Scenario 01
If you are prototyping, token volume is low and you need the strongest reasoning…
Use commercial APIs from OpenAI, Anthropic or Google.
Comparison: Commercial APIs (OpenAI, Anthropic) vs. Self-hosted open-weight models (vLLM)
OpenAI APIs vs. self-hosted open-source LLMs
Compare total cost of ownership, data privacy and latency between commercial LLM APIs and open-weight models served on your own GPUs.
Decision framework
Which one fits your situation
Scenario 02
If you handle sensitive data that must stay inside your own cloud network…
Self-host open-weight models (for example Llama, Qwen or DeepSeek) with vLLM on dedicated GPUs.
Scenario 03
If you process very high, steady token volumes for classification or extraction…
Model dedicated GPU cost against API pricing — at steady high volume, self-hosting is often cheaper.
Trade-offs
Side by side
How the two options compare on the dimensions that usually decide this choice.
| Dimension | Commercial APIs (OpenAI, Anthropic) | Self-hosted open-weight models (vLLM) | Verdict |
|---|---|---|---|
| Data privacy | Processed by a third party under its data-use terms (zero-retention options exist) | Prompts and outputs stay inside your network | Self-hosting gives the most control over data |
| Cost at high volume | Scales linearly with tokens | Mostly fixed GPU cost, so cost per token falls as utilization rises | Self-hosting wins at steady, high utilization |
| Frontier reasoning | The leading frontier models | Capable open-weight models that trail the frontier on the hardest tasks | Commercial APIs lead on the hardest reasoning tasks |
Questions
Frequently asked questions
At 4-bit quantization the weights alone need about 35–40 GB, so a single 80 GB GPU (A100 or H100) can serve it with room for the KV cache. Higher precision or long contexts need more memory or several GPUs.
On narrow tasks such as structured extraction, classification and domain Q&A, a well-chosen or fine-tuned open-weight model often comes close. Measure it on an evaluation set built from your own data before switching.
Talk the decision through with an engineer
Share your constraints — team, traffic, budget, compliance. We'll reply within one business day, and the call is about your decision, not our preferred stack.