Batch inference at scale with cost caps and retries

Workflow: 1) Planner: Partition jobs by token estimate; enforce daily budget caps. 2) Idempotency: Job keys and checkpoints; safe resume on failure. 3) Concurrency: Token-aware rate limiter; dynamic worker pool. 4) Caching: Embeddings and generations by normalized prompt hash. 5) Retries: Exponential backoff; switch to backup model on 429/5xx. 6) Validation: Schema check with pydantic; reject malformed tool calls. 7) Logging: Trace spans per request; store inputs/outputs with redaction. 8) Metrics: Cost per record, tokens/sec, error rate; alerts on drift. 9) Post-run QA: Sample-based human review; compare against baseline. Result: Predictable spend and SLA despite vendor hiccups.