Kubernetes
Rancher
Helm.sh
Docker
Google Kubernetes Engine
Docker Swarm
Amazon AWS
Docker Compose
Webrix
KlavisAI
Docker
Webrix MCP Gateway is enterprise infrastructure for secure AI adoption. It provides a centralized MCP gateway connecting AI agents (Claude, ChatGPT, Cursor) to internal tools (Jira, GitHub, Slack, databases) with SSO authentication, RBAC, audit logging, and guardrails. Employees get instant self-service access to approved tools while security teams maintain full visibility and control. Deploy on-premise, cloud, or SaaS.
Kubernetes WebrixNo Webrix videos yet. You could help us improve this page by suggesting one.
Webrix's answer:
Webrix is the only enterprise MCP Gateway built specifically for AI adoption at scale. Unlike generic API management or agent platforms, we provide purpose-built infrastructure that connects any MCP-compatible AI agent to internal systems through a single secure gateway. Our architecture is built on the open Model Context Protocol standard (avoiding vendor lock-in), provides enterprise-grade security controls from day one (SSO, RBAC, audit trails), and enables self-service tool access without IT bottlenecks. We solve the last-mile problem that blocks AI adoption: giving employees instant, secure access to the internal tools their AI agents need.
Webrix's answer:
Webrix's answer:
AI adoption leaders, VPs of Engineering, CTOs, and technical decision-makers at mid-to-large enterprises (500-5,000+ employees) that build software in-house. These organizations have strong technical capabilities, existing internal tools that need AI integration, and security/compliance requirements that prevent ad-hoc AI tool adoption. Secondary audiences include security teams evaluating POCs, engineering teams wanting faster AI tool access, and IT leaders needing visibility into AI usage and ROI.
Webrix's answer:
Webrix was founded by developers who saw the same pattern repeating across enterprises: employees wanted to use AI tools like Claude, Cursor, and ChatGPT with their internal systems, but security teams had to block access because there was no safe way to connect AI agents to Jira, GitHub, databases, and internal APIs. IT teams were drowning in access requests while developers worked around restrictions. We built Webrix to solve this fundamental infrastructure gap - providing the secure gateway layer that enterprises need to actually adopt AI at scale without compromising security, compliance, or control.
Webrix's answer:
Kubernetes for container orchestration, Helm for deployment management, Docker for containerization, and the Model Context Protocol (MCP) as the core standard for agent-tool communication. Our gateway runs on cloud-native infrastructure with support for PostgreSQL for session management, integrates with standard identity providers (Okta, Azure AD, Google Workspace) for SSO, and uses industry-standard security practices including secrets management, and audit logging.
Webrix's answer:
Based on our record, Kubernetes seems to be more popular. It has been mentiond 392 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
> but it's still a singleton instance, so where do you run it? Most hardware doesn't give you enough uptime for what you need here, because what you actually needed was a re-architecture for distribution / failover / whatever, and while you could ask your LLM to do that you aren't going to run your bank on the result. If only we had a way to solve these issues with tools capable of running Rust programs in that... - Source: Hacker News / 10 days ago
I run the Jenkins controller in Kubernetes. Helm chart for the deploy, persistent volume for the home dir, a sidecar that injects JCasC config from a ConfigMap. Upgrading Jenkins is just bumping a chart version. Rolling back is rolling back a chart version. Plugin lists are values in a Helm values.yaml file, version-pinned, and reviewed in a pull request like any other change. - Source: dev.to / 2 months ago
Does this scenario sound familiar? It's what happened with containerization before Kubernetes. Kubernetes came along and said: Here's the standard. MCP is doing the same thing for AI tooling. - Source: dev.to / 2 months ago
Building your own runtime layer is the right call in a narrow set of scenarios. The open-source ecosystem has matured enough that deep platform engineering teams can stand up their own orchestration layer on top of the official Model Context Protocol Python or TypeScript SDKs. The SDKs implement the MCP specification over JSON-RPC 2.0 and support both stdio for local process communication and Streamable HTTP for... - Source: dev.to / 2 months ago
Amazon Elastic Kubernetes Service (EKS) is a fully managed service from Amazon Web Services (AWS) that makes it easy to run Kubernetes on AWS without needing to install, operate, or maintain your own Kubernetes control plane. It automates cluster management, security, and scaling, supporting applications on both Amazon EC2 and AWS Fargate. - Source: dev.to / 2 months ago
Rancher - Open Source Platform for Running a Private Container Service
KlavisAI - Klavis AI is open source MCP integration plaforms that let AI agents use tools reliably at any scale. You can use our API to automate workflows across multiple apps with managed authentications.
Helm.sh - The Kubernetes Package Manager
Docker - Docker is an open platform that enables developers and system administrators to create distributed applications.
Google Kubernetes Engine - Google Kubernetes Engine is a powerful cluster manager and orchestration system for running your Docker containers. Set up a cluster in minutes.
Docker Swarm - Native clustering for Docker. Turn a pool of Docker hosts into a single, virtual host.