Compare Gravitee vs. Spherical Defense

Gravitee is an enterprise platform for managing AI agents, APIs, event streams, and model interactions through a centralized security and governance layer. Its AI Gateway provides three primary entry points through an LLM Proxy, MCP Proxy, and A2A Proxy for governing model access, agent tools, and agent-to-agent communication. The platform can assign and verify agent identities, enforce least-privilege policies, and control which tools, services, and MCP servers individual agents are permitted to use. Security controls include authentication, authorization, PII filtering, API key protection, rate limiting, spending limits, and complete audit trails for AI activity. Gravitee also tracks LLM usage and costs so organizations can understand token consumption, control unplanned spending, and attribute AI usage across users and agents. Its observability features help teams monitor agent behavior, dependencies, traffic flows, and potentially risky or ungoverned interactions. The platform extends these capabilities to conventional APIs and event infrastructure, including REST, GraphQL, gRPC, Kafka, and other enterprise integration technologies. Organizations can also productize internal APIs, MCP tools, workflows, and event streams so they can be securely reused by developers, partners, and AI agents. Gravitee is intended for enterprises, platform teams, security organizations, and AI engineering groups that need a common management layer for agent security, API governance, observability, and AI infrastructure.

Spherical Defense is an innovative API security solution that leverages deep unsupervised learning techniques to safeguard your APIs effectively. The Spherical Defense Express variant is designed for deployment on AWS and can be downloaded in just one minute, starting its protective measures within two hours at an economical rate of $1 per hour. After setting up your Spherical instance, it begins monitoring API traffic right away and continues to do so until it gathers enough data for initial model training. Once approximately 16,000 requests are processed, the system transitions to the training phase, where it refines its security model over about six hours before undergoing evaluation. As the system continues to receive new data, it adapts by training additional models to reflect changes in your API's traffic patterns over time. The process of training and adapting ensures ongoing protection, making it a dynamic and responsive security solution for your APIs. After the evaluation of the trained security model, the system is better equipped to handle future threats effectively.