Pinecone
🛠️ Developer Toolsfreemium
★4.3
Vector database for AI applications
databasevectorssearch
Try Pinecone →Use Cases
- •Build semantic search for large document repositories using vector embeddings
- •Power retrieval-augmented generation (RAG) pipelines for LLM applications
- •Create recommendation engines based on similarity matching of user behavior vectors
Integrations
LangChainLlamaIndexAWS / Azure / GCP MarketplaceHaystack
Pros
- +Fully managed serverless infrastructure with no ops overhead
- +Multi-cloud support across AWS, Azure, and GCP
- +Excellent developer experience with well-documented SDKs in Python, Node, Go, and Java
Cons
- -Costs can escalate quickly at scale due to per-read-unit and per-write-unit pricing
- -Starter plan is limited to a single AWS region (us-east-1)
- -Vendor lock-in since data is stored in a proprietary format, unlike open-source alternatives like Weaviate or Milvus
Quick Start
1. Go to pinecone.io and sign up for a free Starter account
2. Create a new serverless index, choosing a dimension size matching your embedding model (e.g., 1536 for OpenAI)
3. Install the client library with `pip install pinecone` or `npm install @pinecone-database/pinecone`
4. Connect using your API key and upsert vector embeddings into your index
5. Query the index with a vector to retrieve the most similar results
Pricing
Free (Starter): 2 GB storage, 2M write units/mo, 1M read units/mo, up to 5 indexes, AWS us-east-1 only. Standard: $50/mo minimum (includes $15 usage credits), available on AWS/Azure/GCP, storage at $0.33/GB/mo. Enterprise: $500/mo minimum, HIPAA compliant, SSO, audit logs. Dedicated (BYOC): Custom pricing, runs in your own cloud VPC.