In this blog series, we will explore the importance of merging DevOps best practices with MLOps to bridge this gap, enhance an enterprise’s competitive edge, and improve decision-making through data-driven insights. Part one discussed the challenges of separate DevOps and MLOps pipelines and outlined a case for integration.

On November 26, 2024, Anthropic released the Model Context Protocol (MCP)—an open standard for data exchange between applications and data sources. MCP simplifies how Large Language Models (LLMs) interact with external tools and data, addressing the challenges developers face when integrating AI into their systems. At Raygun, we’ve been exploring agentic workflows to improve productivity and saw real potential in MCP.

Microservices are now the architecture of choice for many developers when crafting cloud-native applications. A microservices application is a collection of loosely coupled services that communicate with each other, enhancing collaboration, maintainability, scalability, and deployment. There are several options for enabling this communication between microservices. When it comes to Python, gRPC and REST are two extremely popular directions to go.

At Grafana Labs, our mission has always been to empower users with the tools they need to build their own observability solutions. Our big tent philosophy embodies this mission by allowing you to choose the tools and technologies that best suit your needs. In this post, we want to share an update to our LLM plugin that reflects this philosophy in action.