Systems | Development | Analytics | API | Testing

As enterprises race to deploy multicloud architectures and Agentic AI, they face a common bottleneck: "state." To govern AI token usage, manage agent-to-agent communication, or optimize performance via caching, API and AI gateways require a persistence layer to synchronize data. We’re excited to share the GA of Managed Redis cache for Kong Dedicated Cloud Gateways (DCGW).

A persistent challenge arises as businesses adopt multicloud architectures and agentic AI: the need for state synchronization. API and AI gateways require a robust persistence layer to synchronize data, whether it's for governing AI token usage, facilitating agent-to-agent communication, or boosting performance through caching.

As enterprises start deploying AI agents into real systems, a new architectural challenge is emerging. Agents need a reliable way to discover tools, services, and capabilities dynamically, instead of relying on hardcoded integrations. This is where the Model Context Protocol (MCP) ecosystem is rapidly evolving. MCP servers expose tools and capabilities that AI agents can use. However, once organizations begin deploying multiple MCP servers across environments, the question becomes clear.

In the burgeoning intelligence economy, AI tokens are a metered utility, but enterprise profitability now hinges on a critical distinction: output tokens can cost up to 10x more than inputs, creating a new, invisible risk for cost overruns, particularly with Agentic AI. Learn how Kong AI Gateway and Konnect Metering & Billing provide the essential financial control plane to enforce directional guardrails, protect margins, and turn token consumption into realized revenue.

The AI coding and Agent Harness approach is no longer experimental. This is likely the most impactful agentic AI use case in production today, and Claude Code is one of the solutions really leading the charge. But as engineering teams race to adopt Claude Code across their organizations, a critical question emerges: who's governing all that LLM traffic?

The recent article on OpenAI overhauling its payment systems to reduce its dependency on Stripe highlights an important tension many digital platform builders face today: how to balance usage-based monetization with the realities of payments infrastructure dependency.

We’re proud to share that Kong Insomnia has been recognized as a vendor in the Gartner Market Guide for API and MCP Testing Tools in February, 2026. In a rapidly evolving landscape where AI-driven integration and MCP servers are reshaping how APIs are built, tested, and consumed, being recognized by Gartner validates what our community and customers already know: Insomnia is a serious, enterprise-ready platform for modern API development and testing.

We're excited to announce that Kong just took home the AI Innovator of the Year award from SiliconANGLE Media's 2026 Tech Innovation CUBEd Awards. SiliconANGLE Media runs this annual awards program to recognize companies, technologies, and people moving the needle in B2B tech. Winners go through a review process by industry analysts and experts.

Modern enterprise platforms rarely exist as clean, well-factored systems. They evolve over years or sometimes decades, through acquisitions, reorgs, rewrites, and urgent business priorities. What you’re left with is not a single, unified architecture. It's layer upon layer of architectural decisions made under different leadership, different constraints, and different market conditions.

The APIs that have been powering websites and apps created a massive market, but there are only up to 8 billion humans consuming them behind pixels. As LLMs are taking over the world — in the form of productized agents first — there will be 100X more machines than humans. The internet built for agents will look very different. Agents don't need to see, scroll, and click graphical interfaces. They can access the internet programmatically.