---
title: "Pixeltable vs Weaviate: index on the row vs hybrid search"
description: "Weaviate wins hybrid BM25 plus dense search. Pixeltable keeps embeddings on the source row. Keep Weaviate for that engine. Last reviewed Oct 2026."
keywords:
  - Pixeltable vs Weaviate
  - Weaviate alternative
  - hybrid search
  - vector database comparison
  - pinecone alternatives
url: "https://www.pixeltable.com/compare/pixeltable-vs-weaviate"
---

# Pixeltable vs Weaviate

Weaviate is a vector database with collections, named vectors, and hybrid search that fuses BM25 with dense similarity. Pixeltable is not a vector database: it declares EmbeddingIndex on the table that holds the source file. Keep Weaviate when that engine is the serving contract.

## 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

### Weaviate

- Open-source vector database; Weaviate Cloud is the hosted option
- Hybrid search: BM25 fused with dense similarity (alpha balances the two)
- Collections, named vectors, and vector_config=Configure.Vectors.*
- You still chunk files, store bytes, and put HTTP in front

## Comparison

| Feature | Pixeltable | Weaviate |
| --- | --- | --- |
| What it is | Tables + computed columns + EmbeddingIndex | Vector database of objects and vectors (hybrid search) |
| Hybrid BM25 + dense | Dense similarity on a column; keyword filters are table queries | query.hybrid fuses BM25 and dense (alpha, fusion_type) |
| Keeping vectors in sync | Index is schema; insert/delete/update recompute the affected rows | Insert into the collection; you still embed or configure a vectorizer |
| Source media | Document, Image, Video, Audio columns next to the index | Properties plus vectors; bytes live in object storage you add |
| ANN / serving scale | EmbeddingIndex on the catalog; not a dedicated ANN cluster | The product: HNSW, replicas, Weaviate Cloud |
| Glue around it | One schema | Chunker + embedder (or vectorizer module) + the collection + HTTP |

## Document search

Pixeltable: document_splitter view and EmbeddingIndex. Query with similarity(string=). Weaviate Python v4: collections.create with vector_config, then query.hybrid. Last reviewed: Oct 2026.

### 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

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

if __name__ == '__main__':
    # After: pxt schema update app.py search
    docs = pxt.get_table('search.docs')
    docs.insert([{'document': 'handbook.pdf', 'title': 'Handbook'}])
    chunks = pxt.get_table('search.chunks')
    sim = chunks.text.similarity(string='refund policy')
    chunks.order_by(sim, asc=False).limit(5).select(chunks.text, chunks.title)
```

### Weaviate

```python
import weaviate
from weaviate.classes.config import Configure, DataType, Property

client = weaviate.connect_to_local()
docs = client.collections.create(
    'Document',
    vector_config=Configure.Vectors.text2vec_openai(),
    properties=[
        Property(name='title', data_type=DataType.TEXT),
        Property(name='body', data_type=DataType.TEXT),
    ],
)
docs.data.insert({'title': 'Handbook', 'body': 'Refund policy...'})
hits = docs.query.hybrid(query='refund policy', alpha=0.5, limit=5)
# Chunking PDFs and storing the original file are still outside this collection.
```

## 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 Weaviate. Coming from a vector DB: docs.pixeltable.com/howto/coming-from

## When to choose Weaviate

- **Hybrid search is the product**: BM25 fused with dense vectors, named vectors, or a Weaviate Cloud SLA other services already call. Pixeltable can still produce embeddings.
- **The collection is already the serving contract**: Keep Weaviate. Do not rewrite it into Pixeltable just to “have one vendor.” Roundup: /learn/vector-database-alternatives-for-multimodal-apps

## Making the right choice

### Same aisle as Pinecone, different surface

- Pinecone is managed ANN. Weaviate is hybrid search plus an open-source engine. That is why this page exists separately.
- Qdrant, Chroma, and Milvus remain the same job as Pixeltable vs Pinecone; they do not each get a vs page.
- Last reviewed: Oct 2026. Weaviate docs: https://docs.weaviate.io/weaviate/search/hybrid and https://docs.weaviate.io/weaviate/manage-collections/vector-config

## FAQ

### Is Pixeltable a Weaviate alternative?

For typical document and multimodal RAG, yes: put EmbeddingIndex on the column and query with similarity(string=). For hybrid BM25 plus dense fusion, or a dedicated Weaviate cluster, no — keep Weaviate.

### How is this different from Pixeltable vs Pinecone?

Pinecone is a managed vector index. Weaviate adds hybrid search, named vectors, and a self-hosted option. The Pixeltable side is the same: the index lives on the source row.

### Do I need a vector database?

You need an embedding index. You need Weaviate (or Pinecone, Qdrant, Chroma, pgvector) when that engine is the serving contract. See /learn/vector-database-alternatives-for-multimodal-apps. Last reviewed: Oct 2026.