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What is Fused?

Fused is the glue between your data and the tools that consume it: write Python functions that run serverlessly at scale, and expose them as secure HTTP endpoints. No containers, no infrastructure config, no waiting.

Fused Overview

Built on Python​

Python is the language of data. Whether you want to load data from S3, Snowflake, or Google Drive, or extract from a PDF, scrape a website, or process a one-time download, there's a way to do it in Python. It's the most versatile tool for working with any data, regardless of type, origin, or size.

We ship an opinionated and tested set of dependencies that covers most modern data use cases, so data scientists and engineers can start building in seconds, not hours.

Scalable serverless engine​

Python alone isn't enough. Scaling up compute on demand normally means deploying a new Docker container. Fused lets you create User Defined Functions (UDFs): simple, self-contained Python functions that run on our serverless compute infrastructure. All you need is a @fused.udf decorator:

@fused.udf
def udf():
    import pandas as pd
    return pd.read_parquet("s3://fused-sample/demo_data/airbnb_listings_sf.parquet")

You can call thousands of jobs at the same time:

@fused.udf
def udf():
    basic_example = fused.load('process_data')  # Load existing UDF
    results = basic_example.map(range(100_000), max_workers=500)  # Run in parallel
    return results.df()

This spins up as many nodes as needed under the hood.

Fused parallel job execution

To put that in perspective: two thousand 90-second jobs complete in 120 wall-clock seconds, just 30 seconds of total orchestration overhead, at 0.015s per job, each on a dedicated 4-core machine with 10 GB of RAM. A regular EC2 instance takes ~2 minutes just to turn on. Fused does the whole job in that time.

Expose everything as an API​

Every project you build in Fused lives in a Canvas: a freeform board where every component is either a UDF running Python, a sticky note, or a widget to explore data.

Fused Canvas

Each Canvas is shareable, and every component is directly accessible through an API call. Think of a Canvas as a directory of all your endpoints, and each UDF as an endpoint.

Every Canvas can also be turned into a Fused MCP Server: a list of all the endpoints in your Canvas, corresponding to all your UDFs:

Fused MCP Server

Every UDF you create can instantly become a parameterized endpoint callable from anywhere, once you've scoped the right permissions:

Fused API endpoint

Version control & team collaboration​

Since users are doing real work in Fused, version control matters. The Canvas can be collaborated on and connected directly to GitHub to store and version-control your code.

GitHub integration

Teams can make edits directly from GitHub and push or pull changes to update dashboards and visualizations in real time.

Scoped security & access control​

Every Fused project can be shared and turned into a Fused MCP server, with fine-grained control over who can access what:

  • Public: Anyone can see and execute your UDFs. Optionally require a passcode.
  • Private: Only members of your Fused team can access your work. Issue temporary access tokens (e.g. valid for 1 hour) or use service accounts for secure machine-to-machine delivery.

You control the scope of each UDF individually.

Example: RAG your docs​

We built a bot that answers questions about Fused documentation directly in Slack, all built inside Fused itself.

RAG Slack bot built in Fused

It works in three steps:

  • Connect: Crawl any docs site via its sitemap, parallelize fetching and cleaning all pages into a single llms.txt file saved to S3.
  • Process: Fetch the file, split into overlapping chunks, embed with an off-the-shelf LLM, write vector embeddings to a LanceDB table.
  • Expose: A vector_search UDF takes a natural language question, embeds it, runs a cosine similarity lookup, and returns the top 4 most relevant chunks, cached for 1 hour.

The result: any AI agent (or Slack bot) can query your docs with a single request:

https://udf.ai/fc_YOUR_SHARED_TOKEN/vector_search.json?question=what is fused?

All on S3, simple Python, smart caching, and parallel processing, no additional setup. See the full example in the docs.

Get started​