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.
Weaviate is an open-source vector DB with built-in hybrid search (BM25 + vector), automatic vectorization via modules (OpenAI/Cohere/HuggingFace), multi-tenancy, and GraphQL/gRPC APIs. Self-host or Weaviate Cloud.
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
When each option wins, what it costs, and its biggest gotcha.
| Alternative | Best For | Cost Signal | Biggest Gotcha |
|---|---|---|---|
| Pinecone | Fully managed, simplest API, strong SLA | Serverless ~$0.33/GB + queries | Cloud-only; harder for data residency |
| Qdrant | Rust perf, strong payload filtering, OSS | Free OSS; Cloud $0.05/hr+ | Less built-in vectorization/generative tooling |
| pgvector | Already on Postgres, simple RAG needs | DB infra only | Slower hybrid search, tuning HNSW harder |
| Milvus | Massive scale (billions of vectors), Kubernetes-native | Free OSS; Zilliz Cloud usage | Complex ops, overkill for smaller datasets |
Weaviate Cloud starts ~$25/mo (Sandbox) with tiers that scale by vector count and SLA; a production cluster for 10M vectors typically ~$300-800/mo. Self-host on 3-node cluster ~$400-1,000/mo infra + $500-2K/mo ops time. Pinecone equivalent ~$100-500/mo for similar scale. Break-even self-host vs Pinecone: around 50M+ vectors or strict data residency. Build cost for Weaviate RAG ~$30-60K; savings compound vs rebuilding hybrid retrieval from scratch (~$50-80K).
Specific production failures that have tripped up real teams.
Adding a new property with vectorization across a 10M-doc collection re-embeds everything—costly and slow; design schemas carefully upfront.
The alpha (vector vs BM25 weight) interacts with query characteristics; a single global value often underperforms per-query tuning.
Tens of thousands of tenants cause resource bloat; plan shard/tenant lifecycles explicitly.
If OpenAI embedding API is down, writes fail—consider pre-embedding or fallback embedders.
Production gRPC timeouts produce cryptic errors; enable verbose logs and trace correlation IDs.
We say this out loud because lying to close a lead always backfires.
Self-hosted Weaviate in production needs careful tuning and observability; use managed Weaviate Cloud or Pinecone if ops capacity is thin.
HNSW index updates have overhead; pure key-value or specialized stores handle faster single-record writes.
pgvector is cheaper and simpler at that scale; Weaviate shines past 10M+ or when hybrid search matters.
If your team already knows Pinecone well, switching costs real migration time.