Ollama
Local LLMs, zero cloud. Ideal for offline workflows and privacy.
Strengths
- Data never leaves your machine, complete privacy, zero compliance risk
- 100% free, MIT-licensed, no API costs regardless of volume
- "Docker for LLMs" simplicity, one-command model install and run
- Over 250 open models in the Ollama library: Llama, Gemma, Qwen, DeepSeek, gpt-oss, Mistral, Phi
Limitations
- Slower than cloud APIs, 10-30s outputs common for large models on CPU
- No native GUI, must pair with external web UI or IDE extension
- 70B+ models require serious GPU VRAM or aggressive quantization tradeoffs
Best for
- Privacy-first teams running LLMs on sensitive data with no cloud exposure
- High-volume local inference where API costs would be prohibitive