Smack Technologies vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Defense AI company founded by MARSOC veterans; raised $32M to build first frontier AI lab exclusively for US national security; targets classified compute and defense-native AI agents
About
Smack Technologies is a defense AI company founded by US Marine Corps Special Operations Command (MARSOC) veterans to build the first frontier AI laboratory dedicated exclusively to the United States defense and national security mission. The company was created with the conviction that commercial frontier AI development — the kind happening at OpenAI, Anthropic, and Google DeepMind — has critical applications for defense that require a specialized organization with security clearances, operational experience, and a defense-native culture to properly develop and deploy.\n\nSmack is building AI systems designed for the unique demands of defense operations: high reliability in degraded or denied communications environments, integration with classified data and systems, and operational security requirements that commercial AI vendors cannot easily satisfy. Its founders bring firsthand experience of the capability gaps that exist between what commercial AI can do and what warfighters and intelligence professionals actually need in the field. The company operates at the intersection of frontier model capabilities and defense-grade engineering requirements.\n\nSmack raised $32M in March 2026 to build out its team of AI researchers, defense software engineers, and operational advisors. The company represents a growing category of defense-native AI startups distinct from contractors simply reselling commercial AI APIs — instead building purpose-designed systems from the ground up for classified and operational defense contexts. As the US military accelerates AI adoption across all domains, Smack is positioned as a foundational lab rather than a product company, aiming to be a long-term R&D partner for the defense community.
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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