✅ You want best accuracy and capabilities ✅ You need managed infrastructure (don't want ops burden) ✅ You're building a business (need reliability guarantees) ✅ You have low-volume needs (less than 1M tokens/month) ✅ You want the latest models (cutting-edge) ✅ You need support and SLAs ✅ You can't host infrastructure ✅ You want simplicity (API, no maintenance)

Use Open-Source Models If:

✅ You need privacy (data never leaves your system) ✅ You're processing sensitive/proprietary data ✅ You have high volume needs (10M+ tokens/month) ✅ You want customization or fine-tuning ✅ You need complete control ✅ Cost is critical ✅ You have infrastructure/DevOps skills ✅ You want to avoid vendor lock-in ✅ You need local/offline capability

Real-World Scenarios

Scenario 1: Startup Building AI App

Use: Proprietary (at least initially)

Why:

You need best performance to impress users
You don't have ops team for infrastructure
You need fast iteration and latest models
Scaling infrastructure is overhead you don't need

Cost: ChatGPT API is $45K/month at 10M tokens but worth it for speed-to-market

Scenario 2: Enterprise Processing Proprietary Data

Use: Open-source (self-hosted)

Why:

Privacy is critical (can't send proprietary data to OpenAI)
You have data/ops teams to manage infrastructure
Cost matters at scale
You need customization

Cost: $500K hardware investment, then $200K/year ops = cheaper than proprietary long-term

Scenario 3: Developer Testing Multiple Models

Use: Hybrid approach (proprietary for experiments, open-source for production)

Why:

Test ideas with ChatGPT/Claude (best quality)
Optimize costs with open-source (LLaMA) when ready
Get both benefits

Cost: $100-500/month for testing, $1-2K/month for production

Scenario 4: Content Creator Needing Quick Turnaround

Use: Proprietary (ChatGPT or Claude)

Why:

Need best writing quality
No infrastructure expertise
Speed to output matters
Volume is manageable

Cost: $20/month (ChatGPT Plus) covers most creators

Scenario 5: Research Lab Analyzing Documents

Use: Hybrid or open-source

Why:

Privacy (academic data)
Large volume of processing
Cost matters
Can batch offline

Cost: Open-source self-hosted, ~$500/month on cloud

Hybrid Approach: Best of Both Worlds

The smart move in 2025? Use both.

Your optimal stack:

Proprietary for development: ChatGPT for quick iteration, Claude for analysis
Open-source for production: LLaMA for cost-effective inference
Platform that handles both: Dotlane (or similar) lets you test proprietary models, then switch to open-source when needed

Example workflow:

1. Prototype with ChatGPT (fastest iteration)
2. Test quality with Claude (better reasoning)
3. Measure costs and benchmark open-source (LLaMA, Mixtral)
4. If good enough: Deploy open-source (huge cost savings)
5. If not: Keep using proprietary (slightly higher cost, better quality)

Result: You get the best of both worlds—development speed AND production cost efficiency.

The Open-Source Landscape 2025

Best open-source models:

LLaMA 3: Best general-purpose, great quality
Grok-1: Recently open-sourced, excellent quality
Mixtral 8x7B: Best small model, great balance
Phi-3: Microsoft's compact model, surprisingly good
Deepseek-V2: New contender, strong performance

Hosting options:

Self-hosted: Ollama, LM Studio, vLLM
Managed cloud: Together AI, Replicate, Baseten
DIY cloud: AWS, GCP, Azure with your own setup

Cost comparison (10M tokens/month):

ChatGPT: $45K
Claude: $30K
Open-source (managed): $5-15K
Open-source (self-hosted): $2-3K

The Bottom Line

There's no single "best" choice. It depends on your situation:

Proprietary if you want:

Best performance
Managed infrastructure
Speed to market
Peace of mind

Open-source if you want:

Privacy and control
Cost efficiency at scale
Customization
Independence

Smart move: Use proprietary for development and testing, open-source for production at scale.

The future isn't "proprietary vs open-source"—it's knowing when to use each.