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Milvus

  • What it is:Zilliz is an AI infrastructure company that developed Milvus, a distributed vector database purpose-built for handling massive-scale vector data in AI applications. The company provides both open-source software and commercial managed cloud services (Zilliz Cloud) for vector similarity search and...
  • Best for:AI/ML engineering teams with DevOps resources, GenAI RAG applications requiring billion-scale vectors, Cost-conscious enterprises avoiding vendor lock-in
  • Pricing:Free tier available, paid plans from $0/month
  • Expert's conclusion:At the moment, Milvus is generally regarded as the “best in breed” option for a production ready scalable and flexible vector search engine that allows you to avoid vendor lock-in.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Milvus and What Does It Do?

Zilliz is a business focused on creating artificial intelligence (AI) infrastructure using the Milvus distributed vector database, an infrastructure system to manage and perform computations on very large quantities of vector data. Additionally, Zilliz provides a free, open-source version of this technology and provides paid managed cloud-based versions (Zilliz Cloud) for similar types of computations on their technology such as performing vector similarity searches, etc.

Active
📍Unknown
📅Founded 2017
🏢Private
TARGET SEGMENTS
EnterpriseDevelopersAI/ML EngineersData Scientists

What Are Milvus's Key Business Metrics?

📊
35,000+
GitHub Stars
📊
Tens of billions of vectors
Supported Vector Scale
📊
Named Leader in Vector Database Category
Forrester Recognition
📊
Graduate Project (as of June 2021)
Linux Foundation Status

What is the history of Milvus and its key milestones?

2017

Milvus Development Begins

Zilliz initially started developing Milvus as a distributed vector database to support a variety of AI applications.

2020

Linux Foundation Incubation

Milvus entered into the incubation phase of the Linux Foundation in January 2020.

2021

Linux Foundation Graduate Status

By June of 2021, Milvus had graduated from the incubation phase of the Linux Foundation and presented its technical design and architecture at the ACM SIGMOD Conference.

2022

Milvus 2.0 Release

A major redesign occurred in January of 2022 and Milvus now includes a cloud-native architecture that decouples the storage layer from the compute layer.

2024

Forrester Recognition & Milestone Achievement

In addition to achieving 30,000 GitHub stars, Forrester also ranked Zilliz as one of the leaders in the vector database category.

What Are the Key Features of Milvus?

📊
Advanced Vector Indexing
Milvus currently supports several indexing algorithms including HNSW (Hierarchical Navigable Small World), IVF (Inverted File) variants, DiskANN, and binary indexes to optimize the performance of the Approximate Nearest Neighbor (ANN) search process.
✨
Distributed Scalable Architecture
The ability to independently scale each of the storage layer, coordinator layer, and worker node layers enables the ability to manage tens of billions of vectors across distributed systems.
✨
Vector Quantization
Milvus supports both Product Quantization (PQ) and Scalar Quantization (SQ) methods to compress data and provide users with options to reduce the amount of storage required for their data while potentially reducing the performance of the ANN search process as a consequence of reduced precision.
✨
Multi-Tenancy & Data Isolation
Milvus has three types of multi-tenancy; database-oriented, collection-oriented, partition-oriented and they all have role-based access control (RBAC) for secure resource sharing.
✨
Hybrid & Multi-Vector Search
Furthermore, Milvus allows you to perform similarity searches on both vector data and scalar data in parallel which will enable you to use metadata to filter your results and perform multi-vector queries for complex AI applications.
✨
Real-Time Data Ingestion
It supports real-time data stream ingestion and is dynamically-typed so you can ingest and process data into your system without defining a schema ahead of time.
✨
Multiple Consistency Levels
Additionally, it provides four data consistency models that can be used to maximize both low-latency performance and high-durability for your application: eventual consistency, session consistency, monotonic reads, and strong consistency.
✨
Cloud-Native Design
The stateless access-layer includes load balancing, memory-mapped storage, and a micro-service architecture optimized for simple integration with Kubernetes deployments.

What Technology Stack and Infrastructure Does Milvus Use?

