The Model Context Protocol (MCP) is rapidly becoming the standard for connecting AI agents to enterprise systems, with 62% of organizations already experimenting with AI agents. However, hosting MCP servers presents significant infrastructure challenges. This guide explores the critical requirements for enterprise-grade MCP hosting—scalability, security, observability, and cost-efficiency—and introduces Metorial as the leading serverless platform designed to solve these challenges, offering over 600 pre-built MCP servers and a robust, scalable, and secure environment for your AI applications.

Unmanaged Model Context Protocol (MCP) server deployment is creating a massive, silent security crisis in enterprises. According to recent research, over 15% of employees are running MCP servers locally, with 86% granting them full privileges and storing credentials in plaintext. This guide provides a comprehensive framework for deploying MCP servers securely at scale, covering everything from identity and access management to monitoring and governance. We’ll explore the risks of unsecured deployments, outline production best practices, and show how Metorial’s serverless MCP runtime provides a secure, scalable, and developer-friendly solution out of the box.

Moving AI agents from a promising pilot to a production-grade system is a monumental challenge, with a staggering 95% of projects failing to deliver value. The secret to success lies not in the AI models themselves, but in a robust architecture focused on scalability, observability, and security. This article explores the core principles for building and operating reliable AI agents, highlighting the critical role of the Model Context Protocol (MCP) and how platforms like Metorialprovide the serverless infrastructure needed to bridge the gap from experimentation to enterprise-scale deployment.

The Model Context Protocol (MCP) is an open standard that’s revolutionizing how AI agents connect to external tools and data. While building a basic MCP server is straightforward, deploying, scaling, and securing it for production is a significant challenge. Metorial’s serverless MCP runtime simplifies this entire process, allowing developers to deploy robust, scalable MCP servers in just a few clicks, so they can focus on building innovative AI applications instead of managing infrastructure.

As we move further into 2026, the landscape of artificial intelligence is evolving at an unprecedented pace. AI is no longer a futuristic concept but a practical tool that businesses are integrating into their core operations. According to a recent report from the OECD, over 20% of firms were already using AI in 2025, a number that has more than doubled in just two years. This rapid adoption highlights the growing need for robust and scalable infrastructure to support AI applications. At the heart of this infrastructure is the Model Context Protocol (MCP), a critical component that enables AI agents to interact with external systems and data sources. This blog post will explore the best practices for building and managing MCP servers in 2026, ensuring your AI integrations are secure, reliable, and ready for the future.

Public APIs are infrastructure, not art projects. The best APIs are predictably boring: they use standard REST conventions, maintain consistency across all endpoints, and let developers build a mental model after using just one endpoint. When your API tries to be clever with novel patterns or inconsistent structures, integration goes from hours to days. Save your innovation for your product and let your API fade into the background where it belongs.

We’re in the jQuery age of AI agents, where every integration feels like duct tape, and standards are still a dream. Just like jQuery once smoothed over the chaos of incompatible browsers, MCP is doing the same for fragmented AI tools, making it finally possible to ship agents that actually work. It’s not perfect, but it’s the layer we need right now.

It’s October 2025 and it’s my first time vibe coding. I have mixed feelings.

Your agent can't check Slack, pull data from your Postgres database, read files from Google Drive, or update tickets in Linear. Every time you want to add a new integration, you're staring down weeks of custom API work, authentication headaches, and the inevitable "wait, their API changed again?" moments.