Private AI DevOps Engineer with DeepSeek + AIOps

Case Study
DevOpsAIOpsDeepSeekSelfHostedAIGrafanaPrometheusOllamaAgenticAI

Complete guide to running a private, free, locally-hosted AI coding assistant with DeepSeek Coder, Ollama, multiple web UIs, Prometheus + Grafana monitoring, and AIOps bridge for predictive infrastructure monitoring. Zero API costs, full privacy.

The Problem: AI Assistants Are Great, But…

You've used GitHub Copilot. You've chatted with ChatGPT. They're powerful — until you hit the paywall, the rate limits, or the nagging feeling that your proprietary code is being sent to some server you don't control.

What if you could run a private, free, locally-hosted AI coding assistant that also watches your server metrics and predicts failures before they happen?

“No API costs. Code never leaves your server. It predicts outages. And now, it codes for you autonomously.”

What's Inside This Setup?

The repository documents a complete, production-ready environment built around DeepSeek Coder models running via Ollama, wrapped in multiple web UIs, and monitored with Prometheus + Grafana for AI-driven operations (AIOps).

DeepSeek Coder via Ollama

Local LLM inference with no API keys, no usage limits, full privacy

Multiple Web UIs

Chatbot Ollama, NextChat, and Big-AGI for different workflows

Prometheus + Grafana

Real-time monitoring of GPU usage, model performance, and system health

OpenCode Agentic Workflows

Autonomous AI agents that can code, test, and deploy independently

DeepSeek AI DevOps architecture diagram
Fig 1: Complete system architecture — Ollama, Web UIs, Monitoring, and Agentic AI

Architecture Overview

The system is composed of several interconnected layers:

Inference Layer

Ollama serves DeepSeek Coder models locally. Supports multiple model sizes (1.3B, 6.7B, 33B) depending on hardware. Handles quantization, memory management, and GPU acceleration.

Interface Layer

Multiple web UIs connect to the Ollama API — Chatbot Ollama for quick interactions, NextChat for a polished chat experience, and Big-AGI for advanced multi-model workflows.

Monitoring Layer

Prometheus scrapes Ollama and system metrics. Grafana dashboards visualize GPU utilization, inference latency, memory usage, and request throughput in real time.

Agentic Layer

OpenCode integrates with the local LLM to enable autonomous coding agents that can read files, write code, run tests, and iterate on solutions without human intervention.

Monitoring architecture with Prometheus and Grafana
Fig 2: AIOps monitoring stack — GPU metrics, inference latency, and predictive alerts

Installation & Configuration

Prerequisites

  • •Linux server with GPU support (NVIDIA recommended)
  • •Docker and Docker Compose
  • •Minimum 16GB RAM for 6.7B model, 32GB+ for 33B
  • •10-50GB disk space depending on model sizes

Quick Start

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull DeepSeek Coder
ollama pull deepseek-coder:6.7b

# Run with monitoring
docker compose up -d

# Access web UIs
# Chatbot:  http://localhost:3001
# NextChat: http://localhost:3002
# Grafana:  http://localhost:3000
End-to-end local AI and AIOps architecture
Fig 3: End-to-end stack — from user interfaces through inference to monitoring and alerting

Real DevOps Tasks

The setup has been tested against real-world DevOps scenarios:

Log Analysis

Parse and summarize application logs to identify error patterns and root causes

Scaffolding

Generate boilerplate code for new services, Dockerfiles, and CI/CD pipelines

Test Generation

Automatically create unit and integration tests for existing codebases

Bug Fixing

Identify and fix bugs with contextual understanding of the full codebase

Model Selection Guide

Choosing the right model depends on your hardware and use case:

ModelRAMSpeedBest For
1.3B4GBFastQuick completions, low-resource
6.7B16GBBalancedGeneral coding, daily use
33B32GB+SlowerComplex reasoning, large codebases

Skills & Technologies

DeepSeekOllamaDockerPrometheusGrafanaLinuxPythonAI/MLAIOpsSelf-Hosting

Troubleshooting

  • •GPU not detected: Ensure NVIDIA drivers and CUDA toolkit are installed correctly
  • •Out of memory: Reduce model quantization level or use a smaller model variant
  • •Slow inference: Check GPU utilization in Grafana; ensure no other processes are competing for VRAM
  • •Web UI connection issues: Verify Ollama is running on the expected port (default: 11434)
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