This content originally appeared on DEV Community and was authored by Ajeet Singh Raina
This blog was originally posted on the Docker official website
Generative AI (GenAI) is revolutionizing software development, but creating AI-powered applications comes with significant challenges. First, the current AI landscape is fragmented — developers must piece together various libraries, frameworks, and platforms that weren’t designed to work together. Second, running large language models efficiently requires specialized hardware configurations that vary across platforms, while AI model execution remains disconnected from standard container workflows. This forces teams to maintain separate environments for their application code and AI models.
Third, without standardized methods for storing, versioning, and serving models, development teams struggle with inconsistent deployment practices. Meanwhile, relying on cloud-based AI services creates financial strain through unpredictable costs that scale with usage. Additionally, sending data to external AI services introduces privacy and security risks, especially for applications handling sensitive information.
These challenges combine to create a frustrating developer experience that hinders experimentation and slows innovation precisely when businesses need to accelerate their AI adoption. Docker Model Runner addresses these pain points by providing a streamlined solution for running AI models locally, right within your existing Docker workflow.
In this guide, we’ll build a comprehensive GenAI application that showcases how to create a fully-featured chat interface powered by Docker Model Runner, complete with advanced observability tools(Prometheus, Grafana and Jaeger) to monitor and optimize your AI models.
This content originally appeared on DEV Community and was authored by Ajeet Singh Raina