|
By Kong
Enterprise AI adoption is accelerating. In PwC's April 2025 survey of 308 US business executives, 88% said they plan to increase AI-related budgets in the next 12 months . But scaling AI from pilot to production exposes a structural problem most teams discover too late: **direct LLM API integration** creates fragility at scale. The question is not whether your organization will consume multiple LLMs. It is how you will govern that consumption without building bespoke infrastructure for every provider.
|
By Kong
LLM provider switching went from a theoretical concern to an operational emergency in June 2026, when Anthropic disabled Claude Fable 5 and Mythos 5 following a US government directive . The shutdown was swift, with access suspended just days after the models launched. Enterprises that had built production workflows around those models lost access overnight. The event was a wake-up call, but the underlying risk had been building for years.
|
By Kong
In late June 2026, two of the most widely used AI agent platforms were compromised within the same week. Langflow disclosed a critical unauthenticated remote code execution flaw. Dify, powering over one million applications, revealed four vulnerabilities that exposed private conversations and internal APIs across tenant boundaries. These weren't theoretical risks. They were production exploits hitting real infrastructure.
|
By Hugo Guerrero
Generative AI systems do not fail because models are weak. They fail because architectures are incomplete. Once organizations accept that prompts cannot guarantee reliability, a new challenge emerges: how to design systems that systematically convert successful AI behavior into repeatable, governable, and auditable workflows.
|
By Hugo Guerrero
Generative AI has completely changed the landscape of enterprise automation, knowledge work and operational efficiency. In 2026, the question is no longer whether these models can perform complex tasks, but whether they can do so reliably enough for mission-critical systems. Despite the availability of sophisticated models and expansive context windows, technology leaders continue to face frustration. Organizations struggle to produce consistent and repeatable results.
|
By Juhi Singh
As API ecosystems grow, developers and platform teams often work in separate environments. Platform teams manage APIs, gateways, and governance centrally, while developers recreate those configurations locally for testing and debugging. Over time, this can lead to configuration drift, inconsistent workflows, and security gaps. The release introduces our first native Kong Konnect integration, allowing developers to discover, import, and test Gateway configurations directly from Konnect.
|
By Claudio Acquaviva
AI agents move fast. Securing them shouldn't slow you down. Kong and Noma are partnering to change that. This program delivers high-quality, reliable integrations providing real business value for customers..
|
By Datong Sun
Each change to Kong Gateway's codebase triggers a comprehensive test suite that runs more than 17,000 * 2 = 34,000 test cases among the two primary architectures (x86 and ARM) we support. This process takes about 23.5 hours on a single machine. But we don't wait that long. A large fleet of machines runs the suite in parallel, and we shard the work aggressively so each commit finishes in a fraction of that time. That setup works well, right up until flaky tests get involved.
|
By Kong
Kong and Persistent Systems partner to make migrating off old API management platforms faster and lower risk Legacy API management platforms were built for a different era. They weren't designed for microservices, multi-cloud deployments, or AI workloads. They're expensive, rigid, and hold engineering teams back. The problem is that migration has always felt hard. APIs are load-bearing infrastructure. Policies are complex. Risk is real. So the old platform stays, and the technical debt compounds.
|
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.
|
By Kong
Learn how Kong Konnect's new unified catalog brings APIs and services together into a single source of truth, helping organizations manage the full lifecycle of their API platform — from design to governance to consumption.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
|
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.
- July 2026 (3)
- June 2026 (7)
- May 2026 (18)
- April 2026 (17)
- March 2026 (16)
- February 2026 (14)
- January 2026 (18)
- December 2025 (13)
- November 2025 (9)
- October 2025 (19)
- September 2025 (18)
- August 2025 (18)
- July 2025 (22)
- June 2025 (15)
- May 2025 (12)
- April 2025 (27)
- March 2025 (15)
- February 2025 (10)
- January 2025 (8)
- December 2024 (12)
- November 2024 (9)
- October 2024 (9)
- September 2024 (9)
- August 2024 (10)
- July 2024 (12)
- June 2024 (15)
- May 2024 (9)
- April 2024 (12)
- March 2024 (11)
- February 2024 (18)
- January 2024 (11)
- December 2023 (9)
- November 2023 (16)
- October 2023 (17)
- September 2023 (12)
- August 2023 (13)
- July 2023 (11)
- June 2023 (21)
- May 2023 (13)
- April 2023 (27)
- March 2023 (13)
- February 2023 (17)
- January 2023 (9)
- December 2022 (10)
- November 2022 (16)
- October 2022 (12)
- September 2022 (13)
- August 2022 (12)
- July 2022 (12)
- June 2022 (14)
- May 2022 (12)
- April 2022 (7)
- March 2022 (15)
- February 2022 (18)
- January 2022 (26)
- December 2021 (26)
- November 2021 (28)
- October 2021 (31)
- September 2021 (28)
- August 2021 (21)
- July 2021 (21)
- June 2021 (29)
- May 2021 (28)
- April 2021 (14)
- March 2021 (11)
- February 2021 (13)
- January 2021 (11)
- December 2020 (24)
- November 2020 (18)
- October 2020 (14)
- September 2020 (6)
- August 2020 (8)
- July 2020 (11)
- June 2020 (5)
- May 2020 (5)
- April 2020 (7)
- March 2020 (7)
- February 2020 (7)
- January 2020 (6)
- December 2019 (6)
- November 2019 (10)
- October 2019 (9)
- September 2019 (5)
- August 2019 (6)
- July 2019 (4)
- June 2019 (2)
- May 2019 (4)
- April 2019 (2)
- March 2019 (4)
- February 2019 (5)
- January 2019 (7)
- December 2018 (9)
- November 2018 (10)
- October 2018 (3)
- September 2018 (1)
- July 2018 (1)
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?