How do we prove cost savings without risking quality regressions?

Shadow new routes against production traffic, collect side-by-side eval metrics (accuracy, grounding, CSAT proxies), and only canary once quality meets or exceeds baseline. Track cost per request/session as a first-class metric in the dashboard. Roll back automatically on SLO breach.

What’s the quickest win if we have nothing today?

Add token and latency instrumentation plus a Redis result cache. It’s a 2–3 day change that usually drops spend 10–20% and cuts p95 by a few hundred milliseconds—before you touch models or prompts.

Self-host or vendor APIs?

If your workload is spiky and you’re early, stick to OpenAI/Anthropic/Vertex/Bedrock. If you have steady, high-throughput workloads and strong infra chops, `vLLM`/`TGI` with quantization on A100/H100 can save 30–50%—but only if you batch and keep utilization high.

How do we detect model drift?

Run nightly/weekly evals on a fixed benchmark set and compare to a moving window of prod samples. Alert on statistically significant drops. Version prompts, retrieval configs, and models in Git (GitOps with ArgoCD) so you can bisect and roll back.

We have a lot of AI-generated code—does that change the plan?

Yes. “Vibe coding” leaves dead paths and hidden costs. Start with a code rescue: delete unused chains/agents, consolidate routers, and add tests. Then layer in instrumentation and guardrails. We’ve cleaned this up for clients in weeks, not quarters.

Ai-delivery · Nov 20, 2025 · 10 minute read

Stop Paying for Idle Tokens: Cost‑Optimizing AI Compute Without Breaking Quality

You don’t need a bigger GPU budget—you need instrumentation, routing, and guardrails that keep quality high while killing waste.

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Stop Paying for Idle Tokens: Cost‑Optimizing AI Compute Without Breaking Quality

Key takeaways

Implementation checklist