Milvus
- Company typePrivate
- Founded2017
- HeadquartersRedwood City, United States
- Headcount1–10
- GTM typeB2B
- OfferingSoftware
What Milvus does
Milvus is an open-source vector database founded in 2017 and maintained by Zilliz, headquartered in San Jose, California. It is purpose-built for storing, indexing, and querying high-dimensional vector embeddings at scale, supporting dense, sparse, binary, and multimodal vectors alongside scalar fields. The platform runs in three deployment modes sharing a unified API: Milvus Lite (embedded Python library for notebooks and edge devices, installed via pip), Milvus Standalone (single-machine Docker deployment), and Milvus Distributed (Kubernetes-based cluster for billion-scale workloads).
The technology stack includes 10+ index types (HNSW, IVF, Product Quantization, GPU), hardware-accelerated compute via AVX512, Neon SIMD, and NVIDIA GPU indexing, plus the new Loon storage engine and Vortex columnar format underpinning the upcoming Milvus 3.0 and Vector Lakebase releases. Milvus 2.6.x delivers sub-10ms latency at billion-scale, 100x faster metadata filtering, and 7x faster full-text search than Elasticsearch, with three-layer tiered storage reducing storage costs by up to 87%.
Milvus monetizes through Zilliz Cloud, a fully managed commercial service offered in serverless and dedicated cluster tiers on AWS, Google Cloud, and Microsoft Azure, with BYOC options for regulated enterprises. Revenue mechanics combine freemium OSS adoption (Apache 2.0 license, 44.9K+ GitHub stars) with subscription and usage-based paid tiers, plus consulting-led enterprise deployments via system integrators such as Accenture and Deloitte. The customer base spans hundreds of enterprises across e-commerce (Walmart, eBay, Shopee, Tokopedia, Airbnb), social media (Reddit, Roblox, Line), financial services (PayPal, ZipRecruiter), healthcare (Doximity, OpenEvidence), legal (Filevine, Rexera), security (Palo Alto Networks, Trend Micro), and AI-native startups (Read AI, Notta, Exa, Credal AI).
Milvus firmographics
Firmographics- Name
- Milvus
- Legal name
- Milvus
- Website
- https://milvus.io
- Company type
- Private
- Founded year
- 2017
- Operating status
- Operating
- Headcount range
- 1–10 employees
- Ownership category
- akta.pro rank
Milvus industry classification
Industry- Product category
- Vector Database
- NAICS
- Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (5182), Computer Systems Design and Related Services (54151)
- SIC
- Services-Computer Programming, Data Processing, Etc. (7370)
- akta.pro primary industry
- Data Security & Privacy Managed Services (DLP/Encryption) (BPAEADAL)
- akta.pro secondary industry
- Security Operations Center (SOC) as a Service (BPAEADAB)
Keywords
Where Milvus is headquartered
LocationHeadquarters
- HQ city
- Redwood City
- HQ country
- United States
- HQ region
- North America
Offices1 record
Markets served
Milvus business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Technology or R&D, Personnel, Infrastructure, Marketing or Sales, Operations
Revenue model
- Open-source software (free): Milvus is an open-source vector database distributed under the Apache 2.0 license. Organizations can download, deploy, and use Milvus at no cost on their own infrastructure (Milvus Lite, Standalone, or Distributed modes). This drives community adoption and ecosystem growth without direct revenue from the open-source core.
- Zilliz Cloud (managed Milvus): Zilliz Cloud is the fully managed commercial service for Milvus, available in serverless and dedicated cluster tiers on AWS, Google Cloud, and Microsoft Azure. It provides a SaaS offering with BYOC (bring-your-own-cloud) options for security and compliance requirements. Zilliz Cloud generates subscription and usage-based revenue from organizations seeking zero-ops vector database deployments.
