • Zilliz Cloud (formerly called Attu ): This is a fully managed vector database service based on Milvus, offered by Zilliz. They handle the infrastructure, scaling, maintenance, and optimization. This is a subscription-based service. This is their primary revenue stream.
  • Enterprise Support and Services: Zilliz provides enterprise-grade support, consulting, and professional services for companies deploying and managing Milvus.
  • Potential Future Commercial Features: Zilliz could potentially introduce commercial features or extensions to Milvus in the future, but the core remains open-source.

• Therefore, while Milvus itself is free, Zilliz generates revenue through its managed cloud service and enterprise offerings. You pay for the convenience, scalability, and support that Zilliz Cloud provides.

Alternatives to Milvus

Here are some popular alternatives, categorized:

• Other Vector Databases:

  • Pinecone: A fully managed vector database designed for ease of use and scalability. Focuses heavily on simplicity and developer experience.
  • Weaviate: An open-source, graph-based vector database with strong semantic search capabilities.
  • Qdrant: An open-source vector similarity search engine with a focus on ease of deployment and high performance.
  • Vespa: Yahoo s open-source search engine that also supports vector search. Highly scalable and feature-rich, but complex to set up.
  • ChromaDB: Open source embedding database aimed at LLM applications
  • Pgvector (PostgreSQL Extension): Adds vector similarity search capabilities directly to PostgreSQL. A good option if you re already using PostgreSQL.
  • Faiss (Facebook AI Similarity Search): A library for efficient similarity search. Not a full database, but a powerful building block.
  • Annoy (Approximate Nearest Neighbors Oh Yeah): Another library for approximate nearest neighbor search.

• Cloud-Based Search Services with Vector Capabilities:

  • Amazon OpenSearch Service: AWS s managed search service. It offers a k-NN plugin for vector search.
  • Azure Cognitive Search: Microsoft Azure s search service. Supports vector search.
  • Google Cloud Vertex AI Matching Engine: Google s vector search service, part of their Vertex AI platform.

Pricing

• Milvus (Open Source): Free to use. Your costs are related to the infrastructure you use to deploy and manage it (servers, storage, Kubernetes clusters, etc.).

• Zilliz Cloud: Subscription-based. Pricing depends on several factors:

  • Storage: The amount of vector data you store.
  • Compute: The resources required for indexing and searching.
  • Throughput: The number of queries per second (QPS) you need.
  • Data Transfer: Ingress/Egress of data
  • Typically they have a free tier for testing/development and then tiered pricing based on usage. You ll need to contact Zilliz directly or visit their website for the most up-to-date pricing information.

• Other Vector Databases:

  • Pinecone: Consumption based pricing based on index size, compute resources, and data transfer.
  • Weaviate, Qdrant, Vespa: Open source and free to use, but require infrastructure management. Commercial support and managed services may be available.
  • Pgvector: Free to use with PostgreSQL, but you pay for the PostgreSQL instance.
  • Cloud provider offerings (Amazon OpenSearch, Azure Cognitive Search, Google Cloud Vertex AI): Pay-as-you-go based on usage.

Customer Care Details

• Milvus (Open Source):

  • Community Support: Primarily through the Milvus community:
    • GitHub: For issues, bug reports, and feature requests.
    • Slack: A Milvus Slack workspace for real-time discussions and help.
    • Forums/Mailing Lists: May exist for longer-form discussions.
  • Documentation: Comprehensive documentation on the Milvus website.

• Zilliz Cloud:

  • Dedicated Support: Zilliz provides dedicated customer support for paying Zilliz Cloud users. The level of support often depends on your subscription tier (e.g., basic, premium, enterprise).
  • Service Level Agreements (SLAs): Zilliz Cloud offers SLAs that guarantee uptime and performance.
  • Documentation: Zilliz provides documentation specific to Zilliz Cloud, covering setup, configuration, and usage.
  • Contact Information: You can typically find contact information for sales and support on the Zilliz website.

• Other Vector Databases: The level of customer care varies depending on whether it s an open-source project or a commercial service. Commercial services usually offer dedicated support, while open-source projects rely on community support.

Key Considerations When Choosing:

• Scalability Requirements: How much data do you need to store and search?
• Performance Needs: What are your latency requirements?
• Ease of Use: How easy is it to set up, configure, and manage?
• Integration: Does it integrate well with your existing tech stack?
• Budget: What is your budget for infrastructure, support, and managed services?
• Open Source vs. Managed Service: Do you want to manage the database yourself, or would you prefer a managed service?

In summary, Milvus is a powerful open-source vector database. Zilliz provides a commercial offering through their cloud product and support. The best choice for you depends on your specific needs and resources. Remember to thoroughly evaluate your requirements before making a decision.