Pinecone

Pinecone is the most widely adopted fully managed vector database, purpose-built for storing and querying high-dimensional vector embeddings at scale. It is the default choice for teams building retrieval-augmented generation (RAG) systems, semantic search, and recommendation engines who want zero infrastructure management.

• RAG applications — store document embeddings and retrieve the most relevant context to ground LLM responses in your own data
• Semantic search — find similar items by meaning rather than keywords, across text, images, or any embedded content
• Hybrid search — combine vector similarity with keyword (sparse) search for better retrieval accuracy
• Metadata filtering — filter results by metadata fields (category, date, user_id) alongside vector similarity for precise, scoped retrieval
• Namespaces — partition data within a single index for multi-tenant applications without creating separate indexes
• Real-time updates — upsert and delete vectors instantly with no reindexing lag

Backend engineers, AI application developers, and product teams use Pinecone because it eliminates the operational overhead of running a vector database — no cluster management, no index tuning, no replication configuration. You send vectors via API, and Pinecone handles availability, scaling, and performance.

Pinecone integrates natively with LangChain, LlamaIndex, OpenAI, and most embedding providers, making it the path of least resistance for adding vector search to any AI application.

1. Create an account — sign up at pinecone.io and get your API key from the Pinecone Console.
2. Install the Python client:

   pip install pinecone

3. Create an index:

   from pinecone import Pinecone
   
   pc = Pinecone(api_key='YOUR_API_KEY')
   pc.create_index(
       name='my-index',
       dimension=1536,  # Match your embedding model's dimension
       metric='cosine',
       spec=ServerlessSpec(cloud='aws', region='us-east-1')
   )

4. Upsert vectors:

   index = pc.Index('my-index')
   index.upsert(vectors=[
       {'id': 'doc1', 'values': [0.1, 0.2, ...], 'metadata': {'source': 'docs'}},
       {'id': 'doc2', 'values': [0.3, 0.4, ...], 'metadata': {'source': 'faq'}}
   ])

5. Query by similarity:

   results = index.query(
       vector=[0.1, 0.2, ...],
       top_k=5,
       include_metadata=True,
       filter={'source': {'$eq': 'docs'}}
   )

Pricing: Free tier (Starter): 1 index, 100K vectors, no credit card required. Standard: serverless pricing based on storage (~$0.33/GB/month) and read/write units. Enterprise: dedicated infrastructure with SLAs. Full pricing details.