Pinecone vs Convex for Vector Search

If you're building an AI application that needs vector search, you've probably looked at Pinecone. It's the most well-known dedicated vector database. But there's another option that gets less attention: running vector search directly on Convex as part of your application backend.

This is an honest comparison. We'll cover architecture, pricing, DX, and the tradeoffs. We'll also be upfront: Memcity runs on Convex, so we have a bias. We'll try to be fair about where Pinecone is the better choice.

Architecture: Dedicated vs Integrated

Pinecone: Purpose-Built Vector Database

Pinecone is a standalone managed service. You send it vectors, it stores and indexes them, and you query them over HTTPS.

Your architecture looks like this:

Your App → Your Backend → Pinecone (vectors)
                        → Your Database (metadata)
                        → Your Storage (documents)

Every search requires a round-trip to Pinecone's API. Metadata lives in your primary database. The actual document content lives in your storage layer. Your backend orchestrates across all three.

Pros:

• Purpose-built for vector operations -- highly optimized
• Scales to billions of vectors
• Supports multiple index types (dense, sparse, hybrid)
• Mature ecosystem, lots of integrations

Cons:

• Another service to manage (billing, API keys, monitoring)
• Cross-service latency for every query
• Metadata and vectors are split across systems
• No built-in RAG pipeline -- you build the orchestration

Convex: Vectors + Application in One

Convex is a full application backend (database, serverless functions, file storage, real-time subscriptions) that also supports vector search natively.

Your architecture looks like this:

Your App → Convex (vectors + metadata + documents + functions)

Everything lives in one system. Vector search, metadata queries, document storage, and backend logic all run in the same deployment. No cross-service calls.

Pros:

• Zero network hops between vector search and your data
• Transactions across vectors and metadata are atomic
• Real-time subscriptions (search results update live)
• One bill, one dashboard, one auth system

Cons:

• Younger vector search implementation than Pinecone
• Not designed for billion-scale vector-only workloads
• Convex-specific (not a general-purpose vector DB)

Developer Experience

Pinecone

import pinecone
 
# Initialize
pc = pinecone.Pinecone(api_key="your-key")
index = pc.Index("my-index")
 
# Upsert vectors (you generate embeddings separately)
index.upsert(vectors=[
    {"id": "doc1", "values": embedding, "metadata": {"source": "policy.md"}},
])
 
# Query
results = index.query(vector=query_embedding, top_k=10)

Pinecone is a vector database. You bring your own embeddings, your own chunking logic, your own reranking, and your own RAG pipeline. It does one thing (vector storage and search) and does it well.

Convex + Memcity

import { memory } from "./memory";
 
// Ingest (chunking + embedding + indexing handled automatically)
await memory.ingestText(ctx, {
  orgId: "org_123",
  knowledgeBaseId: "kb_456",
  text: "Your document content...",
  source: "policy.md",
});
 
// Search (full 16-step RAG pipeline)
const results = await memory.getContext(ctx, {
  orgId: "org_123",
  knowledgeBaseId: "kb_456",
  query: "How does the refund policy work?",
});

Memcity handles the full pipeline: chunking, embedding (Jina v4), BM25 indexing, hybrid search, RRF fusion, reranking, knowledge graph traversal, and more. You pass in text and get back search results.

Key difference: Pinecone is a building block. Convex + Memcity is a complete solution. Whether that's better depends on how much control you want vs. how much you want handled for you.

Pricing

Pinecone

Pinecone charges based on pod type and storage:

Serverless pricing (2024): ~$0.002 per 1K queries + storage costs.

The catch: Pinecone only stores vectors. You still need a database for metadata, a storage service for documents, compute for your RAG pipeline, and an embedding API. The total cost is Pinecone + your database + your compute + your embedding API.

Convex + Memcity

Convex pricing is usage-based (functions, database, bandwidth). Memcity tiers:

Memcity is a one-time license, not recurring. You pay Convex usage costs (which include database, compute, and vector search) and embedding API costs (Jina).

For small-to-medium workloads (under 1M vectors), Convex + Memcity is typically cheaper because there's no recurring vector database bill and you eliminate the separate database and compute costs.

For large-scale vector-only workloads (100M+ vectors), Pinecone's infrastructure is purpose-built for that scale.

Feature Comparison

When to Use Pinecone

Pinecone is the better choice when:

• You need billion-scale vector search. Pinecone is built for massive vector workloads. If you're indexing the entire internet or have 100M+ vectors, Pinecone's infrastructure is designed for this.

• You already have a backend you like. If your backend is Django, Rails, or a custom Node server and you just need a vector search API, adding Pinecone is simpler than migrating to Convex.

• You want maximum control. Pinecone gives you a primitive (vector search) and you build everything else. If you have a team that wants to build custom chunking, custom reranking, custom pipelines -- Pinecone gets out of your way.

• You need multi-cloud or on-prem. Pinecone runs on AWS, GCP, and Azure. Convex runs on Convex cloud.

When to Use Convex + Memcity

Convex + Memcity is the better choice when:

• You're building on Convex. If you're already using Convex (or starting a new project), adding Memcity gives you a full RAG pipeline with zero additional infrastructure.

• You want a complete solution. Chunking, embedding, hybrid search, reranking, knowledge graph, episodic memory, access control, audit logging -- all in one component. No glue code.

• You care about real-time. Convex's reactive subscriptions mean search results can update in real-time as new documents are ingested. Pinecone requires polling.

• You want to own the source code. Memcity installs as source code. You can read, modify, and customize every step of the pipeline. Pinecone is a black box.

• Your scale is under 1M vectors. For most SaaS products, internal tools, and documentation search, this is more than enough. And you save the cost and complexity of a separate vector database.

The Bottom Line

Pinecone is a best-in-class vector database. If you need a standalone vector search API, it's hard to beat.

But vector search alone isn't enough for a production AI application. You need chunking, embedding, hybrid search, reranking, access control, and orchestration. Pinecone gives you one piece of the puzzle. Convex + Memcity gives you the whole picture.

If you're building on Convex, try Memcity:

npx shadcn@latest add @memcity/community

Full documentation at memcity.dev/docs.