---
title: "Pixeltable vs LlamaIndex: data plane vs query engine"
description: "Pixeltable stores multimodal rows and keeps indexes in sync. LlamaIndex runs query engines and agents. Use Pixeltable for RAG data; keep LlamaIndex when the query engine is the product."
keywords:
  - Pixeltable vs LlamaIndex
  - LlamaIndex alternative
  - LlamaIndex vs Pixeltable
  - multimodal RAG
  - query engine
  - Python AI backend
url: "https://www.pixeltable.com/compare/pixeltable-vs-llamaindex"
---

# Pixeltable and LlamaIndex

Updated: 2026-10-28

## Which is better, Pixeltable or LlamaIndex?

It depends. Pixeltable belongs when the data layer is the product: PDFs, images, video, embeddings, and retrieval that stay current on insert, without a loader plus a vector store plus a rebuild. LlamaIndex belongs when the query engine, LlamaParse, or an agent workflow is the product, or the team already writes VectorStoreIndex. Keep LlamaIndex and point a tool at Pixeltable tables. Complementary, not a wipe. As of 2026-10-28.

LlamaIndex is a data framework for LLM apps: loaders, indexes, query engines, and agents. Pixeltable is the data plane those engines query: typed media tables, computed columns, embedding indexes, and HTTP in one Python file. They are not the same job. Use both, or drop the extra store if Pixeltable already holds the chunks.

## Summary

### Pixeltable

- Tables, computed columns, and embedding indexes in one schema
- Insert a document or image; chunking and indexes update
- Same file serves HTTP with FastAPIRouter
- Not a query-engine library and not a vector database

### LlamaIndex

- VectorStoreIndex, retrievers, and QueryEngine over nodes you load
- FunctionAgent and AgentWorkflow for tool-calling loops
- Readers and connectors for many file types and APIs
- Persistence and the vector store are still a store you bring

## Comparison

| Feature | Pixeltable | LlamaIndex |
| --- | --- | --- |
| What it is | Multimodal tables + incremental compute + serving | Data framework: loaders, indexes, query engines, agents |
| Where the bytes live | Native Document, Image, Video, Audio columns | Document and Node objects; you pick a vector store behind VectorStoreIndex |
| Chunking and embeddings | document_splitter view + EmbeddingIndex; insert keeps them current | Index build embeds nodes; no automatic re-embed, so changed sources need an explicit insert or refresh |
| Query engines and agents | similarity() plus @pxt.udf tools; no QueryEngine or AgentWorkflow | index.as_query_engine(), retrievers, FunctionAgent, AgentWorkflow |
| Connectors | Typed media columns and iterators you declare | Readers and integrations for many sources; the default on-ramp for RAG tutorials |
| Multimodal rows | One schema for PDFs, images, and video; CLIP and text indexes together | Possible; loaders, embedders, and stores per modality that you keep aligned |
| Team already using it | Python tables; agents learn TableModel from the skill | VectorStoreIndex and QueryEngine are what many RAG tutorials already teach |

## RAG that stays in sync

Pixeltable: chunking, the index, and a query on the table. Apply with pxt schema update app.py app. LlamaIndex: VectorStoreIndex.from_documents and as_query_engine, from their starter tutorial (accessed 2026-10-28).

### 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(
        Docs.document, separators='sentence,token_limit', limit=512
    ),
):
    __indexes__ = [pxt.EmbeddingIndex(text, embedding=embed)]

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

### LlamaIndex

```python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.openai import OpenAIEmbedding

# Starter path: developers.llamaindex.ai/python/framework/getting_started/starter_example
documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(
    documents,
    embed_model=OpenAIEmbedding(model='text-embedding-3-small'),
)
query_engine = index.as_query_engine()
response = query_engine.query('What is the refund policy?')
print(response)
# Images, versioning, and re-chunking on insert are still yours.
# Persist if you do not want to rebuild: index.storage_context.persist('storage')
```

## When to choose Pixeltable

- **The data layer is the product**: PDFs, images, or video plus the embedding. Insert should update the index. You do not want a loader, a vector store, and a rebuild job beside the table.
- **You want retrieval without a second index library**: EmbeddingIndex on the column. similarity(string=query) then order_by. A LlamaIndex agent can call that as a tool.

## When to choose LlamaIndex

- **The query engine or agent loop is the product**: as_query_engine, retrievers, FunctionAgent, AgentWorkflow. Pixeltable does not ship that runtime. Docs: developers.llamaindex.ai/python/framework/getting_started/starter_example
- **The team already writes LlamaIndex**: Keep VectorStoreIndex. Point a tool at Pixeltable tables instead of standing up Chroma for every prototype.

## Making the right choice

### Complementary, not a wipe

- Pixeltable replaces the RAG data path: chunking, embeddings, persistence, incremental insert.
- LlamaIndex still fits when the application is a query engine or an agent workflow. Call Pixeltable from a tool.
- LangChain and LangGraph are the same split: /compare/pixeltable-vs-langchain.
- No latency bake-off on this page. LlamaIndex does not publish a Pixeltable comparison we can cite.

## FAQ

### Is Pixeltable a LlamaIndex alternative?

It replaces the data layer of a LlamaIndex stack (chunking, embeddings, retrieval, persistence). It does not replace QueryEngine, FunctionAgent, or AgentWorkflow. Keep LlamaIndex when that runtime is the product.

### Can I use Pixeltable with LlamaIndex?

Yes. Keep the query engine or agent. Expose a Pixeltable query or @pxt.udf as a tool so retrieval hits tables instead of a sidecar vector store.

### Does Pixeltable replace LlamaParse?

No. LlamaParse is LlamaIndex's hosted document parser (developers.llamaindex.ai/llamaparse/quickstart/, accessed 2026-10-28). Pixeltable stores a Document column and splits it with document_splitter. If LlamaParse is already how you get text out of messy PDFs, keep it and insert the result.

### Is Pixeltable a vector database?

No. EmbeddingIndex is one piece of a table that also holds source media and computed columns. A vector database is the index alone. LlamaIndex sits on a vector store you choose; Pixeltable is not that store.