Overview
Open Source Models
Full control, customizable, but requires infrastructure
- • Llama 2/3 (Meta)
- • Mistral 7B/8x7B
- • Falcon 40B/180B
- • Yi 34B
- • Qwen 72B
Commercial APIs
Managed service, pay-per-use, no infrastructure needed
- • GPT-4 (OpenAI)
- • Claude 3 (Anthropic)
- • Gemini (Google)
- • Command R (Cohere)
- • Titan (Amazon)
Cost Comparison
Total Cost of Ownership
Monthly costs for 10M tokens/day usage
| Model Type | Model | Hosting Cost | API Cost | Total Monthly |
|---|---|---|---|---|
| Open Source | Llama 3 70B | $3,000 (4x A100) | $0 | $3,000 |
| Mistral 8x7B | $1,500 (2x A100) | $0 | $1,500 | |
| Via Provider | $0 | $1,200 | $1,200 | |
| Commercial | GPT-4 Turbo | $0 | $9,000 | $9,000 |
| Claude 3 Sonnet | $0 | $5,400 | $5,400 | |
| GPT-3.5 Turbo | $0 | $1,800 | $1,800 |
Performance Comparison
| Model | Type | Quality Score | Speed | Best For |
|---|---|---|---|---|
| GPT-4 | Commercial | 95% | Medium | Complex reasoning |
| Claude 3 Opus | Commercial | 94% | Medium | Analysis, writing |
| Llama 3 70B | Open Source | 85% | Fast* | General purpose |
| Mistral 8x7B | Open Source | 82% | Very Fast* | Cost-effective |
| Yi 34B | Open Source | 80% | Fast* | Multilingual |
*Speed depends on hosting infrastructure
Deployment Options
Self-Hosted
Pros:
- ✓ Full control
- ✓ Data privacy
- ✓ Customization
- ✓ No API limits
Cons:
- ✗ High upfront cost
- ✗ Maintenance burden
- ✗ Scaling challenges
Managed Hosting
Providers:
- • Replicate
- • Together AI
- • Modal
- • Baseten
Best of both worlds
Commercial API
Pros:
- ✓ Zero maintenance
- ✓ Instant start
- ✓ Auto-scaling
- ✓ Latest models
Cons:
- ✗ Higher per-token cost
- ✗ Vendor lock-in
Decision Framework
When to Choose Open Source
- • Processing >2M tokens/day consistently
- • Strict data privacy requirements
- • Need for model customization/fine-tuning
- • Have ML engineering expertise
- • Budget for infrastructure ($3K+/month)
When to Choose Commercial APIs
- • Variable or unpredictable usage
- • Need latest cutting-edge models
- • Limited engineering resources
- • Quick time to market
- • Usage under 1M tokens/day
Hybrid Approach
Many successful companies use a hybrid strategy:
- Base load: Self-hosted open-source models for predictable traffic
- Peak handling: Commercial APIs for burst capacity
- Complex tasks: Premium models (GPT-4, Claude) for difficult queries
- Simple tasks: Lightweight open-source models
Example Hybrid Architecture
80% of requests
Mistral 7B self-hosted
Simple queries, classification
15% of requests
GPT-3.5 Turbo API
Medium complexity tasks
5% of requests
GPT-4 / Claude 3 API
Complex reasoning, critical tasks
Result: 65% cost reduction vs all-commercial approach
Conclusion
The choice between open-source and commercial LLMs depends on your specific needs. For most startups and small teams, commercial APIs provide the best balance of quality and convenience. As you scale beyond 1-2M tokens/day, consider migrating high-volume, simple tasks to open-source models while keeping commercial APIs for complex queries.
References
- [1] OpenAI. "API Pricing" (2024)
- [2] Anthropic. "Claude Documentation" (2024)
- [3] Google. "Vertex AI Pricing" (2024)