GitFounders
About Weaviate
Weaviate stores both data objects and their associated vector representations, supporting hybrid search queries that combine keyword filtering with vector similarity. Its cloud‐native architecture ensures fault tolerance and scalability, while extensive integrations with popular language model frameworks and embedding APIs streamline the development of generative AI and semantic search applications. Developer-focused APIs, thorough documentation, and a vibrant community further simplify the journey from prototype to production.
Key Features
- Open‐source, AI‐native vector database
- Hybrid search combining vector and structured filtering
- Cloud‐native with fault tolerance and scalability
- Extensive integrations with LLM frameworks and embedding modules
- Developer-friendly with comprehensive documentation and strong community support
Pricing
Ideal for teams building and prototyping AI applications with scalable, usage-based billing.
- ✓Starting at $25/mo
- ✓$0.095 per 1M vector dimensions stored
- ✓Flexible pay-as-you-go pricing
- ✓Free trial available for prototyping
Optimized for large-scale, enterprise-level deployments with dedicated resources and comprehensive support.
- ✓Dedicated resources and custom SLAs
- ✓High-performance for large-scale production
- ✓Flexible storage and SLA tiers
- ✓Tailored for extensive production workloads
Summary
Weaviate transforms data storage by seamlessly merging vector search with structured filtering in a cloud-native environment. Its robust integrations, scalable performance, and developer-centric design empower teams to rapidly build and deploy next-generation AI applications.
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