- Optimization solver: Solve linear, quadratic, quadratically constrained and mixed-integer models.
- Flexible deployment: Run models on local workstations, servers, cloud services or container platforms.
- Multi-language APIs: Build models with Python, Java, C++, .NET and AMPL interfaces.
- Trial and academic licensing: Evaluate a full-featured commercial trial for 30 days or use the academic license when eligible.
- Technical guidance: Use Gurobi support, tuning and benchmarking services for production optimization work.
Gurobi
Enterprise-grade mathematical optimization and decision intelligence platform
Updated August 11, 2026
Gurobi Overview
Gurobi Optimizer is mathematical optimization software for linear, quadratic, quadratically constrained and mixed-integer models. Developers can work through APIs for Python, Java, C++, .NET and AMPL.
The solver can run on a local machine, a server, cloud infrastructure or in containers. This lets a team use the same model in different deployment environments.
Key Features
Pricing
| Plan | Price | Featured |
|---|---|---|
| Commercial evaluation license | Free for 30 days | Full-featured Gurobi Optimizer trial for commercial evaluation |
| Academic license | Free for eligible academics | Always-free academic use under the academic license terms |
| Commercial production license | Custom quote | Local, server, cloud and container deployment options; contact Gurobi sales |
Commercial production licenses require a quote. The 30-day commercial evaluation and always-free academic license are conditional offers, not a continuing free commercial plan.
Pricing source: Gurobi licensing
Pros
| Compared with | Why this product may fit better |
|---|---|
| IBM ILOG CPLEX | IBM ILOG CPLEX is closely tied to the wider IBM estate, while Gurobi keeps its optimizer focused across Python, Java, C++, .NET and AMPL. Gurobi also supports local, server, cloud and container deployment without changing solvers. |
| FICO Xpress | A focused production workflow brings Gurobi's modeling APIs, parameter tuning and direct technical guidance together. FICO Xpress also offers a commercial solver stack, so buyers should compare support on the models whose performance affects revenue. |
| Google OR-Tools | Google OR-Tools is an open-source toolkit, whereas Gurobi adds commercial support and a broader continuous and mixed-integer solver. The paid license can be worthwhile when solve time, reliability and access to specialists affect customer-facing operations. |
| SCIP | SCIP provides an open-source optimization route; Gurobi adds more deployment choices and an accountable commercial support relationship. That difference matters when unusually hard models run under service commitments and community help is not enough. |
Cons
| Compared with | Where this product may fall short |
|---|---|
| IBM ILOG CPLEX | IBM ILOG CPLEX can fit an existing IBM procurement, middleware and support relationship without adding another solver vendor. Gurobi's technical gains should be measured on representative models before accepting a separate contract and license server. |
| Google OR-Tools | For modest routing or scheduling problems, Gurobi's paid production license may be difficult to justify. Google OR-Tools remains free for commercial use, while Gurobi's no-cost access is limited to evaluation and qualifying academic work. |
| MOSEK | MOSEK specializes in convex conic optimization and may better suit a team centered on that model class. Gurobi's wider mixed-integer scope is valuable only when those additional capabilities are genuinely part of the roadmap. |
Reviews
- Reddit performance comment: One optimization practitioner says a commercial solver can win “by a large margin” when the problem genuinely needs it. The surrounding discussion makes this a workload-specific observation, not a promise that every model will justify the license.
- Reddit licensing comment: Another participant calls a subscription change “a nasty expensive surprise.” That individual account does not establish current pricing, but it is a useful reminder to obtain renewal, deployment and license-server terms in writing before production adoption.
- Reddit solver comment: A later commenter describes Gurobi as “the best solver in the market.” The praise is clear but subjective, so teams should benchmark their own hard models and include competing solvers rather than treating reputation as a performance test.
- G2 reviews: G2 lists 4.7/5 and emphasizes fast optimization, approachable modeling APIs and support across common programming environments. Less positive feedback concerns commercial cost and the expertise required to formulate and tune models, both of which belong in the deployment budget.
