Pyomo alternatives

3 tools to consider instead of Pyomo, shown against it.

Pyomo AMPL Google OR-Tools Gurobi Optimizer
Vendor Sandia National Laboratories / Pyomo community AMPL Optimization Inc. Google Gurobi Optimization
Pricing model Open source + paid options Subscription Open source + paid options Quote only
Free tier Yes Yes Yes Yes
Deployment Self-hosted Cloud, Self-hosted Self-hosted Cloud, Self-hosted
Open source Yes (BSD-3-Clause) No Yes (Apache-2.0) No
Best for Python-based data science and engineering teams who want optimization modeling embedded in their existing codebase. Operations research teams and academics who want to formulate optimization problems once and test them against multiple solvers. Engineering teams building routing, scheduling or packing optimization into a product without a commercial solver license. Teams solving large-scale linear or mixed-integer optimization problems in production who need a fast, well-supported commercial solver.
Pricing

Free and open source; solvers called by Pyomo may themselves be commercial.

Pricing has not been verified yet — see the vendor's site.

Development licenses for the AMPL system and each solver are priced separately from about $300/month and $105/month respectively (billed annually); custom enterprise pricing; free community edition with open-source solvers and a free academic license.

AMPL Development License from $300/month
Solver add-ons from $105/month
Enterprise Custom quote
Community Edition Free

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Free and open source; no vendor pricing page.

Pricing has not been verified yet — see the vendor's site.

Quote-based commercial licenses (named-user, floating, compute-server, cloud); free unrestricted license for academic use, plus a size-limited free license and a 30-day full trial.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features
  • Python-native modeling of variables, constraints and objectives
  • Solver-agnostic: works with Gurobi, CPLEX, Xpress, GLPK, CBC, HiGHS and more
  • Linear, mixed-integer, nonlinear and some stochastic formulations
  • Bilevel and generalized disjunctive programming extensions
  • Integrates with pandas, NumPy and standard Python tooling
  • Model transformation and preprocessing utilities
  • Scriptable for batch runs and sensitivity studies
  • Concise algebraic syntax for variables, constraints and objectives
  • Solver-agnostic: switch solvers without rewriting the model
  • Python, R and cloud APIs alongside the standalone system
  • Sensitivity analysis and automatic differentiation for nonlinear models
  • Set- and index-based data handling for large-scale models
  • Integration with Jupyter notebooks
  • Command-line and scripting interface for batch runs
  • CP-SAT constraint-programming solver
  • Vehicle routing problem (VRP) solver with time windows and capacity constraints
  • GLOP linear-programming solver and MIP wrappers
  • Bin packing and knapsack algorithms
  • Network flow and assignment algorithms
  • Python, C++, Java and C# APIs
  • Pluggable interface to call commercial solvers
  • Simplex and barrier solvers for linear programming
  • Branch-and-cut solver for mixed-integer programming
  • Quadratic and quadratically-constrained programming
  • Multi-core and distributed parallel solving
  • Python, C++, Java, .NET, MATLAB and R APIs
  • Cloud-managed licensing (Web License Service) and Instant Cloud
  • Solution pool and tuning tools for large industrial models

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