Hamilton vs Luigi

Side-by-side — pricing, strengths, tradeoffs, and fit.

Hamilton

Lightweight library for defining data transformations as DAGs.

Luigi

Build complex pipelines of batch jobs with ease.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

Lightweight library for defining data transformations as DAGs.

One-liner

Build complex pipelines of batch jobs with ease.

Maturity

Established

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Developers who need to create clear and maintainable data processing pipelines.
Data science teams working on feature engineering for machine learning models.

✓ Best for

Teams needing to manage large, complex data pipelines with Python
Projects requiring integration with Hadoop or Spark for big data processing

Not ideal for

✕ Not for

Projects requiring real-time streaming data processing (Hamilton is batch-oriented).
Teams looking for a fully managed service solution.

✕ Not for

Real-time streaming applications (batch-only architecture)
Projects that require a web-based UI for workflow management

Strengths

↑ Strengths

Defines data transformations as DAGs for clarity and maintainability.
Simplifies complex data processing workflows.
Supports feature engineering and machine learning pipelines.

↑ Strengths

Dependency resolution and workflow management
Visualization of pipelines
Handling failures and retries
Integration with Hadoop, Spark, and more

Weaknesses

↓ Weaknesses

Limited integrations with other data processing tools
Small community and slower issue resolution

↓ Weaknesses

Limited out-of-the-box integrations with other tools and platforms
Performance issues with large-scale pipelines
Full Hamilton profile →Full Luigi profile →