Infrastructure

Cloud-native distributed architecture with decoupled storage and compute layers; supports both self-hosted deployment and managed Zilliz Cloud (serverless and dedicated cluster options)

Technologies

FAISSDiskANNhnswlibKubernetesMicroservices architecture

Integrations

Apache SparkRayMultiple programming language APIsCloud object storage

AI/ML Capabilities

Vector similarity search engine with support for various distance metrics (Euclidean, cosine, Hamming, Jaccard), advanced indexing algorithms, and vector quantization techniques optimized for high-dimensional data retrieval

Based on official documentation and product specifications

What Are the Best Use Cases for Milvus?

Machine Learning Engineers
To build an efficient similarity search engine for models that generate vector embeddings to allow for fast nearest neighbor searches at scale, for RAG and semantic search type applications.
Recommendation System Developers
For large-scale recommendation engines with billions of vectors, develop a scalable, efficient searching capability to support both user and item embeddings to find similar items, users or content.
Computer Vision Teams
Develop image and video retrieval systems that store and search image embeddings for visual similarity matching and content-based image discovery.
NLP/Semantic Search Builders
Develop semantic search platforms that understand query intent through text embeddings to provide more relevant search results than those found by keyword-based search across documents or knowledge bases.
Data Analytics Teams
In addition to supporting the processing and analysis of historical document collections, also support batch similarity searches to identify trends and patterns in very large unstructured datasets.
NOT FORLow-Latency Financial Trading Systems
Not Recommended — Vector databases are generally not designed for the sub-100 ms latency levels expected in high frequency trading environments.
NOT FORSimple Exact-Match Lookups
Not Recommended — Generally relational databases are more suitable for exact-match lookups, while Vector Databases add an unnecessary layer of complexity when attempting to do anything beyond similarity searching.
NOT FORHIPAA-Regulated Healthcare Applications
Not Applicable — Although Milvus provides Role Based Access Control (RBAC), I could find no evidence of any HIPAA compliance certifications or Business Associate Agreements (BAAs) from publicly available documentation.

How Much Does Milvus Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Milvus Open Source$0100% free under Apache License 2.0 for self-hosted deploymentMilvus Documentation
Zilliz Cloud Free Tier$0/month5 GB storage, 2.5 M vCUs, up to 5 collectionsAWS Marketplace
Zilliz Cloud ServerlessPay-as-you-go$0.001 per usage unit (0.1 cent), based on compute units consumed. Starts at $0.148/million queries for medium datasetsZilliz Pricing & AWS Marketplace
Zilliz Cloud Dedicated ClusterPay-as-you-go or contractCustom cluster pricing, annual commitment discounts availableZilliz Cloud
Zilliz Cloud BYOCCustom quoteBring Your Own Cloud deployment for enhanced security and controlMicrosoft Marketplace
Zilliz Cloud Free Trial30 daysServerless and Dedicated free for 30 days, $200 free credits, cancel anytimeAWS Marketplace
Elest.io Managed Milvus$0.022-$0.081/hourHourly billing starting at NC-MEDIUM-2C-4G ($16/month equivalent)Elest.io
Milvus Open Source$0
100% free under Apache License 2.0 for self-hosted deployment
Milvus Documentation
Zilliz Cloud Free Tier$0/month
5 GB storage, 2.5 M vCUs, up to 5 collections
AWS Marketplace
Zilliz Cloud ServerlessPay-as-you-go
$0.001 per usage unit (0.1 cent), based on compute units consumed. Starts at $0.148/million queries for medium datasets
Zilliz Pricing & AWS Marketplace
Zilliz Cloud Dedicated ClusterPay-as-you-go or contract
Custom cluster pricing, annual commitment discounts available
Zilliz Cloud
Zilliz Cloud BYOCCustom quote
Bring Your Own Cloud deployment for enhanced security and control
Microsoft Marketplace
Zilliz Cloud Free Trial30 days
Serverless and Dedicated free for 30 days, $200 free credits, cancel anytime
AWS Marketplace
Elest.io Managed Milvus$0.022-$0.081/hour
Hourly billing starting at NC-MEDIUM-2C-4G ($16/month equivalent)
Elest.io

How Does Milvus Compare to Competitors?

