ChatGPT, 
Claude, 
Gemini, 
Grok — all four for This page is a collection of AI concept articles for Self-Managed VPS and VDS — covering the core ideas, hosting decisions, agent architecture, and common issues you'll run into when running AI apps and agents on your server.
Core Concepts
The tradeoffs between running models on your own server versus calling a hosted API.
Why an API key and a model's token count are two different things, and why the distinction matters.
What a context window is, how it limits conversation length, and why it affects cost and performance.
What parameter counts mean in practice, and how model size relates to hardware requirements.
Open-Source vs. Proprietary AI
How self-hostable open models compare with proprietary hosted models.
System Prompts vs. User Prompts
The difference between the instructions you set once and the messages you send during a conversation.
Fine-Tuning vs. Prompting vs. RAG
Three different ways to get a model to use your own information, and when to reach for each.
Understanding AI Hallucinations
Why models sometimes state incorrect information confidently, and how to reduce it.
Understanding Model Quantization
How quantized models trade some precision for a much smaller memory footprint.
A comparison framework for picking an AI app based on what you're actually trying to do.
Hosting and Resources
VPS, VDS, or Regular Hosting for AI
Which hosting tier fits your AI workload, and when you actually need dedicated resources.
How to estimate the resources an AI agent needs before you provision a server.
One-Click vs. Manual SSH Installs
The tradeoffs between one-click app installs and installing an AI app yourself over SSH.
What changes about your setup when more than one person needs to use the same AI deployment.
Agent Architecture and Security
What an agent can and can't do on your server, and how to scope its access safely.
AI Agent Tools and Function Calling
How agents call external tools and functions to take action beyond generating text.
How multiple agents coordinate on a task, and when a multi-agent setup is worth the added complexity.
Embeddings and Vector Databases
How text gets converted into searchable vectors, and where a vector database fits into an AI app.
How to isolate an agent's actions so a mistake or a bad instruction is less likely to affect the rest of your server.
Agents vs. Chatbots vs. Automation
How these three terms differ, and how to tell which one a given tool actually is.
Data Privacy and Self-Hosted AI
What self-hosting an AI app does and doesn't guarantee about where your data goes.
Securing Webhooks and API Endpoints
How to lock down the endpoints your AI automations expose to the outside world.
The core pieces — model, memory, tools, and orchestration — that make up an AI agent.
Troubleshooting
Why a model or agent can get stuck repeating itself, and how to break the loop.
AI Agent Timeouts and Disconnections
Common causes of an agent session dropping mid-task, and how to make it more resilient.
Troubleshooting Failed AI Installs
How to diagnose the most common reasons an AI app install fails partway through.