---
title: "Pixeltable vs Pinecone: embedding indexes vs a managed vector DB"
description: "Pixeltable keeps embeddings on the source row. Pinecone wins managed ANN, namespaces, and SLAs. Not a speed bake-off. The only vector-database compare page we publish."
keywords:
  - Pixeltable vs Pinecone
  - Pinecone alternative
  - vector database comparison
  - embedding index
  - RAG Python
url: "https://pixeltable.com/compare/pixeltable-vs-pinecone"
---

# Pixeltable vs Pinecone

Pinecone is a managed ANN index. Pixeltable is tables, transforms, and embedding indexes on the source rows. You do not need a separate vector database for app-scale RAG. Keep Pinecone when the contract is multi-region vector serving or an existing SLA.

## Summary

### Pixeltable

- Chunking, embeddings, and similarity live on the table
- Insert and delete keep the index current — no upsert job
- Source media stays queryable next to the vector
- HTTP from the same file if you declare FastAPIRouter

### Pinecone

- Managed ANN, namespaces, and a query SLA
- Upsert/query API your serving tier already speaks
- You still run ETL, chunking, and a source-of-truth store
- Integrated inference exists; it is still not a multimodal table

## Comparison

| Feature | Pixeltable | Pinecone |
| --- | --- | --- |
| What it is | Tables + computed columns + EmbeddingIndex | Managed vector index (sparse/dense, namespaces, serverless) |
| Keeping vectors in sync | Index is schema; insert/delete/update recompute the affected rows | You embed and upsert; deletes are a second call |
| Source media | Document, Image, Video, Audio columns next to the index | Vectors plus metadata; bytes live in S3 or a database you add |
| ANN at serving scale | EmbeddingIndex on the catalog; not a hosted billion-vector service | The product: low-latency ANN, replicas, multi-region |
| Ops and SLA | You run local or Pixeltable Cloud; no Pinecone-class query SLA | Managed index, auth, and a vendor SLA |
| Lineage | Computed columns record the expression that produced a value | Metadata you attach; the index does not know the pipeline |
| Glue around it | One schema | Chunker + embedder + orchestrator + the index |

## Document search

Pixeltable: document_splitter view and EmbeddingIndex. Query with similarity(string=), then order_by. Apply with pxt schema update app.py search. Pinecone: embed yourself, upsert, query.

### Pixeltable

```python
import pixeltable as pxt
from pixeltable.functions.document import document_splitter
from pixeltable.functions.huggingface import sentence_transformer

TableModel = pxt.model_base()
embed = sentence_transformer.using(
    model_id='sentence-transformers/all-MiniLM-L6-v2'
)

class Docs(TableModel, name='docs'):
    document: pxt.Document
    title: pxt.String
    category: pxt.String

class Chunks(
    TableModel,
    name='chunks',
    base=Docs,
    iterator=document_splitter(Docs.document, separators='sentence', limit=512),
):
    __indexes__ = [pxt.EmbeddingIndex(text, embedding=embed)]

# pxt schema update app.py search
docs = pxt.get_table('search.docs')
docs.insert([
    {'document': 'papers/ml.pdf', 'title': 'Research Paper', 'category': 'research'},
])
chunks = pxt.get_table('search.chunks')
sim = chunks.text.similarity(string='machine learning algorithms')
chunks.order_by(sim, asc=False).limit(10).select(chunks.text, chunks.title)
```

### Pinecone

```python
from pinecone import Pinecone
from sentence_transformers import SentenceTransformer

pc = Pinecone(api_key='...')
index = pc.Index('docs')
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

def upsert_docs(docs):
    vectors = []
    for doc in docs:
        text = extract_text(doc)  # you still write this
        vectors.append({
            'id': doc['id'],
            'values': model.encode(text).tolist(),
            'metadata': {'title': doc['title']},
        })
    index.upsert(vectors=vectors)

def search_docs(query, top_k=10):
    vector = model.encode(query).tolist()
    return index.query(vector=vector, top_k=top_k, include_metadata=True)
```

## When to choose Pixeltable

- **RAG where the source row matters**: Documents, images, or video plus the embedding. Insert should update the index. You do not want a second upsert path.
- **You were about to buy a vector database for an app**: App-scale retrieval does not require Pinecone. Coming from a vector DB: docs.pixeltable.com/howto/coming-from

## When to choose Pinecone

- **The index is already the serving contract**: Namespaces, metadata filters, and a query SLA your other services already call. Pixeltable can still produce the embeddings.
- **ANN is the binding constraint**: Large-scale, multi-region, or p99 query targets that Pinecone (or Qdrant/Milvus) is built for. This page does not claim Pixeltable wins that bake-off. Weaviate, Chroma, Qdrant, and Milvus are the same job — not separate vs pages.

## FAQ

### Is Pixeltable a Pinecone alternative?

For typical document and multimodal RAG, yes: put EmbeddingIndex on the column and query with similarity(string=). For a dedicated global vector serving tier, no — keep Pinecone.

### Can I export Pixeltable embeddings to Pinecone?

Yes. Pixeltable remains the system of record; upsert vectors if Pinecone is the serving contract. That is complementary, not a requirement.

### What about Weaviate, Qdrant, Chroma, Milvus, pgvector?

Same job as this page. pgvector is already implied by Pixeltable vs Supabase. We do not mint a vs page per vendor.