Maple

Maplesoft, best known for its flagship product Maple, remains one of the most established platforms for symbolic mathematics, advanced computation, and engineering modeling. Built on a strong foundation of symbolic algebra, Maple provides capabilities that go far beyond standard numerical solvers—making it an indispensable tool in mathematics-heavy research, engineering simulations, and algorithm development.

Where Maplesoft shines:

• Best-in-class symbolic computation, enabling exact solutions to equations, integrals, differential systems, and algebraic structures
• Powerful numerical solvers integrated seamlessly with symbolic workflows
• MapleSim, their multi-domain modeling & simulation environment, enabling system-level engineering (mechanical, electrical, thermal, fluid, and control systems)
• Rich visualization tools for plots, animations, and dynamic simulations
• Extensive academic adoption, especially in mathematics, physics, engineering, and computational sciences
• Strong pedagogy focus with courseware, step-by-step solvers, and interactive documents

Compared with more general scientific tools (MATLAB, Mathematica, Python), Maple stands out for purity and precision in symbolic math. Its symbolic engine is arguably the most approachable among major CAS platforms, offering intuitive syntax and real-time math document editing. MapleSim, meanwhile, gives engineering researchers a robust, simulation-driven design workflow similar to Modelica-based systems but with a more polished UI.

Limitations? Maple is not designed as a full scientific computing ecosystem, it is strongest in mathematics and system modeling, less so in fields requiring large-scale data science pipelines or heavy AI/ML workflows (where Python dominates). Licensing can also be expensive for small labs unless academic discounts are applied.