Onyx Security vs Modal
Side-by-side comparison of AI visibility scores, market position, and capabilities
Israeli AI agent security startup protecting enterprise AI agent deployments from prompt injection and model compromise. $40M raised (March 2026); Fortune 500 customers; founded 2024; addresses the emerging AI agent attack surface.
About
Onyx Security is an Israeli AI agent security startup founded in 2024 to address the emerging attack surface created by enterprise AI agent deployments. As organizations deploy autonomous AI agents that can take actions, access data, and interact with external services, Onyx identified a critical security gap: existing enterprise security tools were built for human users and traditional software, not for AI agents that operate autonomously and can be manipulated through prompt injection, model compromise, or tool misuse. Onyx was founded to build the security infrastructure layer that makes enterprise AI agents safe to deploy at scale.\n\nOnyx's platform monitors, governs, and protects enterprise AI agent deployments in real time — detecting anomalous agent behavior, enforcing least-privilege access policies, preventing prompt injection attacks, and providing audit trails for agent actions. The product is designed for Fortune 500 enterprises deploying AI agents in business-critical workflows where a compromised or misbehaving agent could exfiltrate data, take unauthorized actions, or cause operational harm. Onyx integrates with leading agentic AI platforms and enterprise security infrastructure without requiring architectural changes to existing agent deployments.\n\nOnyx Security raised $40 million in March 2026 and counts Fortune 500 companies among its early customers — a remarkable milestone for a company less than two years old. The funding and customer traction validate both the urgency of the AI agent security problem and Onyx's technical approach. The company is positioned to lead the AI agent security category as enterprise agentic deployments accelerate globally and CISOs begin to demand purpose-built security controls for AI systems operating in production environments.
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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