FeatureMilvusZilliz CloudPineconeWeaviate
Core FunctionalityVector similarity searchAdvanced: multi-vector, hybrid, sparseBasic vector searchHybrid search
Multiple Vector FieldsYesYes (multi-vector)NoYes
GPU AccelerationNoYes (NVIDIA)NoPartial
Deployment OptionsSelf-hosted (Lite/Standalone/Distributed)Fully managed + BYOCCloud onlySelf-hosted + Cloud
Starting Price$0 (open source)Pay-as-you-go ($0.148/M queries)$0.048/GB stored$0 (open source)
Free TierYes (open source)Yes (5GB)NoYes (open source)
Enterprise FeaturesManual SSO/RBACSSO, multitenancy, SLAYes (enterprise plan)Yes (enterprise)
API AvailabilityYes (multiple SDKs)YesYesYes
Index TypesHNSW, IVF, DiskANNHNSW, SCANN, GPUPod-basedHNSW, IVF
Performance-Cost$0.178/M queries (medium)$0.148/M queries (medium)Higher costVariable
Core Functionality
MilvusVector similarity search
Zilliz CloudAdvanced: multi-vector, hybrid, sparse
PineconeBasic vector search
WeaviateHybrid search
Multiple Vector Fields
MilvusYes
Zilliz CloudYes (multi-vector)
PineconeNo
WeaviateYes
GPU Acceleration
MilvusNo
Zilliz CloudYes (NVIDIA)
PineconeNo
WeaviatePartial
Deployment Options
MilvusSelf-hosted (Lite/Standalone/Distributed)
Zilliz CloudFully managed + BYOC
PineconeCloud only
WeaviateSelf-hosted + Cloud
Starting Price
Milvus$0 (open source)
Zilliz CloudPay-as-you-go ($0.148/M queries)
Pinecone$0.048/GB stored
Weaviate$0 (open source)
Free Tier
MilvusYes (open source)
Zilliz CloudYes (5GB)
PineconeNo
WeaviateYes (open source)
Enterprise Features
MilvusManual SSO/RBAC
Zilliz CloudSSO, multitenancy, SLA
PineconeYes (enterprise plan)
WeaviateYes (enterprise)
API Availability
MilvusYes (multiple SDKs)
Zilliz CloudYes
PineconeYes
WeaviateYes
Index Types
MilvusHNSW, IVF, DiskANN
Zilliz CloudHNSW, SCANN, GPU
PineconePod-based
WeaviateHNSW, IVF
Performance-Cost
Milvus$0.178/M queries (medium)
Zilliz Cloud$0.148/M queries (medium)
PineconeHigher cost
WeaviateVariable

How Does Milvus Compare to Competitors?

vs Zilliz Cloud

The Zilliz Cloud is a commercially managed, full-managed version of Milvus that offers users a 4X increase in performance, GPU acceleration, and fully managed operations and services to allow for compatibility across all platforms. However, the free, self-hosted version of Milvus will require a significant amount of DevOps investment by its users.

While Zilliz Cloud is designed for running large-scale production applications with minimal administrative overhead, it can be useful to run free, community-supported versions of Milvus (the open source) when working on proof-of-concept, prototyping, etc., at a low cost.

vs Pinecone

Both Zilliz/Milvus and Pinecone’s cloud-based vector search, as well as their search functions, (multi-vector, hybrid, and sparse) and self-hosted functionality, have more search functions than Pinecone. However, Pinecone is much simpler to enter into using than either Zilliz/Milvus, but it also costs more for storage, and lacks multi-vector support.

While both solutions have their advantages and disadvantages, if you need to use a solution for large-scale workloads, want to control costs and have a wide variety of tool sets available, you would most likely want to use Milvus. On the other hand, if you simply want to provide the easiest-to-use developer experience possible and do not mind paying a little extra for storage to achieve that, you might want to use Pinecone.

vs Weaviate

Both Milvus/Zilliz and Weaviate can perform hybrid searches using open source search capabilities. Both products have similar pricing models, however, Milvus/Zilliz is more focused on the highest level of pure vector performance/scale (10B+), and Weaviate focuses on semantic search with knowledge graph capabilities.

When searching for high-speed vector similarity, you will probably want to use Milvus. On the other hand, if you are looking to build an application that uses a combination of semantic/hybrid knowledge types, you might want to look into Weaviate.

vs Qdrant

Both Milvus/Zilliz and Qdrant focus on performing high-levels of vector search with filtering functions. While Milvus/Zilliz provides a more comprehensive set of tools and a managed version of the product through Zilliz, Qdrant provides improved disk performance and improved Rust performance. Furthermore, while there are many users of Milvus, Qdrant's user base is smaller. Text Between the Markers Begins Here

If you like having a more established set of tools available for Milvus, you will probably want to use this solution. On the other hand, if you need memory-optimized performance from your vector search, you will probably want to look into Qdrant.

