Side-by-side comparison of AI visibility scores, market position, and capabilities
Open-source static analysis for security vulnerabilities and code quality in dozens of languages; commercial Semgrep Code and Supply Chain products serve enterprise security teams at scale.
Semgrep is an application security company founded in 2020 that has raised over $100M and built one of the most widely adopted open-source static analysis tools in the developer security ecosystem. The platform allows security engineers and developers to write custom code analysis rules in a readable pattern-matching syntax that mirrors the code being analyzed, making it far more approachable than legacy SAST tools. Semgrep supports over 30 programming languages and integrates into developer workflows through IDE plugins, pre-commit hooks, and CI/CD pipelines. The company offers Semgrep Code for SAST, Semgrep Supply Chain for dependency vulnerability scanning, and Semgrep Secrets for detecting hardcoded credentials. Semgrep has been widely adopted at major technology companies for internal security rule development and is used by security teams to enforce coding standards at scale. The combination of open-source community adoption and an enterprise SaaS offering has made Semgrep a leading platform-of-record for developer-first application security.
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.
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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