- Vector Lakebase (Zilliz Cloud exclusive): Vector Lakebase is a new Zilliz Cloud-exclusive product that extends cloud-resident vector search with batch analytics, interactive discovery, and external data lake connectivity. It represents an additional premium commercial tier for organizations needing lake-native vector capabilities beyond standard Milvus deployments.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Freemium | Others | Open-source Milvus (free) |
| Freemium | Monthly | Zilliz Cloud Free Tier |
| Usage-based | Pay-as-you-go | Zilliz Cloud Serverless |
| Subscription | Annual | Zilliz Cloud Dedicated Cluster |
Go-to-market motion3 records
Distribution channels7 records
Marketing channels12 records
Milvus product offering
Product offeringCore offering
Milvus is an open-source vector database purpose-built for storing, indexing, and searching high-dimensional vector embeddings used in GenAI applications. It scales elastically to tens of billions of vectors with sub-10ms query latency and is offered through three deployment modes (Lite, Standalone, Distributed) plus Zilliz Cloud, the fully managed commercial service available across AWS, GCP, and Azure.
Product overview
Milvus is an open-source vector database platform designed for GenAI applications, available in three deployment modes: Milvus Lite (embedded Python library for notebooks and edge devices), Milvus Standalone (single-machine Docker deployment for small-to-medium scale), and Milvus Distributed (Kubernetes-based cluster for billion-scale enterprise workloads). The platform is complemented by Zilliz Cloud, a fully managed cloud service offering serverless and dedicated cluster options with 10x performance improvement over self-hosted deployments. Recent additions include Vector Lakebase (extending vector search with lake-native storage and zero-copy data lake connectivity) and memsearch (open-source persistent memory library for AI agents). The ecosystem includes management tools like Attu (GUI), Milvus Backup, Deep Searcher, and Claude Context. All deployment modes share a unified Python/Go/Java/Node.js/C# API, with native integrations for LangChain, LlamaIndex, DSPy, Haystack, Ragas, OpenAI, and Hugging Face.
Differentiator
Problem solved
Functional benefit
Products and services
- Milvus Open-source vector database built for GenAI applications. Scales to tens of billions of vectors with sub-10ms query latency. Supports multiple deployment modes (Lite, Standalone, Distributed) sharing a unified API under Apache 2.0 license.
- Zilliz Cloud Fully managed Milvus cloud service available in serverless and dedicated cluster tiers across AWS, Google Cloud, and Microsoft Azure. Offers 10x faster performance than self-hosted Milvus with SaaS and BYOC deployment models for enterprise security and compliance.
- Vector Lakebase Zilliz Cloud-exclusive product that extends vector search with lake-native storage, batch analytics, interactive discovery, and external data lake connectivity. Features a zero-copy semantic data plane allowing the same vectors to serve production queries, discovery sessions, and training-data pipelines without data migration.
- memsearch Open-source library that gives AI agents persistent, long-term memory across conversations powered by the Milvus vector database. Stores all agent memories as human-readable plain-text files without vendor lock-in.
Quantifiable outcome
- Sub-10ms query latency at billion-scale vector search workloads
- +7 more outcomes
Companies that use Milvus
Customer profileNamed customers62 records
Segments4 records
Ideal customer profiles4 records
Milvus technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
Integration10 records
AI capability10 records
Feature12 records
Milvus partnerships and signals
Strategic signalPartnerships
15 partnerships are on record, tiered core, supporting and strategic.
- LangChaincoreMilvus is deeply integrated with LangChain as a native vector store retriever. The langchain-milvus package provides seamless RAG pipeline construction, hybrid search, full-text search, asynchronous search, and Agent RAG capabilities. This integration is a primary go-to-market channel for reaching LangChain's large developer community building LLM applications.
- LlamaIndexcoreMilvus integrates with LlamaIndex through the llama-index-vector-stores-milvus package, enabling RAG, async API, full-text search, hybrid search, and metadata filtering. LlamaIndex's large user base of developers building LLM applications serves as a distribution channel for Milvus adoption.
- OpenAIcoreMilvus provides official integration with OpenAI's Embedding API for generating vector embeddings, documented in tutorials showing semantic search workflows using OpenAI embeddings with Milvus as the vector store. OpenAI's dominant position in the LLM market makes this integration critical for developer accessibility.
