Ciaren

Comparison · fair to both tools

Ciaren vs Flowfile: two local-first visual ETL tools

Ciaren vs Flowfile compares two open-source visual ETL tools with the same goals: local-first, visual, and exportable to Python. Flowfile is built on Polars and is strong on code-to-canvas work and a built-in data catalog. Ciaren exports the same flow to pandas or Polars and trains ML models on the canvas.

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In short

Flowfile license
MIT
Ciaren license
AGPL-3.0 core
Flowfile exports
Polars
Ciaren exports
pandas and Polars
Both schedule
Yes, built in
Comparison guide

The difference

Same goals, different focus

Flowfile is an MIT-licensed visual ETL tool built on Polars. Of the visual ETL tools in Ciaren's comparison guide, it is the closest to Ciaren. Flows are saved as readable YAML, which suits version control, and a flow of standard transforms on local files exports as plain Polars code.

It also works in the other direction. With its Polars-like Python API you write a pipeline as code and then open it on the canvas. Flowfile ships desktop installers for Windows, macOS, and Linux, a pip package, and a Docker setup with accounts, groups, and a shared catalog. Its feature list includes a Delta Lake data catalog, Kafka ingestion, scheduling, custom nodes, and an optional AI assistant.

Ciaren's focus is different. One flow exports to pandas or Polars, and the machine learning nodes train scikit-learn models tracked in MLflow. Ciaren also ships data-quality assertion nodes and a plugin SDK whose nodes, connectors, and ML model types run end to end today.

At a glance

Ciaren and Flowfile side by side

Flowfile details come from its website and documentation as of September 2026. Check the vendor's site for current terms and features.

AspectCiarenFlowfile
License and costFree. Core AGPL-3.0, Plugin API Apache-2.0.Free. MIT.
Where it runsYour machine, installed with pip or Docker. The editor opens in the browser.Desktop app, pip package, or Docker.
Workflow stored asLocal database (SQLite by default). Exports a JSON .flow document.YAML files.
Standalone Python exportpandas, eager Polars, and lazy Polars scripts. ML nodes export scikit-learn code.Polars scripts. Some nodes export as calls to the Flowfile API.
Machine learningscikit-learn nodes with MLflow tracking. XGBoost and LightGBM optional.ML nodes. See their docs.
SchedulingBuilt-in cron scheduler, included.Built in: interval, cron, or a trigger on catalog table updates.
ExtensibilityPlugin SDK for nodes, connectors, and ML model types. Further extension points are defined but not wired up yet.Custom Python nodes built in the Node Designer.

Engines and ML

Where Ciaren's focus differs

Engines

Run on Polars or pandas, export either

Polars is the default engine. Pick pandas for a single run without changing the flow, and the runs list shows which engine each run used. Export writes pandas, eager Polars, and lazy Polars from the same flow.

  • Lazy Polars reads with scan functions, for large files
  • ML nodes convert to pandas at the model boundary, so they work on both engines
Engines guide
Runs
Ciaren runs list with each run's status badge, engine, trigger, and timestamps

ML

Train and compare models on the canvas

Train Classifier and Train Regressor fit scikit-learn models and log each one to MLflow. The Experiments tab ranks training runs and highlights the best value in each metric column.

  • Register a model, then point Predict at an alias such as @production
  • Cross-validate with k-fold, stratified, group, or time-series splits
Machine learning in Ciaren
Models
Ciaren ML Models Experiments tab: a leaderboard of training runs ranked by metric, with the champion run marked

Plugins

Add model types through plugins

A plugin can add a trainable model type to the Train nodes' model picker, with its own hyperparameter form. The MLP Classifier example on the Plugins page shows how.

  • Plugins can also add nodes and connectors
  • Plugins declare their permissions, and packages can be signed
ML model plugins
Flow editor · model picker
Ciaren Train Classifier sidebar with the plugin-contributed MLP (neural network) model selected and its hyperparameter form

Fit

Which one fits

Choose Flowfile when

Your work is mostly Polars, you want to move between code and canvas, you want a built-in data catalog, or you prefer the MIT license.

Choose Ciaren when

You want pandas and Polars export from the same flow, ML training on the canvas with MLflow tracking, or plugins that add nodes, connectors, and ML model types.

Limits

When not to use Ciaren

Project status: pre-1.0 alpha

Ciaren is pre-1.0 alpha software for small and medium datasets on one machine. It is not built for distributed or streaming pipelines, datasets of 100 GB or more, or multi-user collaboration.

When not to use Ciaren

FAQ

Common questions

Are both tools open source?

Yes. Flowfile uses the MIT license. Ciaren's core uses AGPL-3.0, and its plugin API uses Apache-2.0, so plugin authors choose their own license.

Do both export Python?

Yes. Flowfile exports Polars scripts, and some nodes export as calls to the Flowfile API. Ciaren exports pandas, eager Polars, and lazy Polars scripts that run without Ciaren installed.

Can I write code and open it on the canvas?

In Flowfile, yes, through its Polars-like Python API. In Ciaren, export goes one way, from the flow to code.

Which one has machine learning?

Both have ML nodes. Ciaren's train scikit-learn models, log them to MLflow, and export as scikit-learn code. For Flowfile's ML nodes, see their docs.

Do both schedule flows?

Yes. Flowfile schedules by interval, by cron, or on catalog table updates. Ciaren has a cron scheduler with retries, catch-up, and auto-disable, plus a webhook trigger for CI and other systems.

Read next

Docs and related pages

Try both on the same data

Install Ciaren with pip and build one flow to compare the exported code.

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