Recall vs Pinecone

Overview

Recall and Pinecone serve different roles in the AI stack, though both involve vectors. Understanding where each fits helps avoid false choices.

Recall is a memory layer built into agent runtimes. It stores agent context (conversation history, decisions, outcomes) as vectors and retrieves them to inform the next step. Recall is embedded — it runs inside your agent process.

Pinecone is a managed vector database. You send vectors to Pinecone's cloud. It indexes them and serves semantic search over large collections. Pinecone is remote — it's a service you query.

Feature comparison

When to use Recall

• You need memory inside your agent loop — past decisions, outcomes, failed attempts
• You want to avoid network latency and keep data local
• You prefer open-source with embedded control
• You're building multi-turn orchestration or long-horizon reasoning
• You need sub-50ms retrieval for real-time agent planning

When to use Pinecone

• You're building a semantic search service (customer documents, knowledge bases)
• You have millions of vectors to index and serve
• You want managed infrastructure without ops overhead
• Network latency to a remote index isn't a blocker
• You need Pinecone's filtering and metadata features at scale

Integration patterns

Recall + Pinecone together: Use Recall for agent memory, Pinecone for a separate knowledge base. Agent memory feeds agent loops; Pinecone handles document search queries.

Example: RAG agent that uses Recall for conversation history and Pinecone to search a document store.

// Agent memory
const memory = new Recall({
  name: "conversationMemory",
});

// Document search (remote)
const documents = await pinecone.query({
  vector: queryEmbedding,
  topK: 5,
});

// Agent combines both
const context = {
  recentHistory: await memory.retrieve(),
  documents: documents,
};

Pricing & scaling

Recall open source: Free forever. Self-host or embed in your app.

Recall Cloud: Pay per vector stored + retrieved. Typically $0.001 per 1K vectors/month + query costs.

Pinecone: Pay per vector stored + per query. Standard indexes: $0.096 per 1M vectors/month +$0.096 per 1M queries.

For agent memory: Recall is cheaper at <1M vectors/agent. For large document corpora: Pinecone's infrastructure is optimized.

Summary

Recall and Pinecone answer different questions:

• Recall: How do I embed memory into my agent's decision loop?
• Pinecone: How do I serve semantic search over a large corpus?

Most production agents use both: Recall for agent context, Pinecone (or similar) for external knowledge. Choose based on where your data lives and how tight your latency requirements are.