Pinecone: the vector database powering your AI projects
Discover Pinecone, the managed vector database for AI
Pinecone is a managed vector database that stores and searches embeddings at scale, connecting AI models to your company's knowledge.
In practice: your documents, tickets and product sheets are converted into vectors, and Pinecone retrieves the content closest to any question in milliseconds. That is what allows an AI assistant to answer from your internal documentation instead of making things up.
Pinecone is built around four pillars:
From vector storage to production operations, everything is managed for you.
1. Managed vector storage: zero infrastructure to run
Pinecone handles the hosting, indexing and availability of your vectors.
- Store millions of vectors without sizing any servers
- Ingest your embeddings through a simple API, from any pipeline
- Organise your data with indexes and namespaces to isolate use cases
- Attach metadata to every vector for more precise searches
2. Semantic search: find by meaning, not keywords
Vector search retrieves content close to an intent, even when phrased differently.
- Query your knowledge base in natural language and get the most relevant passages
- Filter results by metadata: document type, date, customer scope
- Combine vector and keyword search to sharpen relevance
- Get answers in milliseconds, even on large volumes
3. The foundation of RAG: connect LLMs to your knowledge
Pinecone is a core component of RAG (Retrieval-Augmented Generation) architectures.
- Feed your AI assistants with the documents that actually matter
- Reduce fabricated answers by grounding the model in your sources
- Update the assistant's knowledge by re-indexing your content
- Integrate Pinecone with mainstream frameworks such as LangChain or LlamaIndex
4. Production operations: performance and security
Built for production, the platform avoids nasty surprises when traffic grows.
- Absorb traffic spikes with the serverless architecture
- Pay for actual usage rather than reserved capacity
- Secure access with API keys and data encryption
- Monitor your indexes and performance from the console
What about the alternatives?
The vector database market is busy: Qdrant and Weaviate play the open source card, and the vector extensions of PostgreSQL are sometimes enough to get started. Pinecone stands out with its fully managed approach.
Planning a RAG or semantic search project? turnK designs your architecture, from the ingestion pipeline to the final assistant. Take a look at our Data & AI services or contact us: our consultants reply within 48 hours.
Les questions les plus fréquentes
Pinecone is a managed vector database built for AI applications. It stores and searches embeddings at scale, enabling semantic search and powering the RAG architectures used by enterprise AI assistants.
Data and development teams building AI applications: internal assistants, document search, recommendation systems. Its managed model is particularly suited to teams that do not want to operate vector search infrastructure themselves.
Fully managed vector storage, fast semantic search with metadata filtering, integration with RAG frameworks such as LangChain and LlamaIndex, and a serverless architecture that scales with demand.
Pinecone offers a free tier to get started and usage-based billing, depending on the volume of data stored and queries performed. Dedicated plans exist for enterprise needs; up-to-date pricing is available on the official website.
Qdrant and Weaviate on the open source side, or PostgreSQL's pgvector extension for modest volumes. The right choice depends on your volumes, hosting constraints and in-house skills: turnK helps you decide and integrates the chosen solution.
