llm-d is an open-source, Kubernetes-native framework for serving large language models at scale. It doesn't run models itself. It sits in front of inference engines such as vLLM, another open-source, high-throughput serving engine, and decides how requests are spread across them. Teams taking a…
Kong AI Gateway sees every model call your teams make, so it knows who spent what, on which model, shortly after each request. Your FinOps platform holds the invoices from your cloud and model providers and runs showback and chargeback for each business unit. Put the two together and each business…
TL;DR? Event Gateway 1.3 introduces the following: Have you ever watched somebody pump their own gas? Nobody's standing there with a clipboard. You swipe a card, pick a grade, and the pump takes it from there, because the pump has the rules built in: a pre-authorized amount, an automatic shutoff…
Agents are moving from demos into production, and the platform teams running them keep hitting the same four problems: spend nobody can attribute, credentials that security cannot manage, enterprise APIs rewrapped as agent tools team by team, and no single view of what an agent actually did. Today…
Every request returned 200 OK, the task still failed. This is the problem in one sentence. A single user intent now fans out into multiple model calls, tool invocations, MCP (Model Context Protocol) interactions, policy executions, retries, and agent handoffs. Each call can look healthy on its own…
TL;DR? You’ve been asked to make a slate of popular services like GitHub, Figma, or Salesforce available as MCP servers to agents inside your organization. Your employees expect to access these through their existing single-sign-on provided by your corporate identity provider. Your security team…
As enterprises put more AI applications and agents into production, managing the associated costs is becoming an increasingly important operational challenge. AI spending is distributed across multiple providers, models, applications, and teams, making it difficult to understand where costs…
AI agents are becoming increasingly capable, but their usefulness depends on what they can access. An agent that can reason through a customer support issue is of limited value if it can’t retrieve customer information, check an order status, or initiate a refund. Similarly, an IT operations agent…
TL;DR: This Kong employee profile highlights Noah Koeck's progression at Kong from London-based SDR to Commercial Account Executive in the DACH region, demonstrating how ambition, teamwork, and proactive learning drive career success in the AI connectivity era. From growing up in the Austrian Alps…
At Kong, we fully agree with Gartner’s recent reporting on the emerging marketing of AI FInOps tools. AI FinOps must not be an afterthought but should be a critical and mandatory component for any enterprise AI program. The explosive growth of AI workloads from simple AI-assisted coding to product…
TL;DR: Kong Insomnia 13.4 is all about giving you more visibility than ever before. Whether it's your git changes, your secrets, or how your users are using Insomnia, you’re able to see more than ever before and make better decisions than ever. We’ve gotten great feedback on the native git support…
Today ServiceNow launched the ServiceNow Control Tower Alliance , and Kong is proud to be a founding member to help enterprises put AI to work safely, at scale. It's a milestone for Kong, and we think it's an important moment for the industry. Here's what the alliance is, why it matters, and why we…
When an application calls a model provider directly, the service needs the provider's API key, and any control over that traffic has to be built into the service itself. For one service calling one model, that is the right amount of machinery. It holds up less well as the application grows. Every…
A healthy API and a successful model call do not prove that an AI application produced a useful result. AI observability connects logs, metrics, and traces with evidence about behavior, retrieval, tool use, quality, safety, and token consumption. That combined view helps AI engineers explain what…
AI governance is an operating model organizations can use to keep AI systems accountable, safe, observable, and aligned with applicable requirements. It can combine decision rights, lifecycle processes, and technical controls across models, agents, tools, data, and APIs. [1] [2] [3] For enterprise…