| Category | LLM APIs & Inference | MLOps & Monitoring |
| Pricing | Free tier available, pay-per-token for production | Free + Teams $50/mo |
| GitHub Stars | — | ✓ More stars |
| Platforms | Web | Linux, macOS, Windows, Web |
| Key Features | - ✓ LPU hardware — custom chips for inference, not repurposed GPUs
- ✓ GPT OSS 120B at 500 tok/s ($0.15/M input)
- ✓ GPT OSS 20B at 1000 tok/s ($0.075/M input)
- ✓ Llama 4 Scout 17B at 750 tok/s with 131K context + vision
- ✓ Qwen3-32B at 400 tok/s with 131K context
- ✓ Compound AI systems with web search + code execution
- ✓ Whisper transcription ($0.04-0.11/hour)
- ✓ OpenAI-compatible API — drop-in replacement
- ✓ Free developer tier: 250-300K TPM, 1K RPM
| - ✓ Experiment tracking
- ✓ Model registry
- ✓ Sweeps
- ✓ Reports
- ✓ Artifacts
|
| Pros | - + Fastest inference available (500-1000 tok/s)
- + Free tier with generous limits (250K+ tokens/min)
- + OpenAI-compatible API — swap one line of code
- + Latest open-source models (GPT OSS, Llama 4, Qwen3)
- + Compound AI for agentic workflows (search + code exec)
| - + Best-in-class experiment tracking
- + Beautiful visualization
- + Team collaboration features
- + Model registry
- + Free for individuals
|
| Cons | - − Cloud-only — cannot self-host LPU hardware
- − Rate limits on free tier (1K RPM)
- − Smaller model catalog than running locally via Ollama
| - − Can be overwhelming for beginners
- − Teams pricing adds up
- − Some features locked to enterprise
- − Heavy client library
|
| Tags | inferencefastfreehardware | mlopstrackingexperimentsmonitoring |