Developers need solutions that are scalable, efficient, and easy to integrate into existing software architectures. AMPL provides powerful APIs that let you connect optimization models to your applications, data pipelines, and cloud-based workflows.
Whether you’re building a custom decision-support system, integrating optimization into an AI/ML pipeline, or embedding solvers into an enterprise platform, AMPL offers the APIs, deployment flexibility, and automation tools to make it seamless.
With support for Python, C++, Java, and REST APIs, AMPL allows you to build, automate, and deploy optimization workflows efficiently.
Data
Solvers
APIs
Deploy
Editors
Books
Models
AMPL's modeling system is used extensively in finance, energy, supply chain optimization and more.
Extensive integrations allow for a complete, one-stop-shop optimization platform.
Thousands of modelers choose AMPL for complex projects where tasks are critical and must be modeled to reflect the real-world problem while maintaining reliability.
AMPL offers a suite of powerful, well-documented APIs to seamlessly integrate optimization into your tech stack:
AMPL is built to fit into modern DevOps and automation environments, making it easy to embed in enterprise applications.
Developers thrive on great documentation, and AMPL provides everything you need to get started and go deep.
AMPL provides developer-friendly resources to help you integrate optimization into your applications.
Explore full API documentation and sample code for Python, C++, Java, and REST.
Get started quickly with real-world examples tailored for software development.
Learn how to deploy AMPL models in cloud environments like AWS, Azure, and Google Cloud.
Connect with other developers and AMPL experts for troubleshooting, guidance, and best practices.
AMPL provides a fully featured Python API that allows you to define models, set parameters, and retrieve solutions programmatically. You can easily call AMPL inside pandas, NumPy, or SciPy workflows and integrate it with machine learning frameworks like TensorFlow or scikit-learn.
Yes! AMPL supports REST API-based remote solving, making it easy to deploy optimization models as part of a cloud-based service. It also works with Docker, Kubernetes, and AWS Lambda for scalable deployment.
Unlike general-purpose optimization libraries (such as PuLP or OR-Tools), AMPL provides:
Yes! AMPL works inside Docker containers, making it easy to deploy in microservices, CI/CD pipelines, and scalable backend systems.
Access a complete optimization application building platform with simple pricing, or contact us to design a custom package specifically for your business needs.
Get in touch to book a time for us to talk about your specific needs, and demo real solutions.
From startups to Fortune 500s, explore how hundreds of businesses use AMPL to optimize complex problems.