LLM Application Backend

Calling an LLM directly from your app works in a demo but not in production — you need cost control, safety, versioned prompts, and the ability to debug a bad answer after the fact.

Requests flow through an LLM gateway that handles auth, rate limiting, provider routing, and retries. Prompts are versioned artifacts, not inline strings. A semantic/exact cache short-circuits repeated queries. Input and output guardrails screen for injection, PII, and policy violations. Every call is traced (prompt, model, tokens, latency, cost) for observability and eval.

• Gateway indirection so you can switch providers or models without touching app code.
• Prompt versioning so you can attribute a regression to a change and roll back.
• Cache on normalized input to cut cost and latency on hot paths.
• Streaming responses for perceived latency; buffer for guardrail checks where needed.

Provider outages need fallback routing or a degraded mode. Unbounded context or retries blow up cost — cap both. Prompt injection from user or retrieved content can exfiltrate data — screen inputs and constrain tool access. No tracing means you cannot reproduce a bad answer.

Add provider fallbacks and per-tenant quotas at the gateway. Move hot prompts behind the cache. Introduce an eval pipeline that replays traced traffic against prompt changes before you ship them.