OpenGM vs GTSAM
Side-by-side — pricing, strengths, tradeoffs, and fit.
OpenGM
Discrete optimization and inference solver for factor graphs
GTSAM
Factor graph based least-square optimization solver for robotics and computer vision.
At a glance
Open SourceFree Tier
· Not enough data·From $0
Open SourceFree Tier
● Established·From $0
Overview
One-liner
One-liner
Discrete optimization and inference solver for factor graphs
One-liner
Factor graph based least-square optimization solver for robotics and computer vision.
Maturity
Maturity
Not enough dataMaturity
EstablishedOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Researchers working on discrete optimization problems who need a flexible framework for experimentation
Developers implementing computer vision algorithms that require factor graph inference
✓ Best for
Robotics teams working on SLAM and pose graph optimization tasks
Computer vision researchers implementing SfM algorithms
Academic projects requiring efficient least-square optimization solutions
Not ideal for
✕ Not for
Projects requiring real-time performance as OpenGM may not be optimized for speed in all scenarios
Applications where the problem cannot be represented using factor graphs
✕ Not for
Projects that require real-time processing with minimal latency
Applications needing a web-based interface for configuration and monitoring
Strengths
↑ Strengths
Support for various discrete optimization algorithms
Factor graph representation of problems
Modular design allowing easy integration with other tools
↑ Strengths
Factor graph-based optimization for solving least-square problems
Supports a wide range of robotics and computer vision tasks
Highly optimized algorithms for efficient computation
Weaknesses
↓ Weaknesses
Steep learning curve for non-C++ developers
Limited documentation and support resources
Performance overhead due to template-heavy design
Narrow focus on factor graph models limits general applicability
↓ Weaknesses
Steep learning curve due to complex factor graph concepts
Limited language support, primarily C++
Documentation is sparse and not beginner-friendly
Performance can degrade for very large-scale problems