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By Alex Drag
Every few months, a frontier AI lab makes a move that says the quiet part out loud: agents are only as useful as the systems they can reach. The latest example is Anthropic's acquisition of Stainless, the company behind the tooling that turns API specs into SDKs and MCP servers. Anthropic's own framing is direct. Agents need to connect to data and tools, and the path from an API to an agent-ready interface needs to get shorter. We agree. We've been making a version of this argument for two years.
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By Claudio Acquaviva
Customers today face significant challenges as their Kubernetes environments scale. The proliferation of microservices, external integrations, and new AI workloads increases traffic volume and connectivity complexity, creating material risks to performance and availability. The core issue is a lack of end-to-end governance: as diverse workloads expand, unmanaged interactions make it difficult to apply consistent security and enforce global consumption policies.
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By Dan Temkin
Your AI infrastructure is producing financial signals your CFO has never seen. Token consumption is a direct cost line item. Cache hit rate is a margin improvement. Model routing decisions are cost arbitrage events. These things are happening right now, in the gateway layer, with no route to the CFO, which means no route to the boardroom. As the AI connectivity platform owner, you're the person who can build that route.
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By Hugo Guerrero
There's a reason we don't find our keys by scanning every room like a security camera. We replay the tape. We remember the groceries, the front door, the distraction. We reconstruct the *why* to find the *where*. Our brains are commit logs, not snapshots. Most agentic AI systems today work more like the camera — a static frame of the world at a given moment. They store state. They retrieve context. They produce an answer.
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By Kong
A guide to learning how MCP registries help govern AI agent-to-tool connectivity AI agents are only as capable as the tools they can reach. When an agent needs to query a database, file a support ticket, or pull data from a CRM, it has to find the right tool, authenticate, and invoke it — all at runtime. The Model Context Protocol (MCP) standardizes how agents communicate with these tools. But MCP alone does not answer a fundamental question: how does the agent know which tools exist?
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By Dan Temkin
The ‘AI Credit’ Economy: GitHub’s Pricing Shift Is the Beginning, Not the Exception *GitHub just sent waves of budget panic across its developer base. Seat-based Copilot pricing is out. Consumption-based credits are in. And if you're building an AI-driven product today on flat-rate pricing? You're building a problem into your roadmap.* Seats aren't going away, but they now fund a shared pool of AI credits (one credit = one cent) instead of unlocking uncapped use.
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By Heather Halenbeck
Three-quarters of enterprises are now piloting or deploying AI agents. But here’s the problem: actually integrating those agents with enterprise applications is proving to be one of the hardest parts of the whole endeavor. The research doesn’t mince words about the challenge. And it maps directly to the infrastructure gap Kong was built to address..
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By Claudio Acquaviva
As organizations transition from experimental AI to production-grade systems, they often face a fragmented landscape of unmanaged LLM providers, complex tool integrations, and escalating security risks. This infrastructure gap leaves AI applications vulnerable to sophisticated threats like prompt injection and data exfiltration, necessitating a unified stack that secures the edge while streamlining the data plane..
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By Hugo Guerrero
Most engineering teams adopt Apache Kafka for one simple reason: it works. It scales effortlessly, it is incredibly reliable, and it powers real-time systems across almost every industry. But as your Kafka usage expands across different teams, regions, and external consumers, success creates a brand new problem. Kafka is a massive data firehose, and without the right nozzle, it quickly becomes unmanageable.
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By Kong
Kong's AI Gateway applies the same architectural pattern as the API Gateway — now governing LLM, MCP, and agent traffic at the infrastructure layer. Just as API gateways abstracted rate limiting, auth, and caching across microservices, AI gateways do the same for large language models and agents — with token budgets, semantic caching, and semantic routing replacing their REST equivalents. Kong breaks this into three layers: LLM Gateway, MCP Gateway for tool calls, and Agents Gateway for agent-to-agent traffic.#Shorts.
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By Kong
Most AI revenue will flow through tokens — and the two bottlenecks are tokens per watt (energy cost) and tokens per second (throughput). Tokens per watt determines how much output you can generate from a fixed energy supply — already constrained and getting tighter. Tokens per second sets the ceiling on how fast that revenue can flow. Kong's AI Gateway optimizes both at the connectivity layer: semantic caching and semantic routing increase token output without adding watts or latency.#Shorts.
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By Kong
LLMs are absorbing the business logic of microservices for agentic use cases — but both patterns will coexist in enterprise infrastructure for a long time. Cloud-native infrastructure (microservices + APIs) keeps powering web and mobile experiences. The agentic layer — LLMs, MCP tool calls, and context traffic — runs in parallel, activating the same APIs and CRUD operations underneath. Kong manages both swim lanes: the API traffic between clients and microservices, and the context traffic flowing between agents and LLMs.#Shorts.
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By Kong
CLI offers speed and developer freedom for API access; MCP provides centralized security, governance, and observability at enterprise scale. With CLI, credentials live on the developer's local machine and audit trails are shell-only — fast, but ungoverned. MCP adds authentication, centralized policy enforcement, and observability across all API calls, at the cost of some speed and higher token consumption. Kong's MCP Gateway is built for teams that need the governance trade-off without giving up too much velocity.#Shorts.
