TL;DR

Proprietary models (GPT-4, Claude) are more capable but expensive and less controllable. Open source (Llama, Mistral) offers flexibility and privacy but requires infrastructure. Choose based on your needs.

Proprietary models

Examples:

  • OpenAI (GPT-4, ChatGPT)
  • Anthropic (Claude)
  • Google (Gemini)

Pros:

  • State-of-the-art performance
  • Easy to use (API)
  • No infrastructure needed
  • Regular updates

Cons:

  • Ongoing costs (per token)
  • Data sent to vendor
  • Limited customization
  • Vendor lock-in
  • Rate limits

Open source models

Examples:

  • Meta (Llama 3)
  • Mistral AI
  • Stability AI
  • EleutherAI (GPT-Neo)

Pros:

  • No per-token cost (after setup)
  • Full control and privacy
  • Customizable (fine-tuning)
  • No rate limits
  • Can run offline

Cons:

  • Requires infrastructure
  • Maintenance overhead
  • Often less capable than latest proprietary
  • Slower updates

Cost comparison

Proprietary (API):

  • GPT-4: $0.03-0.06 per 1K tokens
  • Scales with usage
  • Predictable, no upfront cost

Open source (self-hosted):

  • GPU servers: $500-5000/month
  • One-time setup effort
  • Fixed cost regardless of usage
  • Cheaper at high volume

Break-even:

  • Low usage: Proprietary cheaper
  • High usage (millions of tokens/month): Open source cheaper

Capability comparison

Current state (2024):

  • GPT-4 > Claude 3 > Gemini > Llama 3 70B > smaller open source

Gap narrowing:

  • Open source improving rapidly
  • Fine-tuned open models competitive for specific tasks

Privacy and control

Proprietary:

  • Data sent to vendor
  • Enterprise plans offer data isolation
  • You don't control updates

Open source:

  • Complete data privacy
  • Full control over deployment
  • Freeze versions

Customization

Proprietary:

  • Limited (prompts, few-shot)
  • Fine-tuning available (expensive)

Open source:

  • Full fine-tuning control
  • Modify architecture if needed
  • Domain adaptation easier

Infrastructure requirements

Proprietary:

  • None (API call)

Open source:

  • GPUs (NVIDIA A100, H100)
  • Serving infrastructure (vLLM, TGI)
  • Monitoring and scaling

When to choose proprietary

  • Need best-in-class performance
  • Low-medium usage volume
  • Want simplicity
  • No infrastructure team
  • Rapid prototyping

When to choose open source

  • High usage volume
  • Privacy/compliance requirements
  • Need full control
  • Have ML infrastructure team
  • Domain-specific fine-tuning

Hybrid approach

Best of both:

  • Prototype with proprietary
  • Switch to open source for production
  • Use proprietary for complex tasks, open source for simple

Managed open source:

  • Hugging Face Inference Endpoints
  • Replicate
  • Together AI
  • Easier than full self-hosting

Self-hosted:

  • AWS, GCP, Azure VMs
  • Your own servers
  • Full control

What's next

  • Model Selection Guide
  • Fine-Tuning Basics
  • Cost Optimization