Operations | Monitoring | ITSM | DevOps | Cloud

Within the realms of microservices and distributed systems, gRPC has emerged as a cornerstone technology. Its adoption by tech giants like Google, Netflix, and Square underscores its capability to facilitate high-performance, scalable inter-service communication. Built as a modern take on the traditional Remote Procedure Call (RPC) paradigm, gRPC enables services, potentially written in different languages, to communicate efficiently and reliably across networks.

The Model Context Protocol (MCP) is rapidly becoming the connective tissue for agentic AI systems and IDE tooling. Whether you’re building a dev tool that integrates with LLMs or enabling a context-aware API backend, standing up an MCP server is a rite of passage. But MCP is still in its early days and there are some sharp edges. Here are four practical shortcuts to fast-track your MCP server development so you can skip the boilerplate and get to the good stuff: intelligent tooling.

Amazon Web Services, or AWS, offers a variety of cloud services ranging from AWS resources such as CDNs and data lakes to cloud computing and transformation services such as compute resources, virtual servers, and dynamic availability zones. For this reason, AWS cloud is one of the most broadly adopted cloud solutions, offering a global network of solutions at generally lower costs compared to on-premises solutions.

Google Cloud Platform, or GCP, is a cloud resources collection offered by Google for enterprise and standard users. GCP offers a wide range of cloud services, including compute, storage, networking, security, analytics, and even machine learning models. Google Cloud products are the backbone of many cloud applications. Google Cloud allows flexibility with the scalable and predictable cost management.