Persistent Systems and Kong announces strategic partnership to scale enterprise AI adoption
Persistent Systems and Kong, a leading developer of API and AI connectivity, today announced a strategic partnership to help enterprises implement the control layer required to scale AI securely and reliably. Persistent is uniquely positioned as Kong's global systems integration partner, combining its engineering-led approach with Kong's unified API and AI connectivity platform to simplify integration, strengthen governance and accelerate enterprise AI adoption.
As enterprises move from AI experimentation to production, the challenge is no longer access to models. It is how AI systems are connected, governed and operated at scale. APIs, data pipelines, models and agents are converging into a single operational fabric. Without a unified control layer, this fabric becomes fragmented, difficult to govern and increasingly complex to scale.
This partnership addresses that gap by enabling enterprises to implement Kong's governed, scalable connectivity layer across APIs, data and AI services. Together, Persistent and Kong will enable organizations to modernize legacy API environments, strengthen governance and reduce operational costs, while supporting high-performance workloads seamlessly across hybrid and multi-cloud environments.
The collaboration also enables enterprises to operationalize GenAI and agentic workflows, including Model Context Protocol-based architectures, with built-in security, observability and policy-driven control. This includes policy-driven safeguards such as Personally Identifiable Information (PII) protection, centralized access management and end-to-end observability, ensuring enterprise-grade security and compliance across API and AI interactions.
The partnership advances Persistent's AI-first, platform-driven approach by creating a more predictable path from AI ambition to enterprise-scale execution. By combining Kong's AI Gateway and unified API and AI connectivity platform with Persistent's GenAI Hub and engineering-led delivery, organizations can move beyond isolated use cases toward governed, production-grade AI systems with greater control, lower risk and faster realization of business value.
