CrowdStrike vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
CrowdStrike (CRWD) reported $3.95B ARR in FY2025 (ended Jan). Revenue $3.74B, up 29% YoY. Market cap ~$85B. 8,600+ employees. Austin, TX. AI-native cybersecurity platform. Charlotte AI for threat detection.
About
CrowdStrike is an AI-native cybersecurity company founded in 2011 by George Kurtz, Dmitri Alperovitch, and Gregg Marston and headquartered in Austin, Texas, that built the endpoint detection and response (EDR) category and has since expanded into the broadest cloud-native cybersecurity platform in the industry. The company was founded on the insight that traditional antivirus software — signature-based, retrospective, and endpoint-isolated — could not keep pace with sophisticated adversaries operating at machine speed. CrowdStrike's founding architecture, the Falcon platform, was designed cloud-native from day one: a single lightweight agent on the endpoint feeding a cloud-based AI that learns from trillions of security events across every customer simultaneously. The company trades on Nasdaq under the ticker CRWD.\n\nThe CrowdStrike Falcon platform consolidates more than 28 security modules across endpoint security, identity threat protection, cloud security, next-gen SIEM and log management, threat intelligence, and managed detection and response — all delivered through a single agent and unified console. The AI at the platform's core, Charlotte AI, provides conversational security operations, automated investigation, and AI-generated threat summaries that reduce analyst workload. CrowdStrike's threat intelligence team, Adversary Intelligence, tracks and names nation-state and criminal threat actors globally, giving customers predictive insight into campaigns before they hit their environments.\n\nCrowdStrike reported $3.95 billion in annual recurring revenue (ARR) for FY2025 and total revenue of $3.74 billion, up 29% year over year, with a market capitalization of approximately $85 billion. The company has 8,600+ employees and counts a substantial share of the Fortune 500 and global governments as customers. Despite the July 2024 sensor update incident that caused a significant IT outage affecting millions of Windows systems globally, CrowdStrike's customer retention remained strong — a testament to the platform's depth of integration and the switching costs built into its consolidated architecture.
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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