Cloud LLM APIs
Most AI agents default to cloud LLM APIs because setup is straightforward: add an API key and start calling. Providers like OpenAI and Anthropic offer capable models through managed APIs with no infrastructure to run. Magnitude takes the opposite approach: local inference, on your hardware, with models selected and tuned for your machine. It is an open source alternative to paid API services for developers who want zero token costs and full data privacy.
| Magnitude | Cloud LLM APIs |
|---|
| License | Apache 2.0 | Proprietary (API access) |
| Inference cost | Free (hardware you already own) | Per-token billing |
| Data privacy | Fully local, no cloud relay | Prompts sent to third-party servers |
| Model quality ceiling | Bounded by local hardware | Frontier models available |
| Rate limits | None | Tier-dependent |
| Works offline | Yes (after model download) | No |
| Setup effort | CLI install plus hardware profile | API key only |
| Agent harness support | Pi, OpenCode, Hermes, Cline, Claude Code, and more | Any API-compatible agent |
Magnitude is the better choice when cost and privacy are the primary constraints. Agentic tasks make many LLM calls per session; billing those through a cloud API accumulates fast across a workday, while local inference costs only the hardware you already own. For teams handling proprietary code or compliance-sensitive data who cannot send prompts to a third-party server, Magnitude is the practical path to agent-assisted development without cloud exposure.
When Cloud APIs Fit Better
Cloud APIs remain the stronger choice when you need frontier model quality that local hardware cannot deliver, when you need managed uptime without infrastructure to operate, or when your hardware is too constrained to load a useful model. On machines with limited RAM, the quality gap between local and frontier models is substantial enough that cloud APIs are still worth the per-token cost for complex reasoning tasks.