What are the strengths and limitations of Milvus?

Pros

  • If you are going to host the completely free open source version of Milvus yourself, there will be no licensing fee for you to pay.
  • The horizontally scaled version of Milvus provides developers with the ability to scale out to support tens of billions of vectors.
  • The high-performant version of Milvus is capable of processing data in real-time by taking advantage of optimized HNSW/DiskANN indexing.
  • With the availability of five different ways to deploy Milvus (Lite, Standalone, Distributed, etc.) developers can select the method that best suits their needs. In addition, Zilliz also offers a fully-managed version of Milvus.
  • There are many tools available to developers who are using Milvus, these tools include but are not limited to, the Attu GUI, CLI, Spark/Kafka connectors and several SDKs.
  • In addition to the standard search functionality offered by Milvus, developers can also leverage advanced features such as Hybrid Search, Filtered Search, Sparse/Bulk Vectors and Multi-Vector Fields. START_TEXT
  • The mature ecosystem around Milvus is comprised of numerous organizations that utilize this solution in their respective production environments.
  • The vendor-neutral architecture of Milvus reduces the likelihood of being dependent upon one particular provider by offering multiple managed hosting options.

Cons

  • Complex Self-Hosting - Requires DevOps/Kubernetes expertise for scaled production usage
  • No Built-In UI - Attu needs to be installed separately and may require more effort than other competitors to set up
  • Ongoing operational burden - Cluster administration/monitoring/scalability are all performed manually
  • No GPU (Graphical Processing Unit) available within OSS (Open Source Solution) - Zilliz Cloud provides GPU acceleration for high-performance vector processing
  • High learning curve - significant time and effort will need to be dedicated to mastering advanced configuration/schema design
  • Fewer managed service alternatives - Zilliz is the only major company providing managed services for embedded hosting
  • Enterprise SLAs (Service Level Agreements) Are Not Provided - Self-hosted does not provide formal support or guarantees on service level agreements
  • Memory intensive - large amounts of memory are needed for RAM without tiered storage for vector processing

Who Is Milvus Best For?

Best For

  • AI/ML engineering teams with DevOps resources — Free OSS offers massive scalability without costs from vendors
  • GenAI RAG applications requiring billion-scale vectors — Most scalable/highest performance solution for production embeddings
  • Cost-conscious enterprises avoiding vendor lock-in — Flexibility through multi-cloud hosting options as well as open source
  • Teams already using Kubernetes/Helm infrastructure — Native distributed deployment using existing stack supported
  • Recommendation systems and anomaly detection — Optimized for high-throughput similarity search workload

Not Suitable For

  • Teams without dedicated infrastructure engineers — Simpler libraries require less overhead for operation compared to self-hosted version of Milvus with managed options such as Pinecone/Zilliz Cloud
  • Small prototyping projects (<1M vectors) — Deployment overhead outweighs benefits compared to simpler libraries
  • Real-time sub-10ms latency requirements — Latency will be less consistent when not utilizing GPU acceleration available in Zilliz Cloud
  • Non-technical data science teams — Unlike serverless managed solutions, Milvus has a greater need for schema/indexing expertise

Are There Usage Limits or Geographic Restrictions for Milvus?

Free Tier Storage
5 GB, 2.5 M vCUs, 5 collections max (Zilliz Cloud)
Self-Hosted Scale
Hardware limited only - requires manual scaling to billions of vectors
Query Performance
Medium dataset: $0.178/M queries (OSS), $0.148/M (Zilliz) perf-cost
Collections per Cluster
100K+ collections supported (Zilliz Cloud enterprise)
Vector Capacity
1.5M (perf-opt), 5M (capacity-opt), 20M (tiered) per Query CU
GPU Acceleration
NVIDIA GPU support only in Zilliz Cloud, not OSS
Compliance Certifications
SOC 2, ISO 27001, GDPR (Zilliz Cloud enterprise)

Is Milvus Secure and Compliant?

