Gurobi is a high-performance mathematical optimization solver.
Pros
- Industry-leading performance
- Easy integration with popular programming languages
Cons
- Higher price point compared to some alternatives
Contact for pricing
Best Eris Solver Alternatives ranked by AI · updated May 2025
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Eris Solver is a powerful optimization software for solving complex mathematical problems.
Price: Starting at $99/month
🎯 erissolver.com
Top 5 Eris Solver alternatives
IBM ILOG CPLEX Optimization Studio is an enterprise optimization suite for modeling and solving planning, scheduling, logistics, and resource-allocation problems. It combines...
Pros
- Mature solver for linear, mixed-integer, and constraint programming
- OPL provides a concise language for complex optimization models
- Strong fit for enterprise planning and scheduling deployments
Cons
- Commercial licensing is costly compared with open-source options
- Steeper learning curve than application-focused planning software
- Community Edition has model and usage limitations
Free Community Edition; commercial pricing by quote
MOSEK is a software package for solving mathematical optimization problems.
Pros
- Fast and reliable optimization algorithms
- Support for various optimization problem types
Cons
- Higher cost for some users
Starting at $2,000/year
Optimalon is an optimization software platform for manufacturers, logistics teams, and other organizations solving complex planning and allocation problems. It supports applications...
Pros
- Supports multiple optimization problem types beyond route planning
- Can model industry-specific constraints and objectives
- More application-focused than general-purpose solver libraries
Cons
- Commercial pricing and licensing details are not publicly transparent
- Smaller ecosystem than IBM CPLEX, Gurobi, or Google OR-Tools
- May require specialist modeling expertise for advanced implementations
MiniZinc is a high-level modeling language for expressing constraint satisfaction and constraint optimization problems. It targets multiple constraint programming, mixed-integer, and SAT-based...
Pros
- Solver-independent models can target CP, MIP, and SAT backends
- Compact declarative syntax is well suited to scheduling and combinatorial problems
- Includes an IDE, standard library, tutorials, and benchmark suite
Cons
- Less suitable than mathematical modeling systems for large continuous optimization models
- Performance depends heavily on the selected backend solver
- Requires learning a domain-specific modeling language
Free and open source
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