Pinecone

About

Key Features

• Run similarity search on p2 pods → <10 ms query latency and up to 200 QPS per replica
• Traffic spike during production use → graph-based index sustains higher throughput with lower latency
• Need more capacity mid-day → vertically resize pods (1x/2x/4x/8x) with zero downtime
• Managing multiple datasets → store vectors in Collections and spin up new indexes from them
• Usage fluctuates week to week → pay-for-what-you-use hourly pod sizing adjustments

Use Cases

• A full-stack engineer adding RAG to a product help center so answers come from internal docs
• An e-commerce ML team serving real-time product recommendations without maintaining their own vector store
• A search engineer prototyping semantic search over support tickets and promoting it to production when QPS grows

Try It Like This

1. 1
   Add RAG to a help center

   Create a Pinecone project and API key → embed internal docs with your chosen encoder and upsert vectors into a Collection → query the index for top-k matches and feed results into your LLM prompt to generate answers.

2. 2
   Real-time product recommendations

   Instrument product events to compute item/user embeddings and upsert them to a Pinecone index in real time → run nearest-neighbor queries on p2 pods for low-latency recommendations → serve the nearest items from query results in your recommendation API.

3. 3
   Prototype semantic support search

   Encode a batch of support tickets and create an index from a Collection → run semantic queries from your frontend to retrieve relevant tickets → iterate on index type and replica sizing as QPS grows.

4. 4
   Scale capacity during traffic spikes

   Monitor query latency and throughput metrics in Pinecone's dashboard → vertically resize pods (1x/2x/4x/8x) or add replicas with zero downtime → validate sub-10 ms queries on p2 pods and revert sizing when load subsides to control cost.

5. 5
   Manage multiple datasets with Collections

   Create separate Collections for different datasets (docs, products, tickets) → spin up lightweight indexes from a Collection for specific query patterns → maintain and update vectors centrally while keeping indexes optimized for different use cases.

Pros & Cons

Getting Started

1. 1 Create an account and choose the Starter plan from the Pinecone console.
2. 2 Provision an index (p2 pods if you need low latency) and obtain your API key.
3. 3 Upsert a small batch of embeddings and run your first query to see millisecond results.

Pricing