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Weaviate — Vector Databases · fobi

Live

Coming soon

Agriculture Tech
Autonomy
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Biotech / Synthetic Biology
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Cloud & Edge Computing

Commerce
Cybersecurity
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Weaviate

Weaviate is an open-source vector database that integrates with machine learning models for semantic search and AI applications. Raised $50M Series B in 2023. Open-source first with a managed cloud offering. Strong positioning in European AI ecosystem.

The leading open-source vector database with significant community adoption, offering hybrid search and multi-modal embedding support.

weaviate.io Vector Databases

Netherlandsprivate

At a glance

Total raised

$66M

last round series b

Headcount

51-200

approximate · public range

Headquarters

Amsterdam

Netherlands

Founded

2019

7 years active

Status

Private

Vector Databases

Latest valuation

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Date	Stage	Amount	Valuation	Lead investors
Apr 21, 2023	Series B	$50M	$200M	Index Ventures
Feb 1, 2022	Series A	$16M	—

AI bull / bear

Bull case

Weaviate's open-source-first strategy gives it a distribution advantage that proprietary rivals like Pinecone cannot match. Developers adopt the free community edition, build applications, and graduate to the managed cloud offering as they scale. This funnel model worked for MongoDB and Elastic, and vector databases follow the same infrastructure adoption curve. Weaviate's hybrid search engine combining vector similarity with keyword and scalar filtering is a genuine technical differentiator for production AI workloads that need precision, not just semantic matches. The $50M Series B in 2023, while not disclosed in total context, signals solid investor conviction at a time when AI infrastructure funding was concentrated. Weaviate's position in the European AI ecosystem provides insulation from US-centric talent wars and cloud-platform lock-in, while still serving a global customer base. As enterprises move from LLM experiments to deployed RAG pipelines, Weaviate's multi-modal embedding support and Kubernetes-native architecture make it a natural pick for teams that want database independence from Big Three cloud providers.

Bear case

Vector databases are rapidly being commoditized. Every major cloud provider has embedded vector search into their existing database services, and PostgreSQL with pgvector offers a "good enough" alternative that eliminates the need for a standalone vector database entirely. Weaviate's open-source model creates a monetization problem: the community edition is too capable, reducing urgency to upgrade to the managed cloud tier, and enterprise customers who do pay face cheaper alternatives from hyperscalers that bundle vector search at near-zero marginal cost. The competitive landscape is brutally crowded with Qdrant, Milvus, Pinecone, and Chroma all chasing the same developer mindshare, and the lack of disclosed funding history suggests Weaviate may be capital-constrained relative to peers. As the market consolidates, standalone vector databases risk being absorbed as features within broader data platforms, leaving Weaviate without a defensible long-term moat beyond its current community momentum.

Generated by deepseek-v4-flash · June 2, 2026Refreshes quarterly · see disclosures

Weaviate

At a glance

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Funding history

How they make money

Founder backgrounds

Bob van Luijt

Etienne Dilocker

AI bull / bear

In the news

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Leadership

Products

Weaviate Vector Database

Weaviate Cloud

Open roles

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