Fused Overview

What is Fused — and when should you use it

Fused is a platform for running Python in the cloud, organized into projects and independently callable functions. The two core primitives are canvases and UDFs.

The mental model

Canvas = project. A canvas is the container for a piece of work — equivalent to a repo or a folder. All UDFs in a canvas share the same permissions and access controls (private / team / public). Set it once; all UDFs inherit it.

UDF = callable function with its own compute. Each UDF (@fused.udf def udf(...)) is independently deployed, has its own API endpoint, its own compute, and its own cache. You can call one UDF without touching any other. Secrets and integrations (fused.secrets, fused.api.notion_connect(), etc.) are resolved automatically by the runtime — no credential management in code.

canvas (project)
├── udf_a.py        → own endpoint, own compute, own cache
├── udf_b.py        → own endpoint, own compute, own cache
└── widget.json     → optional browser UI on top

UDFs within a canvas call each other via fused.load("udf_name"), so you can compose them into pipelines while keeping each piece independently testable.

When to use Fused instead of a local script

The clearest signal: if someone other than you needs to call it, or if it needs to still work tomorrow, put it in Fused.

Parallelism and scale

This is the biggest advantage of structuring work as UDFs rather than scripts.

Run many jobs in parallel with .map()

Call any UDF across a list of inputs — each input spawns its own instance, all running concurrently:

@fused.udf
def process_item(item: str = ""):
    import pandas as pd
    # ... do work on one item ...
    return pd.DataFrame({"result": [item]})

pool = process_item.map(["item_1", "item_2", "item_3", ...])
results = pool.df()     # collect all results into a DataFrame
pool.times()            # inspect per-job execution times

Design principle: prefer many small UDFs over one large one. A task that processes 100 items sequentially in 10 minutes becomes 100 parallel 6-second jobs. The 120s per-job limit is easy to stay under when each UDF does one unit of work.

Attach larger compute with engine

By default UDFs run serverless (remote). For memory-heavy, CPU-heavy, or long-running work, attach a dedicated instance with engine:

@fused.udf(engine="medium")
def udf():
    # 16 vCPU, 64 GB RAM, no time limit
    ...

Two reasons to use a dedicated engine: more compute, or no time limit. A job that takes 3 minutes can't run on the default serverless runtime but runs fine on small. Dedicated engines also have a longer cold-start — use remote by default; switch when you've hit the 120s cap or a memory constraint.

You can also override engine per .map() call:

pool = process_item.map(inputs, engine="medium")
pool.wait()
results = pool.df()

How to structure a new project

1. One canvas per project. Permissions and sharing apply to everything in it — don't mix unrelated work.
2. One UDF per logical function. If two things have different inputs, outputs, or scaling needs, they're separate UDFs.
3. Prefer many small UDFs. Small UDFs are parallelizable with .map(), independently callable, and easier to debug. Loop inside a UDF → can't parallelize. Split into UDFs → can.

Constraints to know upfront

• 120s execution timeout on the default serverless runtime — use a dedicated engine to remove this limit
• All imports must be inside the UDF function body — module-level imports are not executed
• No persistent in-memory state between calls — use external storage (Fused files, S3, Notion, etc.) for state
• Canvas names: [a-zA-Z0-9_] only — no hyphens; my_project not my-project

See also

• fused:fused-cli — pushing, running, and managing canvases from the CLI
• fused:fused-udfs — writing UDFs: structure, types, caching, performance
• fused:fused-integrations — connecting UDFs to Notion, Snowflake, S3, etc.
• fused:canvas-toml — canvas folder layout and canvas.toml format
• fused:json-ui-schemas — building widget UIs on top of UDFs