Harness the power of AMPL’s APIs to connect optimization models directly with your existing software, data pipelines, and development workflows. Whether you’re working in Python, C++, Java, or RESTful APIs, AMPL provides flexible integration options that eliminate friction and streamline automation. Embed, extend, and optimize—without changing your tech stack.
Seamlessly integrate AMPL into your existing applications and data pipelines using its Python, C++, and Java APIs. Automate model execution, parameter updates, and result retrieval—no manual intervention needed.
Connect AMPL with enterprise systems, machine learning models, and cloud services to enhance decision-making at scale. Use APIs to integrate optimization directly into financial planning, logistics, and AI-driven analytics.
Embed AMPL into your architecture without third-party middleware, ensuring fast, reliable access to solvers and models. Leverage high-performance computing, cloud deployments, and containerized workflows for secure optimization at scale.
Whether you’re running AMPL locally, in the cloud, or within a CI/CD pipeline, its APIs provide flexible, production-ready integration. Deploy optimization models as microservices or embed them into real-time decision systems with ease.
Use AMPL with Python through the amplpy package, enabling dynamic model interaction, data manipulation, and solution analysis.
Combine AMPL with R for statistical modeling and advanced data analysis.
Use AMPL within C# applications to build enterprise and .NET-based optimization solutions.
Integrate AMPL directly into high-performance C++ applications for fast and efficient optimization.
Leverage AMPL in Java for seamless integration with large-scale business and analytics applications.
Use AMPL within MATLAB for mathematical computing and simulation-driven workflows.
Create an account, download a free license, and start integrating your data today. Or contact us to learn more about key integrations for your workflow.
See AMPL, integrations, and enhanced workflows in action.