cost-aware-llm-pipeline

Reduce LLM API costs by routing tasks to cheaper models based on complexity and applying prompt caching to avoid resending long system prompts. This minimizes token usage while preserving output quality across batch processing workloads.

Model routing for LLM cost optimization assigns tasks to cheaper models when complexity is low and reserves expensive models for harder tasks. It tracks per-call spend with immutable records to minimize costs while preserving quality.

Track LLM spend across multiple API calls using deterministic cost tracking with immutable per-call records. A budget guard evaluates current spend against limits without mutating state, ensuring historical cost data remains accurate and auditable.

Prompt caching works with Claude, GPT, and similar APIs to reduce token usage and latency. It avoids resending long system prompts by caching them, which lowers overall LLM spend during batch processing and multi-model architectures.

Handle transient LLM API errors using narrow retry logic that retries only on transient failures and fails fast on authentication or bad requests. This prevents wasting budget on unrecoverable errors during cost-constrained workflows.

You can use this pipeline for budget-constrained workflows with strict spending limits. It implements a budget guard that never mutates state, routing tasks to appropriate models and tracking costs deterministically to ensure spending limits are respected.