SOC 2 Type IIEnterprise-grade security certification for Zilliz Cloud
ISO 27001International information security management certification
GDPR ComplianceFull compliance for EU data protection requirements
BYOC DeploymentBring Your Own Cloud option for maximum data sovereignty and control
Multi-TenancyAdvanced multitenancy with resource isolation at enterprise scale
Global ClustersRegion-level resilience and data locality compliance
99.95% SLAHigh availability guarantee for production workloads (Zilliz Cloud)

What Customer Support Options Does Milvus Offer?

Channels
Community support, active discussionsAsynchronous support, file issuesFree 1:1 expert sessions, book 20-minute slotsReport bugs and problems
Hours
Office hours by appointment, community support 24/7
Response Time
Community-driven, varies; expert sessions scheduled
Satisfaction
Positive community feedback on expert help
Specialized
Vector DB experts available during office hours for performance tuning, schema design, scaling, integrations
Support Limitations
•Primarily community support for open-source version
•No guaranteed SLAs for open-source self-hosted
•Professional support via Zilliz Cloud managed service

What APIs and Integrations Does Milvus Support?

API Type
gRPC (primary), REST API available
Authentication
API Key, TLS certificates, RBAC in enterprise
SDKs
Python (pymilvus), Java, Go, Node.js, C++, C#, Rust
Documentation
Comprehensive docs at milvus.io/docs with code samples and interactive tutorials
Webhooks
Not natively supported; use Pulsar/Kafka for event streaming
Sandbox
Zilliz Cloud offers free tier for testing; Milvus Lite for local development
SLA
Self-hosted: none; Zilliz Cloud: 99.99% uptime SLA available
Rate Limits
Configurable query node limits; depends on deployment scale
Use Cases
Vector similarity search, ANN, hybrid search, RAG pipelines, agentic AI

What Are Common Questions About Milvus?

Milvus is a high-dimensional vector database designed to store index & search large numbers of high-dimensional vectors. Milvus implements the Approximate Nearest Neighbor (ANN) algorithms to find semantic similarities in embeddings created by AI models.

Milvus is completely open-source & self-hosted. Flexibility exists to deploy Milvus in stand-alone mode, Kubernetes mode or cloud-mode. In contrast, Pinecone is a managed SaaS option with a simpler setup than Milvus but at a higher price point when scaling-up.

Yes, Milvus has built-in features including: TLS encryption, role-based access control (RBAC) & data isolation. For enterprise deployments there is also support for audit logs and compliance certifications via Zilliz Cloud (e.g. SOC 2, HIPAA available).

The core version of Milvus has no cost; however the company behind Milvus, Zilliz, offers serverless and dedicated cluster versions of Milvus as part of its "cloud" service under a pay-as-you-go pricing structure that begins at "free" and increases up to an "enterprise" level pricing structure based upon the amount of compute resources and storage being utilized by each customer.

There are official integrations. Both frameworks have included a Milvus component to store vectors and to utilize the stored vectors within pipelines for RAG (Relevance-based Aggregation), semantic search and agent memory.

Milvus is provided as four separate versions: Milvus Lite (a single binary); Milvus Standalone (which can be run in Docker containers); Milvus Distributed (which can be run in Kubernetes); and Zilliz Cloud (which is a fully-managed SaaS offering which enables customers to choose from either serverless or dedicated options for their own usage.

Yes, Milvus supports vector similarity search & also utilizes a filter based upon the metadata (as scalars) to further limit the number of vectors to be searched through. Additionally, Milvus supports full-text search and the capability to support multiple vectors per entity.

The community receives assistance through a variety of means including: - Free Office Hours (Expert Engineers) - Support through GitHub Issues - Extensive Documentation - Zilliz Cloud Support

Is Milvus Worth It?

Milvus has the largest, and most robust Open Source Vector Database. This allows for deployment of the technology as needed, and therefore provides scalability for the user when used in production environments. This is further enhanced by the large and active community which supports the user -- whether they are experimenting or are in an Enterprise environment.

Recommended For

  • A production ready Relevance-based Aggregation (RAG) solution - Semantic Search - Recommendation System Development
  • Businesses seeking to avoid being locked into the SaaS Platform offered by any one vendor and choose to host the application themselves.
  • Businesses requiring Scalable Vector Search functionality and have data that is horizontally scalable.
  • Developers using open-source technologies and/or have budget constraints.
  • Customers requiring the capability to deploy the application across multiple cloud platforms or hybrid environments.

