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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.
Amazon EKS
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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, Amazon EKS seems to be more popular. It has been mentiond 79 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.
On managed Kubernetes platforms like EKS, this has a second benefit: the cluster autoscaler pays attention to resource requests when deciding whether to add new nodes. - Source: dev.to / about 1 month ago
After returning from AWS Summit London 2026 I was doing some research on running AI/ML workload in AWS EKS with Karpenter. With some assistance from Gemini I turned some of my notes from various talks into this guide that will talk through the intricacies of deploying and scaling Generative AI (GenAI) workloads on AWS EKS, leveraging the power of Karpenter. - Source: dev.to / 2 months ago
This post is a small step in that direction: serving an LLM using vLLM, deployed on Amazon EKS, provisioned the infra using AWS CDK, and wrapped into a simple chatbot using Streamlit. - Source: dev.to / 3 months ago
In this post, I'll walk you through setting up observability for Spring Boot applications on Amazon EKS - starting with the basics (logs and metrics), diving into distributed tracing, and finishing with Application Signals. Hopefully this saves you some time. - Source: dev.to / 6 months ago
Running Apache Spark on Kubernetes with AWS EMR on EKS brings big benefits โ you get the best of both worlds. AWS EMR's optimized Spark runtime and AWS EKS's container orchestration come together in one managed platform. Sure, you could run Spark on Kubernetes yourself, but it's a lot of manual work. You'd need to create a custom container image, set up networking, and handle a bunch of other configurations. But... - Source: dev.to / 8 months ago
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