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Weaviate

Weaviate is a cloud-native, real-time vector database that allows you to bring your machine-learning models to scale. There are extensions for specific use cases, such as semantic search, plugins to integrate Weaviate in any application of your choice, and a console to visualize your data.

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Founded

2019

Location

Amsterdam, North Holland

Employees

119

Funding

$67M Series B

Weaviate: AI‑Native Vector Database for RAG, Search, and Multimodal Retrieval

Overview

Weaviate is an open‑source, AI‑native vector database built for high‑performance search, retrieval, and RAG. It stores objects and embeddings, supports hybrid keyword-plus-vector search, and scales from prototypes to billion‑vector production systems. The company is headquartered in Amsterdam and led by co‑founder and CEO Bob van Luijt, with a managed cloud (WCS) alongside self‑hosted options.

  • Website: [Weaviate](https://weaviate.io/)
  • Product overview: [Platform](https://weaviate.io/platform)
  • Documentation: [Docs](https://docs.weaviate.io/weaviate)
  • Pricing: [Pricing page](https://weaviate.io/pricing)
  • About the company: [About Weaviate](https://weaviate.io/company/about-us)
  • Weaviate fits cleanly into modern AI stacks for LLM applications, semantic enterprise search, and multimodal search, with integrations for LangChain, LlamaIndex, and model providers like OpenAI and Cohere.

    Quick Facts

  • Category: [AI‑native vector database](https://weaviate.io/platform)
  • Founded: 2019; HQ: Amsterdam, Netherlands
  • Leadership: CEO Bob van Luijt; CTO and co‑founder Etienne Dilocker
  • Open source with managed cloud option: [Weaviate Cloud](https://weaviate.io/platform)
  • SDKs: Python, JavaScript/TypeScript, Go
  • Social proof: ~42k+ LinkedIn followers, 100+ employees
  • Funding: $50M Series B led by Index Ventures (2023)
  • Core Capabilities

  • Vector and hybrid search with keyword scoring and filters for structured attributes
  • Built‑in vectorizers and reranking modules; plug‑and‑play with popular LLM toolchains for RAG
  • Production reliability: sharding, clustering, and typed schema with filters
  • Deploy anywhere: managed cloud, Kubernetes, or bare metal
  • Scales from prototype to billion‑vector clusters; designed for low‑latency retrieval
  • Developer experience: clean APIs, strong docs, and language clients
  • Common Workloads and Use Cases

  • RAG for documents, chatbots, and support portals
  • Enterprise and site search with hybrid keyword + semantic ranking
  • Personalization and recommendations
  • Multimodal search across text and images
  • Knowledge bases unifying structured and unstructured data
  • Learn more: [Enterprise use cases](https://weaviate.io/blog/enterprise-use-cases-weaviate)
  • Who It’s For

  • AI engineers building RAG and agentic systems needing fast, scalable retrieval
  • Platform teams standardizing semantic search across applications
  • Data/ML teams adding vector search to existing services
  • Startups prototyping quickly, then scaling to production
  • Integrations and Ecosystem

  • SDKs: Python, JavaScript/TypeScript, Go
  • Frameworks: LangChain, LlamaIndex, Haystack
  • Model providers and vectorizers: OpenAI, Cohere, Hugging Face; reranking modules available
  • Connectors and ingestion: ETL options and connectors (e.g., Airbyte)
  • Deployment Options

  • Managed cloud with auto‑scaling and reduced ops burden
  • Self‑hosted on Kubernetes or bare metal with full control
  • Pricing Snapshot

  • Managed cloud from $25/month with pay‑as‑you‑go dimensions (e.g., $0.095 per 1M vector dimensions stored per month). Check the page for current rates and tiers: [Pricing](https://weaviate.io/pricing).
  • Open source core is free to self‑host.
  • Free trial/entry tiers may be available; see [Pricing](https://weaviate.io/pricing) for the latest.
  • Developer Experience

  • Fast start with language clients and copy‑paste examples: [Quickstart](https://docs.weaviate.io/weaviate/quickstart)
  • Built‑in vectorization, hybrid scoring, and filters reduce glue code
  • RAG‑ready: integrates with LangChain/LlamaIndex and popular embedding/reranking models
  • Architecture and Features (At a Glance)

  • Schema‑based data modeling with data types and metadata filters
  • Sharding and clustering for horizontal scale
  • Hybrid BM25 + vector search; reranking for improved relevance
  • Multimodal support (text, images) and plugin‑style vectorizers
  • Observability and ops features in managed cloud
  • Community and Market Feedback

    Pros (from user sentiment):

  • Easy to integrate; strong docs and examples
  • Clean APIs and helpful developer experience; quick path to semantic search
  • Strong vector + hybrid search focus
  • Helpful support and community
  • Cons (from user sentiment):

  • Self‑hosting can add ops overhead and tuning at scale
  • Mixed feedback on replication/consistency vs alternatives
  • Some users find cluster setup/management tougher than hosted competitors
  • Benchmarks and latency vary by embedding flow and settings
  • Why Teams Choose Weaviate

  • Open‑source flexibility with a production‑ready managed cloud
  • Developer‑friendly APIs and quickstarts for faster time‑to‑value
  • Hybrid search, filters, and reranking for high‑quality relevance
  • Proven at scale for RAG, enterprise search, and multimodal retrieval
  • Getting Started

  • Explore the product: [Platform](https://weaviate.io/platform)
  • Try it quickly: [Quickstart (Python/JS/Go)](https://docs.weaviate.io/weaviate/quickstart)
  • Evaluate costs: [Pricing](https://weaviate.io/pricing)
  • Learn about the team: [About Weaviate](https://weaviate.io/company/about-us)
  • See reviews: [G2 Weaviate page](https://www.g2.com/products/weaviate/reviews)
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