Mamba vs Stan

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

Mamba

Markov chain Monte Carlo for Bayesian analysis in Julia.

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

Markov chain Monte Carlo for Bayesian analysis in Julia.

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

Research teams working on Bayesian statistics who need a flexible and efficient MCMC library.
Academics developing new statistical models that require advanced sampling techniques.

✓ 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

Teams requiring real-time data processing as Mamba is designed for batch analysis.
Projects with strict performance constraints where every millisecond counts, given the computational intensity of MCMC methods.

✕ Not for

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

Strengths

↑ Strengths

Support for various MCMC algorithms including Metropolis-Hastings and Gibbs sampling.
Flexible model specification allowing users to define complex Bayesian models.
Efficient computation leveraging Julia's performance capabilities.
Comprehensive documentation and examples for ease of use.

↑ 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

Limited language support
Complex setup process
Small community and limited support

↓ 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 Mamba profile →Full Stan profile →