Business Profile
Weaviate provides a feature-rich AI-native vector database that helps developers build AI-powered applications with reduced hallucination, data leakage, and vendor lock-in.
AI engineers, developers, and data teams building AI-powered applications; enterprises seeking scalable, secure deployment options.
Agentic AI with Query Agent that acts as a domain expert, bridging natural language queries with both structured and unstructured data; multi-collection routing, automatic filter construction, semantic and keyword hybrid search, and an embedded embeddings service; open-source foundation with cloud and Kubernetes deployment options.
Not explicitly specified; marketing emphasizes quick-start options (Try Weaviate Cloud, Quickstart tutorials, Colab demos) but exact deployment timelines are not provided in the content.
MetaBuddy used Query Agent to convert siloed wellness data into a conversational coaching experience; engagement increased 3x, trainer analysis time reduced by 60%, with health metrics unified behind a semantic interface.
An AI-native, open-source vector database with built-in vector and hybrid search, support for embeddings, agentic AI capabilities via Query Agent, and flexible deployment options (self-hosted, managed Weaviate Cloud, or Kubernetes in a VPC).
Developers and organizations building AI-native applications that require scalable, secure vector search, RAG capabilities, and agentic data interactions.
Agentic AI that understands data schemas and APIs, decomposes complex queries, and dynamically builds structured and fuzzy searches across multi-collection data, all within an open-source, scalable vector database with enterprise-ready deployment options.
Weaviate can be deployed self-hosted, as a managed service (Weaviate Cloud), or as a Kubernetes package in your VPC; supports multiple model integrations and an embedding service.
Weaviate Embeddings pricing includes Snowflake arctic-embed models: 1.5 = $0.025 per 1M tokens; 2.0 = $0.040 per 1M tokens. General pricing details are referenced but not fully enumerated in the content.
Based on matching: problems solved, target roles, key features, industries
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