llama-swap vs Managed Cloud Inference APIs
Managed cloud inference services like Together AI, Fireworks AI, and Replicate expose OpenAI-compatible endpoints from their own GPU infrastructure. Enterprise options like Amazon Bedrock and Azure OpenAI do the same at larger scale with compliance certifications. llama-swap does neither: it runs on your own hardware and manages the inference servers you configure there.
| Feature | llama-swap | Managed Cloud APIs |
|---|
| License | MIT | Proprietary |
| Self-hosting | Required | Not available |
| Data leaves your network | No | Yes |
| Pricing | Free (hardware cost) | Per-token billing |
| Model selection | Any locally-runnable model | Provider catalog only |
| Uptime guarantee | None (self-managed) | Provider SLA |
| Offline operation | Yes | No |
llama-swap is the better choice when inference data cannot leave your network (compliance requirements, confidentiality agreements, or internal policy), when per-token costs at volume make cloud APIs expensive, or when you need to run models not available in any cloud provider's catalog. A research team running fine-tuned checkpoint variants or an organization that cannot send prompts to external servers will find llama-swap handles both use cases with no licensing restrictions.
Managed cloud APIs are the better choice when your team lacks GPU-equipped infrastructure, needs zero-maintenance uptime, or depends on frontier models that cannot run locally. Horizontal scaling, geographic distribution, and guaranteed latency SLAs are outside what llama-swap provides: it is a single-machine proxy with no built-in clustering or failover.
