For AI engineers building retrieval and RAG systems, Zilliz is a vector database company that offers Zilliz Cloud, a fully managed version of Milvus, the open-source database it created, for storing and searching billions of embeddings at scale.

Presence & Market Position

SOV Score

Tracked · below panel floor — earned mentions are accruing toward the next scored window.

Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →

Zilliz is positioning itself as the production data layer for retrieval, RAG, and agent-memory systems that need vector search at scale. Its product agenda centers on Vector Lakebase, which combines vector, semantic, hybrid, full-text, and geospatial search in a lake-native architecture. Broader GTM conversation remains early so far. The company’s current narrative stays technical, concentrating on production AI workloads, low-latency search over active data, access to colder data, and the infrastructure required to keep model context connected to large information stores.

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Customers

Nvidia, Salesforce, Walmart, PayPal, Reddit, Airtable, Exa, MiniMax, OpenEvidence, Filevine

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Business Profile

Founded in 2017, Zilliz is an independent, 139-person vector-database company with $113 million in funding and $16.2 million in reported 2023 revenue. The business grew around Milvus, the open-source database it created, and Zilliz Cloud, its managed commercial service. Its current expansion into Vector Lakebase connects production vector search with lake-native storage and batch workflows, keeping the cloud business closely tied to the technical ecosystem built around Milvus.

Founded
2017
Employees
139
Funding
$113M
ARR
$16.2M (2023)
Status
Independent
Scale stage
Growth
GTM Categories
GTM Developer ToolsStorage, Memory & Context

Agent Readiness

90A
Agent Readiness
APIMCPSDKCLI

Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →

Zilliz has built a broad public agent surface around Zilliz Cloud. Developers can use REST APIs and Python, Java, Node.js, and Go SDKs, while the MCP Server gives compatible agents standardized access to control-plane and data-plane tools. Its AI tooling also includes reusable skills, a Claude Code plugin, and prompt modules that help coding agents work with schemas, search, and migrations.

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