The importance of artificial intelligence in driving business success has never been clearer. Yet, for too long advanced AI capabilities have been the preserve of tech giants and well-funded enterprises. This concentration of resources has created inequality that threatens to leave many businesses behind. But the importance of democratizing AI access extends far beyond individual company success - it's about fostering a vibrant, competitive ecosystem where innovations can emerge from unexpected places.

The future is AI. That’s a fact, and all the major cloud corporations are taking notice and investing in generative AI offerings to serve their customers better. Microsoft Azure has invested in OpenAI‘s ChatGPT, Google has Vertex AI, and Amazon has created Bedrock. But what exactly is AWS Bedrock? And, most importantly, how much will it cost? Will this generative AI be an easy investment, or will you have to break the budget to squeeze it in?

One of the most critical gaps in traditional Large Language Models (LLMs) is that they rely on static knowledge already contained within them. Basically, they might be very good at understanding and responding to prompts, but they often fall short in providing current or highly specific information.

Large Language Models, or LLMs, have become a near-ubiquitous technology in recent years. Promising the ability to generate human-like content with simple and direct prompts, LLMs have been integrated across a diverse array of systems, purposes, and functions, including content generation, image identification and curation, and even heuristics-based performance testing for APIs and other software components.

Today, every enterprise is digital. Regardless of industry, every business must incorporate digital technologies and strategies into its operations to remain competitive. Maintaining reliable IT infrastructures and digital services while minimizing downtime due to unplanned outages is critical to business success.

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.

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.