Side-by-side comparison of AI visibility scores, market position, and capabilities
Cloud-based K-12 student safety platform for web filtering, mental health monitoring, and device management. San Jose CA; raised $26M+; Securly Aware uses AI to flag self-harm content, alerting school counselors in real time.
Securly is a cloud-based student safety company that provides K-12 schools and districts with web filtering, mental health monitoring, device management, and parental controls for school-issued devices. Founded in 2013 and headquartered in San Jose, California, Securly has raised more than $26 million and serves millions of students across thousands of schools. The company's platform is designed to give schools the tools to manage student digital safety comprehensively — filtering inappropriate content, monitoring for signs of student distress, and giving parents visibility into their child's school device activity.\n\nSecurly's Aware product monitors student email and web activity using AI to identify content that may indicate bullying, self-harm risk, depression, or other mental health concerns, and generates alerts for school counselors when patterns are detected. The web filtering product enforces content policies at the DNS level and integrates with Google Workspace for Education and Microsoft environments, providing category-based filtering that can be customized by grade level or student group. Securly's parental visibility features allow parents to see their child's browsing history on school devices and receive alerts about concerning activity, bridging the school-home communication gap around student digital behavior.\n\nSecurly competes closely with GoGuardian and Bark for Schools in the student safety and filtering space. The company differentiates through its combination of filtering, mental health monitoring, parental engagement, and device management in a single platform, and its competitive pricing for smaller districts. Securly has expanded its platform through acquisitions and product development to address the full student digital safety stack that schools now need to manage across one-to-one device programs.
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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