cost-aware-llm-pipeline

Reduce LLM API costs by routing low-complexity inputs to cheaper models and reserving expensive models for complex workloads. This approach applies model routing thresholds to batched tasks, ensuring you only pay for the computational power each specific prompt actually requires.

Track LLM API spend using immutable cost tracking that records per-call accounting and enforces budget guardrails. This mechanism provides strict auditability over your API usage, automatically preventing budget overruns by halting or flagging operations when spending limits are reached.

Prompt caching reduces token usage and latency by storing and reusing context from recurring prompts sent to LLM APIs. Instead of processing the same input tokens repeatedly, the API retrieves cached prompt data, lowering both computational latency and overall token consumption costs.

Yes, you can route tasks between LLM APIs like Claude and OpenAI by applying complexity thresholds. The pipeline evaluates the input's complexity and automatically directs the request to the most appropriate and cost-effective model, optimizing spend across different API providers.

Use narrow retry logic exclusively for transient LLM API errors like rate limits or temporary server timeouts. You should fail fast immediately on authentication failures or bad requests, as retrying these persistent issues wastes API budget and increases latency without resolving the underlying problem.