- Hugging FacecoreMilvus integrates with Hugging Face's transformers, datasets, and sentence embedding models for generating vector representations. Official documentation includes a question-answering tutorial using Hugging Face SQuAD dataset and sentence-transformers models with Milvus for semantic search.
- DSPycoreMilvus is integrated into the DSPy framework through the MilvusRM retriever module, enabling DSPy programs to leverage Milvus' vector search capabilities for RAG optimization. The dspy-ai[milvus] package provides one-line installation for this integration, targeting Stanford NLP's DSPy user community.
- Haystack (deepset)coreThe milvus-haystack integration enables Haystack users to use MilvusDocumentStore and MilvusEmbeddingRetriever for building RAG pipelines with Haystack's open-source Python framework. deepset, Haystack's parent company, provides official support and co-marketing for this integration.
- RagassupportingMilvus integrates with Ragas for evaluating RAG pipeline performance. Official documentation shows how to use Ragas metrics to assess answer quality, faithfulness, and context relevance for RAG systems built on Milvus and OpenAI.
- MemGPTsupportingMemGPT integration with Milvus enables long-context memory management for AI agents built with the MemGPT framework, expanding Milvus' reach in the AI agent memory use case.
- Anthropic (Claude Code)coreZilliz released memsearch ccplugin as a dedicated persistent memory plugin for Anthropic's Claude Code AI coding assistant. This plugin uses Milvus-powered Memsearch to give Claude Code agents persistent, human-readable memory across conversations, extending Milvus into the AI coding assistant ecosystem.
- Linux Foundation (Vortex)strategicVector Lakebase builds on Vortex, a Linux Foundation open-source columnar file format. This open-source standard ensures interoperability and positions Milvus/Zilliz within the broader data lake ecosystem, supporting Lance, Iceberg, Parquet, and Vortex table formats.
- Kioxia CorporationsupportingKioxia Corporation integrated its KIOXIA AiSAQ open-source software technology into Milvus 2.6.4, enabling SSD-optimized vector search that addresses DRAM scalability bottlenecks in high-volume inference and RAG workloads.
- NVIDIAsupportingMilvus supports GPU-based indexing as part of its index offerings, leveraging NVIDIA GPUs for hardware-accelerated vector operations. The multimodal RAG pipeline uses NVIDIA GPUs alongside Milvus for efficient processing.
- AccenturesupportingAccenture leverages Zilliz Cloud (fully managed Milvus) to power semantic retrieval across training content, knowledge assets, and coaching context for enterprise learning and workflow modernization engagements.
- DeloittesupportingDeloitte uses Zilliz Cloud to power semantic retrieval across knowledge, policy, and client workflow context for enterprise digital transformation and AI implementation projects.
- CoreWeavesupportingCoreWeave published technical documentation on deploying Milvus (alongside Dragonfly and Pinecone) on CoreWeave's cloud infrastructure for production-ready RAG for agentic AI systems, enabling high-performance vector search on GPU-optimized cloud infrastructure.
Scale indicators15 records
Recent moves6 records
Expansion highlights5 records
Milvus competitors and assessment
Company assessmentDirect peers
- Pinecone: Pinecone is a leading fully managed vector database competing directly with Zilliz Cloud. Both target enterprise AI applications requiring production-grade vector search, and Pinecone is one of the most prominent alternatives cited by Milvus users during evaluation.
- Weaviate: Weaviate is an open-source vector database with strong hybrid search capabilities, offering both self-hosted and managed cloud deployment. It competes with Milvus on the same RAG, semantic search, and GenAI use cases among developer and enterprise audiences.
- Qdrant: Qdrant is an open-source vector search engine written in Rust with a managed cloud offering. Reddit notably selected Milvus over Qdrant based on performance and scalability, indicating direct head-to-head competition for production vector search workloads.
