List of Milvus Vector Database Customers
Redwood Shores, 94065, CA,
United States
Since 2010, our global team of researchers has been studying Milvus Vector Database customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Milvus Vector Database for AI Database from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Milvus Vector Database for AI Database include: Walmart, a United States based Retail organisation with 2100000 employees and revenues of $681.00 billion, Shopee, a Singapore based Communications organisation with 40000 employees and revenues of $5.60 billion, Notta, a Japan based Construction and Real Estate organisation with 100 employees and revenues of $10.0 million and many others.
Contact us if you need a completed and verified list of companies using Milvus Vector Database, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Milvus Vector Database customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
Apply Filters For Customers
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
|---|---|---|---|---|---|---|---|---|---|---|---|
 |
Notta | Construction and Real Estate | 100 | $10M | Japan | Milvus IO | Milvus Vector Database | AI Database | 2023 | n/a |
In 2023, Notta deployed Milvus Vector Database on Zilliz Cloud to power its meeting-intelligence semantic search, implementing an AI Database layer for transcript indexing and Q&A. The Milvus Vector Database was used to underpin retrieval augmented generation workflows that surface contextual answers and semantic search results from meeting transcripts.
The implementation stores dense embeddings derived from voice transcripts and serves RAG-powered features for transcripts and question and answer functionality, with vector indexes and real-time query paths hosted on Zilliz Cloud. Engineering configured the vector search topology to handle high-throughput inference, and optimized query execution to reduce end-to-end search latency from approximately 1 second to about 100 milliseconds.
Operational scope focused on meeting-intelligence capabilities within Notta’s product, specifically transcript storage, semantic retrieval, and Q&A experience. The deployment supported large-scale ingestion and storage of embeddings for tens of millions of hours of voice data, enabling semantic retrieval at application scale without introducing new named system dependencies.
Governance and operational impact included reduced technical overhead for engineering teams, enabling product teams to concentrate on feature development rather than index maintenance. Notta reported about a 10x latency improvement from ~1s to ~100ms and a decrease in operational complexity following the Milvus Vector Database deployment.
|
 |
Shopee | Communications | 40000 | $5.6B | Singapore | Milvus IO | Milvus Vector Database | AI Database | 2021 | n/a |
In 2021, Shopee deployed Milvus Vector Database as an AI Database to power its Multimedia Understanding systems. The implementation targeted video recall, copyright matching, and deduplication to scale short-video features and recommendations across Southeast Asia.
The deployment used Milvus Vector Database as the operational vector index for embedding retrieval, supporting real-time recall and similarity search within MMU pipelines. Functional capabilities implemented included nearest neighbor search for video recall, similarity matching for copyright detection, and deduplication workflows orchestrated through Multimedia Understanding pipelines. Architecturally Milvus Vector Database served as the scale-out vector store, enabling embedding storage, indexing, and online similarity queries to feed recommendation models and content integrity checks.
Milvus Vector Database was integrated into Shopees Multimedia Understanding and recommendation stacks to enable real-time video recall and copyright and deduplication workflows. The deployment reduced retrieval latency and increased availability for these real-time workflows, improving recommendation quality and content integrity. Operational scope encompassed Multimedia Understanding and recommendation workflows across Shopees Southeast Asia operations, supporting the scaling of short-video features.
|
 |
Walmart | Retail | 2100000 | $681.0B | United States | Milvus IO | Milvus Vector Database | AI Database | 2022 | n/a |
In 2022, Walmart deployed Milvus Vector Database for internal AI applications including semantic product search to improve relevance and discovery across its product catalog and internal services. The Milvus Vector Database implementation targeted high-scale vector search use cases to support both customer facing search and internal retrieval workflows.
Implementation focused on vector indexing, approximate nearest neighbor search, and semantic embedding retrieval, aligning with standard AI Database capabilities for similarity search and dense retrieval. Milvus Vector Database was configured and operationalized within Walmart's platform team environment, providing a reusable vector search layer and supporting retrieval driven features across services. The configuration emphasized serving dense vector queries and embedding based ranking to improve relevance for product discovery.
Integration into platform team workflows centralized vector search infrastructure and standardized access for product, catalog, and internal engineering teams to consume vector retrieval as a platform service. Operational scope covered product catalog search and internal services, with the deployment designed to handle high throughput vector search demands. Stated outcomes included improved search accuracy and enabling richer retrieval driven features for customers and internal teams.
|
Buyer Intent: Companies Evaluating Milvus Vector Database
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
|---|---|---|---|---|---|---|---|---|
| No data found | ||||||||