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By Kong
Software is going headless: the internet is shifting from GUIs built for humans to APIs, MCP servers, and CLIs built for machines and agents. Machines will consume the internet at a scale 1,000x greater than humans — more agents will exist than people, and programmatic access moves far more data than any click ever could. This transition requires API and AI infrastructure capable of moving terabytes at a scale never built before. Kong provides the connectivity layer for this machine internet — the infrastructure between agents, LLMs, and the services they consume.#Shorts.
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By Kong
The context graph — not the UI layer or system of record — is the true competitive IP of the AI era, and Kong built Context Mesh to help companies govern it. Without the right context layer, AI agents are generic and interchangeable regardless of which LLM is underneath. Companies that own and protect their context graph can differentiate their agentic workflows; those that don't are left with legacy CRUD backends that don't translate to agentic use cases. Context Mesh gives enterprises policy and governance over what agents can consume — the rulebook for all context flowing in and out.#Shorts.
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By Kong
Want to bill customers for the AI tokens they actually use? This video shows you how to set up a LangChain app that meters LLM token usage and streams it to Kong Konnect Metering & Billing as CloudEvents — turning every prompt and response into invoiced usage, automatically.
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By Kong
Learn how to securely manage API keys and sensitive data in Insomnia using three layers: Shared Environment (safe for Git) Private Environment (local-only secrets) External Vaults with HashiCorp Vault This demo shows how to avoid committing secrets, collaborate safely, and dynamically fetch credentials without exposing them.
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By Kong
Learn how Kong and Traceable combine to deliver a unified API lifecycle management and security platform that protects your APIs from design time through production — closing the gap between what APIs are designed to do and how they're actually used. Key Takeaways.
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By Kong
In this eBook, Kong Co-Founder and CTO Marco Palladino illustrates the differences between API gateways and service mesh - and when to use one or the other in a pragmatic and objective way.
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By Kong
In this eBook, Kong Co-Founder and CTO Marco Palladino breaks down how Kuma now supports every cloud vendor, every architecture and every platform together in a multi-mesh control plane. When deployed in a multi-zone deployment, Kuma abstracts away both the synchronization of the service mesh policies across multiple zones and the service connectivity (and service discovery) across those zones.
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By Kong
We live in an exciting time for software; we are witnessing a monumental shift in how applications are built. We have the opportunity to participate in the large-scale movement from centralized applications to decentralized, highly performant software architectures.
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By Kong
To better prepare for the future, it's important to get a solid understanding of this rising technology trend. In this e-book, we examine cloud native architecture, look back at the rise of cloud native app development, and explore the future of cloud native on the entire software ecosystem.
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By Kong
This eBook explains how microservices can facilitate the adoption of a multi-cloud strategy. Included are a holistic overview of the multi-cloud pattern including the benefits and drawbacks, strategies for adoption, and challenges to overcome if adopting the strategy without microservices.
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By Kong
Performance is a critical factor when choosing an API management solution. For businesses, the need to deliver low latency and high throughput is critical to ensuring that API transaction rates keep up with the speed of business. This white paper compares the performance of Kong and Apigee to understand performance in production environments.
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By Kong
This ebook explains the process for transitioning from a monolithic to a microservices-based architecture. Included are technical aspects and common mistakes to avoid.
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By Kong
This ebook explains how Kubernetes is modernizing the microservices architecture. Included are a deep dive into the history of Kubernetes and containers, the technical and organizational benefits of using Kubernetes for container orchestration, as well as considerations for adopting it.
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By Kong
This eBook compares a monolithic vs microservices architectural approach to application development. It dives into the benefits and challenges of microservices and helps you determine whether a transition to microservices would be right for your organization.
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By Kong
This ebook explains the role that the service mesh pattern plays in the leap towards de-centralized architectures. A novel re-packaging of the functionalities of traditional API gateways, service mesh represents the next stage in the natural evolution of microservices.
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Next-Generation API Platform for Modern Architectures. Connect all your microservices and APIs with the industry's most performant, scalable and flexible API platform. Empower your developers to build and optimize APIs. Leverage the latest microservice and container design patterns.
The Service Control Platform transcends API management to intelligently broker information across all your services. With Kong’s fast, flexible, and lightweight core, you control your entire service architecture – centralized or decentralized, microservices or monolith. Kong Service Control Platform transforms your static endpoints into a dynamic network of intelligent services.
Built for Modern Architectures:
- Connect Everything: Use plugins to extend and connect services across hybrid and multi-cloud environments, regardless of vendor.
- Accelerate Innovation: Use Kong's robust library of plugins to reduce redundant coding tasks across teams, technologies and geographies.
- Improve Governance: Analyze real-time data to ensure adherence to policies across teams, partners and individual endpoints.
- Automate End-to-end: Connect Kong with automation tools. Generate custom workflows to improve efficiency and reduce errors.
- Unlock New Ecosystems: Instantly leverage new ecosystems. Deploy Kong with Kubernetes, containers, and more out of the box.
- Increase Compliance: Limit access with role-based access control (RBAC). Encrypt end-to-end to comply with industry regulations.
Go Beyond the Gateway. Ready for the next-generation of API platforms?