---
title: "Pixeltable vs Airflow: one file vs DAG + vector DB + FastAPI"
description: "Airflow + vector DB + FastAPI vs one Python file: Pixeltable stores media, runs transforms, indexes, and serves HTTP. Last reviewed Oct 2026."
keywords:
  - Pixeltable vs Airflow
  - Airflow alternative
  - Airflow vector database FastAPI
  - ML orchestration
  - declarative pipeline
url: "https://www.pixeltable.com/compare/pixeltable-vs-airflow"
---

# Airflow + vector DB + FastAPI vs Pixeltable

Apache Airflow schedules DAGs. A multimodal app on Airflow still needs object storage, a vector database, and FastAPI. Pixeltable is that stack as one Python file: media in tables, transforms as computed columns, indexes and HTTP as declarations. Airflow remains the right tool for heterogeneous warehouse ETL.

## Summary

### Pixeltable

- Tables, computed columns, EmbeddingIndex, FastAPIRouter in one file
- Insert a row; only that row’s dependents recompute
- No separate vector DB or FastAPI project to keep aligned
- Not a general-purpose DAG scheduler for warehouse ETL

### Airflow

- Apache Airflow 3.3: TaskFlow DAGs via from airflow.sdk import dag, task
- Schedules and retries tasks across systems you already run
- A multimodal app still adds S3, a vector DB, and FastAPI
- Best fit: heterogeneous batch, not an application catalog

## Comparison

| Feature | Pixeltable | Airflow |
| --- | --- | --- |
| What it is | Database + orchestration + serving in one Python file | DAG orchestrator; the stack around it is yours |
| Heterogeneous warehouse ETL | Not a replacement for company-wide DAG scheduling | The product: schedule, retry, and observe tasks across systems |
| Multimodal app glue | Schema is the pipeline; insert runs downstream work | You still wire object storage + vector DB + FastAPI |
| Incremental recompute | Changed rows only; cached columns otherwise | A DAG run; you encode incrementality in task code |
| Vector index | EmbeddingIndex on the source table | A task that upserts into Pinecone, Weaviate, or pgvector |
| HTTP serving | FastAPIRouter in the same file | A second service you deploy beside the DAG |

## Document RAG: one file vs the stack

Pixeltable: document_splitter, EmbeddingIndex, FastAPIRouter. Airflow 3.3 TaskFlow still leaves the vector DB and FastAPI as other boxes. Last reviewed: Oct 2026. Airflow: https://airflow.apache.org/docs/apache-airflow/stable/tutorial/taskflow.html

### Pixeltable

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

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)]

@pxt.query
def search_docs(query: str, limit: int = 5):
    sim = Chunks.text.similarity(string=query)
    return (
        Chunks.order_by(sim, asc=False)
        .limit(limit)
        .select(Chunks.text, Chunks.title, sim)
    )

api = FastAPIRouter(name='docs')
api.add_insert_route(Docs, path='/docs', inputs=[Docs.document, Docs.title])
api.add_query_route(path='/search', query=search_docs)
```

### Airflow

```python
from datetime import datetime
from airflow.sdk import dag, task

@dag(schedule=None, start_date=datetime(2026, 1, 1), catchup=False)
def rag_stack():
    @task
    def chunk_pdfs():
        # Files still live in object storage you operate.
        return [{'id': '1', 'text': 'Refund policy...'}]

    @task
    def embed(chunks):
        return [{'id': c['id'], 'values': [0.1, 0.2]} for c in chunks]

    @task
    def upsert(vectors):
        # index.upsert(vectors)  # Pinecone, Weaviate, or pgvector
        return len(vectors)

    upsert(embed(chunk_pdfs()))

rag_stack()
# HTTP is a second FastAPI app that queries the vector DB and reads S3.
```

## When to choose Pixeltable

- **The app is media, models, and retrieval**: A coding agent writes one file. You review one diff. pip install pixeltable. Cloud is Limited Beta, not GA.
- **You were about to glue Airflow to a vector DB and FastAPI**: That stack is the comparison, not Airflow alone. Pixeltable replaces the glue for this job.

## When to choose Airflow

- **Heterogeneous warehouse DAGs**: Spark jobs, SaaS extracts, dbt, and SLAs across existing systems. Pixeltable is not that scheduler. Docs: https://airflow.apache.org/
- **The company already runs Airflow**: Keep it for those DAGs. Point a task at Pixeltable when the multimodal app should own the rows, or leave Pixeltable as the app and Airflow as the rest of the estate.

## Making the right choice

### Do not confuse the scheduler with the stack

- Airflow is excellent at scheduling. The multimodal app still needs storage, an index, and HTTP.
- Prefect and Temporal are the same aisle as Airflow. LangGraph is the agent-loop aisle (see Pixeltable vs LangChain).
- Longer write-up (older snippets): https://www.pixeltable.com/blog/pixeltable-vs-airflow-ml-orchestration
- Last reviewed: Oct 2026. Airflow 3.3 TaskFlow imports dag and task from airflow.sdk.

## FAQ

### Is Pixeltable an Airflow alternative?

For a multimodal app you were going to build as Airflow + object storage + vector DB + FastAPI, yes: one Python file replaces that glue. For company-wide warehouse ETL, no — keep Airflow.

### Does Pixeltable schedule arbitrary DAGs?

No. Computed columns run when rows change. That is orchestration for tables, not a replacement for Airflow’s scheduler, pools, and cross-system sensors.

### Where were these facts checked?

Last reviewed: Oct 2026. Airflow 3.3 TaskFlow: https://airflow.apache.org/docs/apache-airflow/stable/tutorial/taskflow.html and https://airflow.apache.org/docs/task-sdk/stable/index.html (from airflow.sdk import dag, task). Pixeltable: docs.pixeltable.com.