Llama 4: open weights, private by default

Why self-hosted matters

• Data locality — prompts stay in our EU or US clusters; no third-party API terms to review.
• No per-token API markup — open weights mean flat credit costs, even at batch scale.
• Customization — LoRA fine-tunes on your style, your tone, your domain vocabulary, kept private to your workspace.
• Zero training risk — nobody's improving their model on your documents, because there's no third party.

Honest limitations

Llama 4 trails GPT-5 and Claude on the hardest reasoning and its instruction-following is less reliable in long agentic loops. The pattern that works: use Llama for volume and privacy, escalate the hard 10% to a frontier model. Comparison mode tells you exactly where that line is for your tasks.

Why not just self-host Llama yourself?

You can — and then you're the one managing GPUs, quantization and uptime. Sandbox installs on Windows in minutes and gives you Llama 4 on our private clusters plus 20 hosted models, without buying a single GPU.

Keep reading

• DeepSeek guide
• Mistral guide