Do I need one system to manage retries, caching, and spend for LLMs?

Not necessarily one system, but you usually need one place of control for it.

For LLM apps, retries, caching, and spend control are related operational concerns, so they’re often best handled by a shared gateway, middleware layer, or orchestration service rather than scattered across every app/service. That gives you:

• Consistent retry policy: avoid duplicate logic and thundering-herd behavior
• Centralized caching: reuse prompts/results across teams and workloads
• Spend governance: budgets, rate limits, model selection, and alerts in one place
• Observability: logs, traces, token usage, latency, hit rate, and cost tracking
• Safety controls: redaction, allowlists, fallback models, and fail-open/closed policies

That said, you don’t need a single monolith. A common pattern is:

1. LLM gateway/proxy
   • Handles auth, routing, retries, rate limits, spend caps, and model fallback
2. Cache layer
   • Semantic or exact-match caching, often alongside the gateway
3. App-level logic
   • Keeps user-specific decisions, prompt construction, and workflow state

When one system makes sense

• You have multiple apps/teams using LLMs
• Cost control matters
• You need auditing/compliance
• You want to swap providers/models easily

When you might not need it

• Small prototype or single app
• Low spend and simple usage
• No shared infrastructure needs yet

Practical recommendation

If you’re early, start with a thin wrapper around LLM calls that includes:

• retries with backoff
• token/cost logging
• basic caching where safe
• budget alerts and per-key limits

As usage grows, evolve that wrapper into a central LLM platform/gateway.

If you want, I can sketch a simple reference architecture for this.