cost-aware-llm-pipeline

Reduce LLM API costs by routing tasks to cheaper models, tracking usage with an immutable ledger, and caching system prompts to prevent unnecessary token consumption on repeated runs.

Model routing for token efficiency automatically selects the most cost-effective LLM based on task complexity, ensuring you only pay for higher-performance models when explicitly required by the workload.

Prompt caching prevents wasted token usage by storing repeated system prompts, bypassing the need to reprocess identical prompt text on subsequent runs and directly lowering overall token spend.

The best way to handle LLM retries without wasting budget is implementing a narrowed retry policy that targets only transient errors with exponential backoff, preventing repeated spend on permanent failures.

You can track LLM spend without mutating existing state by maintaining an immutable cost ledger, which records usage data entries as append-only logs to ensure accurate budget tracking over time.

This cost-control pipeline works with any LLM-based application that must control spend and support model switching, as it implements a provider-agnostic cost-tracking data model.