Abnormal Security vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
San Francisco AI email security (private, $5.1B valuation); $250M Series D (Wellington Management), behavioral AI detects novel BEC/phishing in Microsoft 365/Google Workspace competing with Proofpoint and Microsoft Defender.
About
Abnormal Security is a San Francisco, California-based AI-native cloud email security company — founded in 2018 by Evan Reiser (CEO) and Sanjay Jha (CTO), both former Google engineers — using behavioral AI to detect and block business email compromise (BEC), phishing, ransomware, account takeovers, and AI-generated social engineering attacks against enterprise email environments (Microsoft 365, Google Workspace) through approximately 600 employees. The company raised a $250 million Series D funding round at a $5.1 billion valuation, led by Wellington Management with participation from existing investors Greylock Partners, Menlo Ventures, Insight Partners, and the CrowdStrike Falcon Fund — bringing Abnormal's total funding to approximately $580 million. Abnormal Security's approach to email security is fundamentally different from traditional signature and rule-based approaches: rather than scanning emails for known malware hashes or suspicious links against threat databases, Abnormal's AI models each employee's historical communication behavior (who they email, how they write, what times they send messages, what financial requests they make) and flags deviations from established baselines as anomalies requiring investigation — enabling detection of novel BEC attacks that have no prior signature, AI-generated phishing that passes link reputation checks, and vendor invoice fraud that impersonates legitimate suppliers.
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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