Most teams optimize the model choice but miss the bigger levers: prompt bloat, unconstrained output length, cold cache hit rates, and synchronous calls for batch workloads. Here's where the real savings live.
Six optimization levers with impact estimates
Prompt Compression
Remove redundancy from system prompts. Generic instructions like "be helpful" consume tokens with zero value. Audit and trim weekly.
Model Routing
Route simple tasks (classification, extraction) to cheap small models. Reserve frontier models for complex reasoning only.
Output Length Control
Set explicit max_tokens and instruct the model to be concise. Unconstrained output is the most common source of unnecessary token spend.
Semantic Caching
Return cached responses for semantically similar queries. High-repetition workloads see 20–40% cache hit rates after warm-up.
Async Batching
Switch synchronous LLM calls for non-real-time tasks to async batch. Providers discount batched requests significantly.
Context Pruning
Remove outdated conversation turns from multi-turn contexts. Stale context adds tokens without improving response quality.
Quick wins to implement this week
- Audit your 5 most-called prompts for verbose instructions that can be tightened
- Add
max_tokensto every API call that doesn't have one - Identify which endpoints can tolerate 500ms+ latency and switch to async batch
- Enable semantic caching for any prompt where inputs repeat frequently (FAQ, classification)
- Set up per-endpoint cost dashboards — you can't optimize what you can't see
Built-in cost controls on MoltBot
Model routing, semantic caching, prompt analytics, batching — all built-in. 14-day free trial.
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