- Chroma: Chroma is an open-source embedding database widely adopted by AI developers for prototyping and RAG applications. It competes with Milvus Lite in the developer/PLG segment and is a common comparison point cited by Milvus in its own positioning.
Broad incumbents
- Elasticsearch (Elastic): Elasticsearch has added dense_vector field types and hybrid search capabilities, positioning itself as a broad incumbent for organizations seeking vector search within an existing search/analytics platform. Milvus 2.6.x benchmarks itself 7x faster than Elasticsearch on full-text search.
- MongoDB: MongoDB Atlas Vector Search adds vector capabilities to its document database platform. As a broad incumbent with massive enterprise install base, MongoDB represents a bundling threat for organizations already standardized on its ecosystem.
Emerging players
- Vespa.ai: Vespa is a search and serving engine with native vector and hybrid search capabilities, used for large-scale retrieval applications. It overlaps with Milvus in use cases like recommendation, semantic search, and RAG, particularly among internet-scale deployments.
- pgvector: pgvector is an open-source PostgreSQL extension that adds vector similarity search to the world's most popular open-source database. It competes with Milvus for developers who prefer to stay within their existing PostgreSQL stack rather than adopt a dedicated vector database.
- LanceDB: LanceDB is a serverless vector database built on the Lance columnar format, targeting AI applications with embedded and cloud-native deployment models. It overlaps with Milvus Lite and Milvus Distributed in the developer and lake-native vector segments.
- Marqo: Marqo is a tensor and vector search engine combining ML model inference with vector storage. It competes with Milvus in multimodal and end-to-end AI search workloads, particularly for cloud-native deployments targeting developers.
Market position
Strengths5 records
Weaknesses5 records
Competitive moat6 records
Key risks6 records
Key highlights7 records
Customer concentration
Milvus social profiles
Digital presenceMilvus financial estimates
Financial estimateRevenue estimate
Valuation estimate
Milvus leadership team
Management profileNumber of profiles
Milvus subsidiaries and ownership
Company hierarchySubsidiaries6 records
Milvus funding detail
Funding detailFunding overview
Funding rounds
Investors
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
Milvus M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about Milvus
What does Milvus do?
Milvus is an open-source vector database purpose-built for storing, indexing, and searching high-dimensional vector embeddings used in GenAI applications. It scales elastically to tens of billions of vectors with sub-10ms query latency and is offered through three deployment modes (Lite, Standalone, Distributed) plus Zilliz Cloud, the fully managed commercial service available across AWS, GCP, and Azure.
Is Milvus a public or private company?
Milvus is a private company. It is classified as unknown and is currently operating.
When was Milvus founded?
Milvus was founded in 2017. It employs 1 to 10 people.
Where is Milvus based?
Milvus is headquartered in Redwood City, United States, in the North America region.
How does Milvus make money?
Three revenue lines are on record. Open-source software (free) is the primary driver. The others are zilliz Cloud (managed Milvus) and vector Lakebase (Zilliz Cloud exclusive).
Who are Milvus's main competitors?
Direct peers on record are Pinecone, Weaviate, Qdrant and Chroma. Broad incumbents are Elasticsearch (Elastic) and MongoDB. Emerging players are Vespa.ai, pgvector, LanceDB and Marqo.
Does Milvus have an API?
Yes. Milvus provides REST and gRPC APIs with client libraries available in Python, Java, Go, Node.js, and C#. The Python SDK (pymilvus) includes Milvus Lite, a vectorDB-as-a-library that runs in notebooks and laptops via pip install. All deployment modes (Milvus Lite, Standalone, Distributed) share the same API, allowing client-side code to work across different deployment configurations. Developer documentation is at milvus.io/docs.
What industry is Milvus in?
Milvus's product category is Vector Database. Its primary akta.pro industry code is BPAEADAL, Data Security & Privacy Managed Services (DLP/Encryption), with a secondary code of BPAEADAB, Security Operations Center (SOC) as a Service. Its NAICS code is 5182 and its SIC code is 7370.