Gurobi is a leading decision intelligence and mathematical optimization platform used by enterprises, researchers, and data scientists to solve highly complex problems.
It delivers state-of-the-art solvers for linear, quadratic, and mixed-integer optimization, enabling faster, more accurate decisions across industries such as supply chain, finance, energy, transportation, and manufacturing, with flexible deployment options and expert-level support.
Plan |
Price |
Featured |
|---|---|---|
| Evaluation License (Commercial Trial) | Free for 30 days | Full-featured Gurobi Optimizer, Benchmarking and tuning support, Technical guidance |
| Academic License | Free (Eligible academic institutions) | Unlimited coursework and research use, Full solver capabilities, Community forum support |
| Commercial License (Local / Server / Cloud) | Custom Quote (Contact Sales) | Production deployment rights, Flexible core and user-based licensing, Enterprise support options |
Price details: https://www.gurobi.com/solutions/licensing/
Competitor |
Pros |
|---|---|
| IBM ILOG CPLEX | Gurobi is often easier to install and configure than CPLEX, with clearer licensing flexibility. Users report faster solve times on many MIP problems and more responsive technical support. Its Python-first experience is generally considered smoother for modern data science workflows. |
| FICO Xpress | Compared to Xpress, Gurobi offers simpler APIs and more approachable documentation. It is frequently praised for superior performance consistency and easier cloud and container deployment, reducing operational overhead for teams scaling optimization workloads. |
| Google OR-Tools | While OR-Tools is free, Gurobi delivers significantly better performance on large, complex industrial models. Its commercial-grade support, advanced tuning capabilities, and solver robustness make it more suitable for revenue-critical enterprise applications. |
| SCIP | Gurobi generally outperforms SCIP in speed and solution quality for large mixed-integer problems. It also provides more comprehensive documentation, commercial support, and easier integration into production systems. |
| MOSEK | Compared to MOSEK, Gurobi supports a broader range of discrete and mixed-integer optimization use cases. Its solver ecosystem is more mature for complex industrial constraints and large-scale decision intelligence deployments. |
Competitor |
Cons |
|---|---|
| IBM ILOG CPLEX | Compared to CPLEX bundles within IBM ecosystems, Gurobi can feel less integrated with broader enterprise analytics stacks. Organizations already standardized on IBM tooling may face additional procurement and integration steps. |
| FICO Xpress | Gurobi lacks some of the vertically integrated optimization suites that Xpress provides for specific industries. Users seeking end-to-end packaged solutions may need additional development effort with Gurobi. |
| Google OR-Tools | Unlike OR-Tools, Gurobi is not open source, which limits transparency and customization at the solver level. Cost can be a barrier for startups or teams with limited budgets. |
| SCIP | SCIP’s academic and open-source roots make it attractive for experimentation, whereas Gurobi’s commercial licensing may restrict exploratory usage outside trials or academia. |
| MOSEK | For users focused purely on convex optimization, MOSEK can be more cost-effective. Gurobi’s broader capabilities may be unnecessary overhead for narrowly defined optimization needs. |
