We build applications powered by Weaviate — the open-source vector database with built-in hybrid search, automatic vectorization, and generative modules. From RAG pipelines and semantic search to recommendation engines and multimodal search, Weaviate provides the foundation for AI applications that understand meaning, not just keywords.
We build applications powered by Weaviate — the open-source vector database with built-in hybrid search, automatic vectorization, and generative modules. From RAG pipelines and semantic search to recommendation engines and multimodal search, Weaviate provides the foundation for AI applications that understand meaning, not just keywords.
Key capabilities and advantages that make Weaviate Vector Database Development the right choice for your project
Combine vector semantic search with BM25 keyword search in a single query — getting the best of both approaches for higher accuracy than either alone.
Built-in vectorization modules for text, images, and multimodal content — no separate embedding pipeline needed. Supports OpenAI, Cohere, Hugging Face, and custom models.
Built-in RAG modules that retrieve relevant objects and pass them directly to an LLM for answer generation — all in a single query.
Native multi-tenancy support for SaaS applications — isolate data by tenant with efficient resource sharing and per-tenant access control.
Discover how Weaviate Vector Database Development can transform your business
Build retrieval-augmented generation systems that answer questions from your documents, knowledge base, or product catalog with Weaviate as the vector store.
Replace keyword search with semantic understanding — customers search by meaning, not exact terms, finding relevant products even with non-standard queries.
Build content and product recommendation systems powered by vector similarity — finding items similar in meaning, style, or user behavior patterns.
Real numbers that demonstrate the power of Weaviate Vector Database Development
GitHub Stars
Open-source community adoption
+50% YoY
Query Latency
P95 query latency at million-scale datasets
Consistently fast
Vector Dimensions
Maximum supported vector dimensions
Supports any model
Our proven approach to delivering successful Weaviate Vector Database Development projects
Design the Weaviate schema — classes, properties, vectorizer modules, and cross-references optimized for your query patterns.
Build ingestion pipelines that chunk, vectorize, and load your data into Weaviate with proper metadata and cross-references.
Implement search APIs, RAG pipelines, and application logic using Weaviate's GraphQL API and generative modules.
Deploy on Weaviate Cloud or self-hosted infrastructure with monitoring, backup, and scaling configurations.
Find answers to common questions about Weaviate Vector Database Development
Weaviate offers built-in hybrid search, auto-vectorization, and generative modules — reducing pipeline complexity. Pinecone is simpler for pure vector search. Choose Weaviate when you need hybrid search, multimodal capabilities, or want to self-host. Choose Pinecone for simplicity and fully managed serverless.
Let's discuss how we can help you achieve your goals