Choose the right AI architecture for your use case
Self-hosted vs public LLM? RAG or agents? We break down every decision so you can build with confidence — not guesswork.
The Decision Framework
Every AI architecture decision can be broken down into four key questions. Answer these and the right stack becomes obvious.
What's your data sensitivity?
High (PHI, PII, trade secrets) → Self-hosted required
Medium (business data) → Hybrid or cloud with BAA
Low (public data) → Any option works
What's your monthly volume?
Low (<1M tokens) → Public API (pay as you go)
Medium (1M-100M tokens) → Hybrid routing
High (>100M tokens) → Self-hosted (amortize GPU)
What's your use case?
Chat / RAG → Local LLMs work great
Agents / Coding → Cloud API (quality matters)
Media Generation → Self-hosted (GPU required anyway)
What's your team size?
Solo / Small (<5) → Simpler = better (cloud or Ollama)
Medium (5-20) → Invest in hybrid setup
Large (20+) → Dedicated ML platform team
Use Cases & Recommended Stacks
Match your use case to proven architecture patterns. Each recommendation is based on real-world deployments.
AI Chat & Conversational
Build chatbots, customer support agents, or internal knowledge assistants.
Recommended Tools
- → Ollama + Open WebUI (self-hosted)
- → OpenAI API (public)
- → Lobe Chat (multi-provider)
Architecture Patterns
RAG & Document Q&A
Chat with your documents, internal wikis, knowledge bases, or research papers.
Recommended Tools
- → AnythingLLM
- → LlamaIndex
- → Haystack
Architecture Patterns
AI Agents & Automation
Build autonomous agents that can reason, use tools, and complete multi-step tasks.
Recommended Tools
- → CrewAI
- → LangGraph
- → LangChain
Architecture Patterns
AI Coding Assistants
AI pair programming, code generation, debugging, and IDE integrations.
Recommended Tools
- → Aider
- → Continue (VS Code)
- → Claude / GPT-4 (API)
Architecture Patterns
Image & Media Generation
Generate images, video, audio, and creative content with AI.
Recommended Tools
- → ComfyUI
- → Stable Diffusion WebUI Forge
- → Fooocus
Architecture Patterns
Audio & Transcription
Speech-to-text, text-to-speech, translation, and voice cloning.
Recommended Tools
- → Whisper (OpenAI)
- → VoiceAI
- → F5-TTS
Architecture Patterns
LLM Comparison: Cloud vs Self-Hosted
The fundamental trade-off: cloud APIs offer best quality and zero setup; self-hosted offers privacy, zero per-token cost, and full control.
OpenAI (GPT-4/4o)
Public APIPros
- + Best reasoning quality
- + Massive context (128K)
- + Vision + function calling
- + Fastest growing ecosystem
Cons
- - Data leaves your servers
- - API costs add up
- - No fine-tuning on latest model
- - Rate limits
Anthropic (Claude)
Public APIPros
- + Excellent reasoning
- + 200K context window
- + Constitutional AI (safer)
- + Great at coding
Cons
- - Data leaves your servers
- - No multimodal (images limited)
- - Fewer integrations than OpenAI
- - Vendor lock-in
Llama 3 (Meta)
Self-hostedPros
- + Free forever
- + Full data privacy
- + Fine-tunable
- + Hardware flexible
- + Open license
Cons
- - Needs GPU (16GB+ VRAM for 70B)
- - Lower quality than GPT-4
- - Setup complexity
- - No vision (yet)
Mistral / Mixtral
Self-hostedPros
- + Excellent quality per parameter
- + Small VRAM requirements
- + Apache 2.0 license
- + Fast inference
Cons
- - Needs GPU for best performance
- - Smaller ecosystem than Llama
- - Limited tool use
- - Less documentation
DeepSeek / Qwen
Self-hostedPros
- + Cutting-edge reasoning
- + Competitive with GPT-4
- + Open weights
- + Multi-lingual
Cons
- - Larger models need more VRAM
- - Newer (less battle-tested)
- - Fewer integrations
- - Limited ecosystem
Architecture Patterns
Four proven patterns for production AI systems. Each has different trade-offs in cost, complexity, and capability.
Pure Cloud (Public API)
Everything runs via OpenAI/Anthropic API. Fastest to build, data leaves your servers.
Speed to market, data sensitivity is low, budget available
Pure Self-Hosted
Run everything locally. Maximum privacy, zero API costs, needs GPU.
Data sovereignty required, high volume, privacy regulations (HIPAA, GDPR)
Hybrid (Cloud + Local)
Use cloud for complex reasoning, local for simple tasks. Best balance.
Cost optimization, mixed workloads, need both quality and privacy
Multi-Model Ensemble
Multiple specialized LLMs for different tasks. Most powerful, most complex.
Production systems with diverse needs, cost-sensitive at scale
Cost Comparison: Cloud vs Self-Hosted
The break-even point depends on your volume. Below ~1M tokens/month, cloud is cheaper. Above it, self-hosted wins.
| Scenario | Cloud API Cost | Self-Hosted Cost | Verdict |
|---|---|---|---|
| Individual Developer | $20-50/month | $0 (hardware already owned) | Self-hosted wins |
| Startup (5-10 users) | $200-1,000/month | $100-300/month (GPU server) | Self-hosted at scale |
| Enterprise (100+ users) | $2,000-10,000/month | $500-2,000/month (GPU cluster) | Self-hosted saves 50-80% |
| High-Privacy (HIPAA/GDPR) | $5,000+/month (enterprise agreements) | $1,000-3,000/month | Self-hosted required |
"But what about GPU costs?"
A single NVIDIA RTX 4090 ($1,600) can run 7B-13B models at 30-50 tokens/sec. For 70B models, you'll need an A100 ($10K+) or cloud GPU rental ($1-3/hr). The break-even vs OpenAI occurs at approximately 50M-100M tokens/month.
Start with Ollama
Run any LLM locally in 2 minutes. Best entry point into self-hosted AI.
View Ollama Review →Build a Chat UI
Open WebUI gives you a ChatGPT-style interface for any LLM backend.
View Open WebUI Review →Build AI Apps
Dify lets you build RAG, agents, and workflows with a visual drag-and-drop editor.
View Dify Review →Found your architecture?
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