llama.cpp vs the OpenAI API
The OpenAI API and llama.cpp address the same core task from opposite deployment models. The OpenAI API is a managed cloud service with no infrastructure to run; llama.cpp runs the same inference workload on your own hardware using open-weight models.
| Feature | llama.cpp | OpenAI API |
|---|
| License | MIT | Proprietary |
| Self-hosting | Yes | No |
| Cost model | Hardware only | Per-token billing |
| Data privacy | Stays on-premises | Sent to OpenAI servers |
| Model selection | Open-weight models in GGUF | OpenAI's proprietary models |
| API format | OpenAI-compatible | OpenAI |
llama.cpp is the better choice when data must not leave your infrastructure, when token volume makes per-token billing expensive, or when you want to run models locally during development before committing to production API costs. The OpenAI API remains the better fit when you need OpenAI's own proprietary models or a managed path to production without any infrastructure work.
llama.cpp vs Groq
Groq delivers ultra-fast cloud inference on custom LPU hardware, with speeds of 400-800 tokens per second on popular open models. This is significantly faster than most consumer GPU setups running llama.cpp. The difference is deployment: Groq is a managed API where prompts leave your infrastructure; llama.cpp is self-hosted with no data leaving the host.
| Feature | llama.cpp | Groq |
|---|
| License | MIT | Proprietary |
| Self-hosting | Yes | No |
| Inference speed | Hardware-dependent | 400-800 tokens/s (LPU) |
| Data privacy | On-premises | Sent to Groq servers |
| Cost | Hardware only | Per-token (free tier available) |
For latency-critical production use cases where maximum token throughput on open models matters and cloud dependency is acceptable, Groq is worth evaluating. llama.cpp is the better choice for air-gapped deployment, predictable infrastructure costs, or running models Groq does not offer.
Together AI, Fireworks AI, and the Anthropic API
Together AI and Fireworks AI are managed cloud platforms for open-weight LLM inference; the Anthropic API is a cloud service for Claude models. All three eliminate infrastructure management but send prompts to external servers and charge per token. llama.cpp is an alternative for teams who want to run open-weight models on-premises rather than through a third-party API. The managed platforms remain the better choice when you need fast time-to-production, fine-tuning capabilities, or access to proprietary model families that are not available in GGUF format.
