Open vs Closed AI Models
The AI world is divided between open and closed approaches. Understanding this split helps you choose the right tools and understand industry dynamics.
Closed Models (Proprietary)
Examples: GPT-4, Claude, Gemini, Copilot
What it means:
- Weights are secret (you can't see inside the model)
- Access only through APIs
- Company controls everything
Advantages of Closed Models
- Generally more powerful — Companies invest billions in training
- Easy to use — Just sign up and call the API
- Constantly improving — Updates roll out automatically
- Safety guardrails — Built-in content moderation
Disadvantages of Closed Models
- Vendor lock-in — Your app depends on their service
- Privacy concerns — Data goes to their servers
- Unpredictable changes — They can change capabilities anytime
- Costs scale with usage — API fees add up
- No customization — Take what you're given
Open Models (Open Weights)
Examples: Llama 3, Mistral, Qwen, Phi, Gemma
What it means:
- Model weights are downloadable
- Run anywhere (your laptop, your server, the cloud)
- Modify and fine-tune as needed
Advantages of Open Models
- Full control — Run it how you want
- Privacy — Data never leaves your infrastructure
- No API costs — Pay for compute, not per token
- Customizable — Fine-tune for your specific use case
- No vendor dependency — Model can't be taken away
Disadvantages of Open Models
- Requires expertise — Setup and optimization needed
- Hardware requirements — Need GPUs to run efficiently
- Usually less capable — Catch-up to frontier models
- You handle safety — No built-in guardrails
The Reality: A Spectrum
It's not pure "open" or "closed"—there are gradations:
| Level | What You Get | Example |
|---|---|---|
| Fully Closed | API only, no details | GPT-4 |
| Partial Info | API + paper describing methods | Claude |
| Open Weights | Downloadable model | Llama 3 |
| Open Training | Weights + training code | OLMo |
| Fully Open | Everything + training data | Some research models |
Important: "Open weights" ≠ "open source"
Llama 3 is open weights but has usage restrictions. True open source has permissive licenses.
When to Use What
Choose Closed Models When:
- You need the best capabilities
- You're prototyping quickly
- You don't have ML expertise
- Privacy isn't critical
- Variable costs work for you
Choose Open Models When:
- Privacy is paramount
- You need to run offline
- You want to fine-tune
- You're cost-sensitive at scale
- You need full control
The Business Dynamics
Closed model companies want to:
- Capture market with best performance
- Build lock-in through APIs and integrations
- Monetize through usage fees
Open model players want to:
- Commoditize the model layer
- Sell compute (Meta wants you on their cloud)
- Build ecosystem dominance
- Advance research through openness
Running Open Models
Local options:
- Ollama — Easiest way to run models locally
- llama.cpp — Efficient inference on CPU
- vLLM — High-performance server deployment
Cloud options:
- Together AI — Host open models via API
- Replicate — Run any model in the cloud
- AWS/GCP/Azure — Deploy on your cloud infrastructure
The Catch-Up Game
Open models consistently trail frontier closed models by 6-18 months:
- GPT-4 released March 2023
- Llama 3 405B (comparable) released July 2024
But the gap keeps narrowing. Each generation, open models close more distance.
The Bottom Line
Closed models: Best performance, easiest to use, least control Open models: More control, more work, catching up fast
Many teams use both:
- Closed models for complex tasks needing best quality
- Open models for high-volume, cost-sensitive, or private applications
The choice isn't religious—it's practical. Pick based on your specific requirements.
Next up: Fine-Tuning Explained — Customizing AI for your needs