!
Use With Caution

  • Businesses lacking the expertise and/or knowledge required to maintain their own distributed deployments. Milvus distributed deployments require knowledge of Kubernetes.
  • Applications requiring ultra-low latency (<10ms) response times. Appropriate optimization should be performed for production use.
  • Small-Scale Proof-of-Concepts. Other technologies may exist to meet your immediate needs.

Not Recommended For

  • Organizations whose users are non-technical and therefore unable to administer their own systems.
  • Start-Up businesses desiring cost-effective methods of developing the operational skill/experience needed to operate the application.
  • Organizations that need nothing but basic vector embeddings stored (no need for anything beyond the basic query).
Expert's Conclusion

At the moment, Milvus is generally regarded as the “best in breed” option for a production ready scalable and flexible vector search engine that allows you to avoid vendor lock-in.

Best For
A production ready Relevance-based Aggregation (RAG) solution - Semantic Search - Recommendation System DevelopmentBusinesses seeking to avoid being locked into the SaaS Platform offered by any one vendor and choose to host the application themselves.Businesses requiring Scalable Vector Search functionality and have data that is horizontally scalable.

What do expert reviews and research say about Milvus?

Key Findings

At present, Milvus is the largest and most widely used open source vector database and by far the most mature in terms of both feature set and production readiness across all deployment options. A very large community exists for Milvus that offers a vast array of support mechanisms such as community forums and documentation and even a fully-managed enterprise-grade cloud managed service called Zilliz Cloud for deploying and managing Milvus on your behalf. Although Milvus has many benefits in terms of its scalability and integration into larger ecosystems of systems; there is a certain amount of technical expertise needed to properly scale and manage a distributed instance of Milvus.

Data Quality

Excellent - comprehensive official documentation, active GitHub (20k+ stars), community channels, and deployment guides. Limited pricing transparency for Zilliz Cloud enterprise tiers.

Risk Factors

!
Experience with Kubernetes will be necessary to implement Milvus successfully.
!
As the Ecosystem of Artificial Intelligence continues to evolve at breakneck speeds, the AI Team’s ability to adapt to this breakneck pace of change may necessitate that they continually upgrade their entire Software Stack.
!
Community support for answering user’s questions will vary in scope and depth of knowledge depending upon the nature of the question asked and the level of expertise of the person asking the question.
Last updated: January 2026

What Are the Best Alternatives to Milvus?

  • •
    Pinecone: Pinecone is a managed vector database that offers serverless simplicity, pod-based scalability and ease-of-use relative to self-hosted Milvus. However, Pinecone is proprietary, expensive and has high levels of vendor lock-in. Therefore, Pinecone would be the best choice for organizations that place a higher premium on ease-of-use than on control.
  • •
    Weaviate: Weaviate is an open-source vector database that comes with many pre-built Machine Learning (ML) capabilities along with a GraphQL API. In comparison to Milvus, Weaviate can be considered a "batteries included" type of offering with a Hybrid Search capability; however, it is a relatively new and immature technology when scaled. As such, Weaviate appears to be best suited for Knowledge Graph + Vector use cases.
  • •
    Qdrant: QDrant is a Rust-based Open Source vector database that is designed to deliver High Performance & Low Latency Searches, with Low Memory Requirements. Due to the Simpler Payload Model than Milvus, QDrant is a great candidate for Edge Device Deployments, where Resources are Limited. Therefore, QDrant is best utilized in Applications requiring Low-Latency On-Premises Searches.
  • •
    Chroma: Try Chroma is a Lightweight Open Source Embedding Database that was specifically Designed for Python Developers. Due to its Relative Simplicity compared to Milvus, it is ideal for Prototyping and Local Development; however, it should NOT be Utilized in Production-Scale Applications.
  • •
    Zilliz Cloud: Zilliz is a Fully Managed Software-as-a-Service (SaaS) Platform built on Top of Milvus, which provides 10X Performance Optimizations of Milvus in the Cloud. This provides a Zero Operations Alternative to Self-Managing Milvus, with Enterprise Service Level Agreement (SLA) Support. Therefore, it would be Best for Teams that Want to Use the Capabilities of Milvus Without Having to Manage the Underlying Infrastructure.

What Additional Information Is Available for Milvus?

