Jenkins vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
47.78% CI/CD market share 2025; 64,517 companies using Jenkins globally; 200,000+ active installations; 11M+ developers; Jenkins Pipeline usage +79% (2021-2023); Monthly jobs: 48.6M; Won 2024 DevOps Dozen Most Innovative Open Source Project
About
Jenkins is an open-source continuous integration and continuous delivery (CI/CD) automation server that has become the foundational infrastructure for software build and deployment pipelines worldwide. Originally developed as Hudson at Sun Microsystems, it was forked and renamed Jenkins in 2011 after Oracle's acquisition of Sun. The project is governed by the Jenkins community and the Continuous Delivery Foundation, with its mission to provide a free, extensible automation engine that enables development teams to build, test, and deploy software reliably and at scale.\n\nJenkins operates as a Java-based server that orchestrates build pipelines through a rich plugin architecture — with over 1,800 community-maintained plugins covering integrations with virtually every version control system, testing framework, cloud provider, and deployment target in the modern software stack. Pipelines are defined as code using Groovy-based Jenkinsfiles, enabling version-controlled, repeatable automation workflows. Jenkins supports both traditional freestyle projects and modern declarative pipeline configurations, making it adaptable to simple single-repo builds and complex multi-service deployment orchestrations alike.\n\nJenkins commands approximately 47.78% of the global CI/CD market as of 2025 and is actively used by 64,517 companies worldwide across over 200,000 active installations, serving an ecosystem of more than 11 million developers. Its position as the open-source CI/CD standard has made it the baseline against which commercial alternatives — including GitHub Actions, CircleCI, and GitLab CI — are compared. Despite competition from hosted CI/CD platforms, Jenkins' flexibility, plugin depth, and zero licensing cost continue to drive adoption across enterprises and engineering organizations managing complex, heterogeneous build environments.
Serverless GPU cloud platform for AI/ML with Python-native deployment and per-second billing; developer-favorite scaling from zero competing with Replicate and Beam for AI compute.
About
Modal is a serverless cloud computing platform purpose-built for AI and machine learning workloads — providing on-demand GPU compute that scales instantly from zero with per-second billing, container management, distributed training support, and a Python-native developer experience that makes running ML workloads in the cloud feel as simple as running code locally. Founded in 2021 in New York City and backed by Redpoint Ventures and other investors, Modal has grown rapidly as AI development has accelerated demand for flexible, developer-friendly GPU infrastructure.\n\nModal's developer experience is its primary differentiator — engineers write Python functions decorated with @modal.function() and deploy them to the cloud with a single command, with Modal handling container building, GPU provisioning, auto-scaling, and execution. The platform supports training jobs that need distributed compute across multiple GPUs, model serving endpoints that scale to zero when unused (eliminating idle GPU costs), and batch inference jobs that process large datasets. The per-second billing model means developers pay only for actual compute time, not provisioned instances.\n\nIn 2025, Modal competes in the AI infrastructure market with Replicate, Beam, Banana, and major cloud providers' managed ML services (AWS SageMaker, Google Vertex AI, Azure ML) for serverless GPU compute. The market for AI-specific cloud infrastructure has grown dramatically as the number of ML engineers deploying models to production has expanded — traditional cloud providers require significant DevOps expertise to use GPU instances effectively, while Modal's Python-native approach reduces the barrier to entry. Modal has attracted a strong developer following among AI researchers and ML engineers building production AI applications. The 2025 strategy focuses on growing the developer community, adding enterprise features (dedicated GPU capacity, private networking, compliance), and expanding the hardware options available (H100 GPUs, custom accelerators).
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