Pinecone is the leading managed vector database purpose-built for semantic search at scale. Unlike keyword search (Elasticsearch) that matches exact terms, Pinecone finds results by meaning — understanding that "affordable housing" matches "budget-friendly apartments." It handles...
ZTABS builds semantic search with Pinecone — delivering production-grade solutions backed by 500+ projects and 10+ years of experience. Pinecone is the leading managed vector database purpose-built for semantic search at scale. Unlike keyword search (Elasticsearch) that matches exact terms, Pinecone finds results by meaning — understanding that "affordable housing" matches "budget-friendly apartments." It handles billions of vectors with sub-50ms query latency, automatic scaling, and zero operational overhead. Get a free consultation →
500+
Projects Delivered
4.9/5
Client Rating
10+
Years Experience
Pinecone is a proven choice for semantic search. Our team has delivered hundreds of semantic search projects with Pinecone, and the results speak for themselves.
Pinecone is the leading managed vector database purpose-built for semantic search at scale. Unlike keyword search (Elasticsearch) that matches exact terms, Pinecone finds results by meaning — understanding that "affordable housing" matches "budget-friendly apartments." It handles billions of vectors with sub-50ms query latency, automatic scaling, and zero operational overhead. For any application that needs "find things similar to X" — product discovery, content recommendations, knowledge base search — Pinecone provides the infrastructure. Its native integrations with OpenAI, LangChain, and Vercel AI SDK make it the default choice for AI-powered search.
Find results by semantic similarity, not keyword matching. Users get relevant results even when they use different words than your content.
Query billions of vectors in under 50 milliseconds. Purpose-built indexing algorithms deliver consistent performance as data grows.
Fully managed — no clusters to configure, indexes to tune, or scaling to manage. Pinecone handles everything so you focus on your application.
First-party integrations with OpenAI, LangChain, LlamaIndex, Vercel AI SDK. Add semantic search to your app in hours, not weeks.
Building semantic search with Pinecone?
Our team has delivered hundreds of Pinecone projects. Talk to a senior engineer today.
Schedule a CallTest different embedding models on your actual data before committing. Ada-002 is good generally, but domain-specific models (Cohere for multilingual, BGE for technical content) often perform better for specialized search.
Pinecone has become the go-to choice for semantic search because it balances developer productivity with production performance. The ecosystem maturity means fewer custom solutions and faster time-to-market.
| Layer | Tool |
|---|---|
| Vector Database | Pinecone Serverless |
| Embeddings | OpenAI Ada-002 / Cohere |
| Framework | LangChain / LlamaIndex |
| Backend | Python / Node.js |
| Search UI | React / Next.js |
| Monitoring | Pinecone Console |
Building semantic search with Pinecone follows a straightforward pipeline. First, your content (products, articles, documents, FAQs) is processed through an embedding model (OpenAI Ada-002) to generate vector representations. These vectors are upserted into Pinecone with metadata (category, date, price, tags).
At query time, the user's search query is embedded with the same model, and Pinecone returns the most similar vectors. Metadata filters combine semantic similarity with structured filters — "find similar products under $50 in the electronics category." For RAG applications, Pinecone retrieves relevant context that feeds into the LLM prompt. Namespaces isolate data per tenant for multi-tenant SaaS applications.
Our senior Pinecone engineers have delivered 500+ projects. Get a free consultation with a technical architect.