Side-by-side comparison of AI visibility scores, market position, and capabilities
SSPM platform acquired by CrowdStrike in 2023; deep security configuration management across 150+ SaaS applications. Tel Aviv Israel; now powers CrowdStrike Falcon SaaS Security, delivering posture management natively inside the Falcon platform for enterprise SOC teams.
Adaptive Shield is a SaaS security posture management (SSPM) company founded in 2019 by Maor Bin and Jony Shlomoff in Tel Aviv, Israel. The company pioneered the SSPM category, which Gartner formally named as a distinct security market in 2021. Adaptive Shield's platform connects to an organization's SaaS applications via native APIs and continuously monitors security configurations, user permissions, and integration settings against security best practices and compliance frameworks. When a configuration drifts from the desired state — for example, multi-factor authentication being disabled for a user, or a permissive external sharing setting turned on — the platform alerts security teams and provides remediation guidance.\n\nAdaptive Shield raised $30 million before being acquired by CrowdStrike in 2023 for approximately $300 million. CrowdStrike integrated Adaptive Shield's SSPM technology into its Falcon platform as Falcon SaaS Security, giving CrowdStrike customers deep visibility into the security posture of their SaaS estates alongside CrowdStrike's existing endpoint, identity, and cloud security capabilities. The acquisition reflected CrowdStrike's strategy to expand from endpoint-centric security to comprehensive platform coverage.\n\nPrior to acquisition, Adaptive Shield supported security configuration checks across more than 150 SaaS applications including Microsoft 365, Google Workspace, Salesforce, ServiceNow, Zoom, Slack, GitHub, and Workday. Its check library covered thousands of individual configuration settings mapped to CIS Benchmarks, SOC 2, and other frameworks. The platform also provided an integration risk monitoring capability that inventoried third-party apps connected to core SaaS platforms, similar to the SSPM expansion toward full SaaS security management.
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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