Community

Weaviate has a Thriving Community, consisting of: Discord, GitHub Discussions (over 20k+ Stars), Weekly Office Hours with Vector DB Experts, and YouTube Tutorials. Additionally, Weaviate has an Ambassador Program for Contributors, and is Very Active Across All Major AI / ML Related Conferences.

Deployment Options

There are four deployment models for Milvus; Milvus Lite (local development), Milvus Standalone (Docker-based), Milvus Distributed (Kubernetes-based), and Zilliz Cloud (cloud-based managed). Milvus is compatible with ARM and X86 architectures, and is deployable across multi-cloud and air-gapped environments.

Ecosystem Integrations

Milvus provides native connector plugins to many of the most popular NLP libraries; LangChain, LlamaIndex, Haystack, Spark, Kafka, and Pulsar. In addition to this, Milvus also offers software developer kits (SDKs) for 8+ programming languages. It is possible to leverage an MCP server for managing natural language databases.

Performance Leadership

On both recall and QPS, Milvus significantly exceeds other competitive vector DB products. In addition to HNSW and IVF, Milvus supports over 18 different indexing algorithms; which include DiskANN. This allows for horizontal scalability and the ability to support collections containing billions of vectors.

Enterprise Adoption

Many Fortune 500 organizations have successfully implemented Milvus in production for use in RAG (real time question answering generation), recommending personalized products based on customer preference, and detecting fraud. Zilliz Cloud provides SOC 2 and HIPAA compliance, along with RBAC, Audit Logging, VPC Peering, and role based access control.

What Are Milvus's Vector Db Performance?

400% faster than Elasticsearch
Full-Text Search Speed
100x faster filtering
JSON Path Filtering
Tens of billions to trillions
Vector Capacity
Up to 100,000 collections per cluster
Multi-tenancy Collections

What Is Milvus's Vector Db Scalability?

Max Vectors
Tens of billions to trillions of vectors
Replication
Multi-replica support for fault tolerance and throughput optimization; hot/cold storage with automatic data migration based on access patterns
Sharding Support
Automatic partitioning and sharding across distributed nodes
Horizontal Scaling
Distributed architecture with independent scaling of compute and storage nodes; query nodes for read-heavy and data nodes for write-heavy workloads

What Vector Db Index Types Does Milvus Support?

HNSWIVFFLAT (brute-force)SCANNDiskANNGPU CAGRAAISAQ (all-in-storage)Product QuantizationQuantization-based variationsMemory-mapped (mmap)

Over 10 index types optimized for different scenarios with GPU acceleration support

What Vector Db Features Does Milvus Offer?

Hybrid Search

Using a single query operation, users may perform dense vector queries, sparse vector queries (BM25, SPLADE, BGE-M3), and full-text search using custom reranking.

Metadata Filtering

Users may utilize SQL-like filtering in conjunction with the results of vector similarity searches.

Multi-Tenancy

Milvus enables users to create isolation at multiple levels (database, collection, partition, or partition key), and to support hundreds to millions of tenants.

Hot/Cold Storage

Milvus enables auto-classification and tiered storage features allowing customers to potentially cut costs by as much as 50% while maintaining performance.

Range Search

Milvus contains advanced ANN (approximate nearest neighbor) capabilities.

Hardware Acceleration

Milvus contains support for using GPUs, SIMD instructions (e.g., AVX512, Neon), and quantization and cache-friendly optimizations.

Array of Vector Support

The struct is a part of Milvus which can be used by the users to save multiple vectors per one entity.

High Availability

Milvus has a stateless micro-service architecture on Kubernetes that is able to provide quick failover and restoration.

What Is Milvus's Vector Db Deployment?

Kubernetes
Kubernetes-native with stateless microservices; Helm chart support for quick recovery and high availability
Serverless
Cloud deployment options available
Self Hosted
Docker, standalone binary, and source deployment
Cloud Managed
Zilliz Cloud managed service available

What Vector Db Distance Metrics Does Milvus Support?

CosineEuclidean (L2)Inner ProductHammingJaccard

What Vector Db Integrations Does Milvus Offer?

Python SDK

There is no need to do anything else besides run "pip install milvus" to start using Milvus.

REST API

Milvus communicates through HTTP.

gRPC

Milvus also has a high speed RPC protocol.

Multiple Programming Languages

Milvus provides full SDK support for many different types of programming languages.

User-friendly Interfaces

Milvus makes it easier to work with your complex data.

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