Edward vs Stan

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

Edward

Probabilistic modeling library built on TensorFlow.

Stan

Probabilistic programming with full Bayesian inference and Hamiltonian Monte Carlo sampling.

At a glance

Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
· Not enough data·From $0

Overview

One-liner

Probabilistic modeling library built on TensorFlow.

One-liner

Probabilistic programming with full Bayesian inference and Hamiltonian Monte Carlo sampling.

Maturity

Declining

Maturity

Not enough data

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

Researchers who need a flexible library for Bayesian modeling and inference
Developers working on projects that require advanced statistical methods

✓ Best for

Research teams needing precise Bayesian inference for complex models
Academics working on advanced statistical methods and simulations
Developers building custom statistical tools requiring high performance

Not ideal for

✕ Not for

Projects requiring real-time probabilistic computations due to TensorFlow's overhead
Teams looking for a user-friendly interface without deep programming knowledge

✕ Not for

Projects with strict real-time requirements due to computational intensity
Teams preferring a graphical user interface over programming-based solutions

Strengths

↑ Strengths

Probabilistic modeling and inference
Built on TensorFlow for scalability
Supports a wide range of probabilistic models

↑ Strengths

Full Bayesian inference with Hamiltonian Monte Carlo sampling
Support for a wide range of statistical models
Highly optimized and efficient computation
Extensive documentation and community support

Weaknesses

↓ Weaknesses

Steep learning curve for non-statisticians and machine learning experts
Limited documentation and examples
Performance issues with complex models
Dependency on TensorFlow's ecosystem and limitations

↓ Weaknesses

Steep learning curve for non-statisticians
Limited language support beyond C++ and R
Complex setup process for new users
Performance issues with very large datasets or complex models
Full Edward profile →Full Stan profile →