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Comparing Milvus with Alternatives

When exploring various vector database options, this comprehensive guide will help you understand Milvus's unique features, ensuring you choose a database that best fits your specific needs. Notably, Milvus is a leading open-source vector database, and Zilliz Cloud offers a fully-managed Milvus service. To objectively evaluate Milvus against its competitors, consider using benchmark tools to analyze performance metrics.

Milvus highlights

  • Functionality: Milvus goes beyond basic vector similarity search by supporting advanced functionalities like sparse vector, bulk-vector, filtered search, and multi-vector search capabilities.

  • Flexibility: Milvus accommodates various deployment modes and multiple SDKs, all within a robust, integrated ecosystem.

  • Performance: Milvus guarantees real-time processing with high throughput and low latency, powered by optimized indexing algorithms such as HNSW and DiskANN, and advanced GPU acceleration.

  • Scalability: Its bespoke distributed architecture effortlessly scales, accommodating anything from small datasets to collections exceeding 10 billion vectors.

Overall comparison

To compare between Milvus and Pinecone, two vector database solutions, the following table is structured to highlight differences across various features.

Feature Pinecone Milvus Remarks
Deployment Modes SaaS-only Milvus Lite, On-prem Standalone & Cluster, Zilliz Cloud Saas & BYOC Milvus offers greater flexibility in deployment modes.
Supported SDKs Python, JavaScript/TypeScript Python, Java, NodeJS, Go, Restful API, C#, Rust Milvus supports a wider array of programming languages.
Open-source Status Closed Open-source Milvus is a popular open-source vector database.
Scalability Scale up/down only Scale out/in and Scale up/down Milvus features a distributed architecture for enhanced scalability.
Availability Pod-based architecture within available zones Available zone failover and cross-region HA Milvus CDC (Change Data Capture) enables primary/standby modes for higher availability.
Perf-Cost (Dollar per million queries) Starts at $0.178 for a medium dataset, $1.222 for a large dataset Zilliz Cloud starts at $0.148 for a medium dataset, $0.635 for a large dataset; free version available Refer to Cost Ranking report.
GPU Accelaration Not supported Support Nividia GPU GPU acceleration significantly enhances performance, often by orders of magnitude.

Terminology comparison

Although both serve similar functions as vector databases, the domain-specific terminology between Milvus and Pinecone shows slight variations. A detailed terminology comparison is as follows.

Pinecone Milvus Remarks
Index Collection In Pinecone, an index serves as the organizational unit for storing and managing vectors of identical size, and this index is closely integrated with the hardware, known as pods. In contrast, Milvus collections serve a similar purpose but enable handling multiple collections within a single instance.
Collection Backup In Pinecone, a collection is essentially a static snapshot of an index, used mainly for backup purposes and cannot be queried. In Milvus, the equivalent feature for creating backups is more transparent and straightforwardly named.
Namespace Partition key Namespaces allow the partitioning of vectors in an index into subsets. Milvus provides multiple methods like partition or partition key to ensure efficient data isolation within a collection.
Metadata Scalar field Pinecone's metadata handling relies on key-value pairs, while Milvus allows for complex scalar fields, including standard data types and dynamic JSON fields.
Query Search Name of the method used to find the nearest neighbors for a given vector, possibly with some additional filters applied on top.
Not available Iterator Pinecone lacks a feature for iterating through all vectors in an index. Milvus introduces Search Iterator and Query Iterator methods, enhancing data retrieval capabilities across datasets.

Capability comparison

Capability Pinecone Milvus
Deployment Modes SaaS-only Milvus Lite, On-prem Standalone & Cluster, Zilliz Cloud Saas & BYOC
Embedding Functions Not available Support with pymilvus[model]
Data Types String, Number, Bool, List of String String, VarChar, Number (Int, Float, Double), Bool, Array, JSON, Float Vector, Binary Vector, BFloat16, Float16, Sparse Vector
Metric and Index Types Cos, Dot, Euclidean
P-family, S-family
Cosine, IP (Dot), L2 (Euclidean), Hamming, Jaccard
FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, SCANN, GPU Indexes
Schema Design Flexible mode Flexible mode, Strict mode
Multiple Vector Fields N/A Multi-vector and hybrid search
Tools Datasets, text utilities, spark connector Attu, Birdwatcher, Backup, CLI, CDC, Spark and Kafka connectors

Key insights

  • Deployment modes: Milvus offers a variety of deployment options, including local deployment, Docker, Kubernetes on-premises, Cloud SaaS, and Bring Your Own Cloud (BYOC) for enterprises, whereas Pinecone is limited to SaaS deployment.

  • Embedding functions: Milvus supports additional embedding libraries, enabling the direct use of embedding models to transform source data into vectors.

  • Data types: Milvus supports a wider range of data types than Pinecone, including arrays and JSON. Pinecone supports only a flat metadata structure with strings, numbers, booleans, or lists of strings as values, whereas Milvus can handle any JSON object, including nested structures, within a JSON field. Pinecone limits the metadata size to 40KB per vector.

  • Metric and index types: Milvus supports a broad selection of metric and index types to accommodate various use cases, while Pinecone has a more limited selection. While an index for vector is mandatory in Milvus, an AUTO_INDEX option is available to streamline the configuration process.

  • Schema design: Milvus offers flexible create_collection modes for schema design, including a quick setup with a dynamic schema for a schema-less experience similar to Pinecone and a customized setup with predefined schema fields and indexes akin to a relational database management system (RDBMS).

  • Multiple vector fields: Milvus enables the storage of multiple vector fields within a single collection, which can be either sparse or dense and may vary in dimensionality. Pinecone does not offer a comparable feature.

  • Tools: Milvus offers a more extensive selection of tools for database management and utilization, such as Attu, Birdwatcher, Backup, CLI, CDC and Spark and Kafka connector.

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