A practical AI stack
A useful application may combine an API service, a queue, a database and a small collection of workers. Keep these responsibilities separate enough to observe and restart independently. If a worker runs untrusted documents or generated code, place it inside a restricted sandbox.
Your data has a route
A private server does not make an external model private. Identify which documents, prompts and logs leave the machine, the provider that receives them, and its retention settings. Redact or minimise inputs where possible and obtain the necessary rights for the material you process.
Inference needs an honest resource estimate
Model size, quantisation, context length and concurrency affect memory. CPU inference may fit smaller tasks but can be slower than an accelerator. Confirm actual hardware and benchmark representative requests before promising a response time to users.
Observe costs and outcomes
Measure latency, queue depth, provider errors and cost per completed job. Keep a small evaluation set to catch regressions when you change a model or prompt. Handle rate limits explicitly instead of creating an uncontrolled retry loop.
Browse and test before paying
The developer access model separates obtaining a limited API key from paying for resources. A read-only integration should be able to inspect the catalogue and its account permissions before any billing authority is granted. Creation and cancellation need separate explicit scopes.
Choose a compatible client
MCP, REST and agent-to-agent workflows can coexist, but supported clients and protocol versions must be tested with the actual released service. No client logo or framework name here represents an endorsement or verified integration. Keep a documented fallback to ordinary authenticated HTTP requests.