The Practical Comparison: Choosing Your Architecture
The decision framework. A side-by-side comparison to help you choose the right AI architecture for your specific problem.
The Decision Framework
After understanding each architecture, the question becomes practical: which one should you use? Here's a framework based on your data type and goal.
What type of data are you working with? This single question eliminates most options immediately.
Data Type to Architecture Mapping
| Data Type | Primary Choice | Alternative |
|---|---|---|
| Text / Language | Transformer | LSTM (lightweight) |
| Images (analysis) | CNN | Vision Transformer |
| Images (generation) | Diffusion | GAN |
| Video (generation) | GAN | Diffusion (emerging) |
| Time series | Transformer / LSTM | RNN (simple cases) |
| Graph / Network | GNN | None comparable |
| Tabular | XGBoost / Random Forest | Neural nets often worse |
Architecture Comparison Matrix
| Architecture | Best For | Training | Inference | Ecosystem |
|---|---|---|---|---|
| Transformer | Language | Expensive | Moderate | Excellent |
| CNN | Image analysis | Moderate | Fast | Excellent |
| GAN | Image/video gen | Tricky | Fast | Good |
| Diffusion | Image generation | Expensive | Slow | Growing |
| LSTM | Time series | Moderate | Fast | Mature |
| GNN | Graph data | Moderate | Moderate | Specialized |
Common Combinations
Modern AI systems often combine architectures for powerful results:
Vision + Language: CNN/ViT encodes images → Transformer generates text
Used in: Image captioning, visual Q&A
Multimodal Models: Vision encoder + Transformer + Diffusion
Used in: GPT-4V, Claude Vision, Gemini
Synthetic Data Pipeline: GAN generates data → Other models train on it
Used in: Enterprise AI, privacy-safe training
The 90% Rule
For 90% of practical AI projects, you'll choose between just three options:
Text task? Use a pre-trained transformer (GPT, Claude API, open-source LLM)
Image analysis? Use a pre-trained CNN (ResNet, EfficientNet)
Image generation? Use Stable Diffusion or DALL-E API
The other architectures matter when you have specialized problems. Start with these three.
Decision Flowchart
Step 1: What's your input data? (text, image, graph, time series)
Step 2: What's your goal? (understand, generate, predict)
Step 3: Match to architecture using the table above
Step 4: Start with pre-trained models before training custom
Step 5: Optimize only if the standard choice doesn't work
Don't overthink architecture selection. Define your problem clearly, identify your data type, and start with the standard choice. Optimize later if needed. The best architecture is the one that solves your problem—not the most sophisticated one.