How do we know if routing to smaller models is safe?

Create a golden dataset that mirrors your top tasks and risks, then A/B routes against it. Set thresholds (e.g., task success within 2% of baseline, safety blocks <1%). Gate rollouts in CI/CD; start with 5–10% traffic canaries and watch guardrail metrics.

Is vLLM actually cheaper than managed APIs?

At steady, predictable volume with batch-friendly workloads, vLLM’s throughput and KV cache can be 2–5x better per GPU than naive hosting, often beating API costs. If your load is spiky and compliance allows, managed APIs with strict budgets might win overall due to elasticity and lower ops overhead.

Where do teams overspend first?

No caching, no batching, and sending everything to a premium model. Also: sloppy RAG (duplicate chunks), missing timeouts, and retries without jitter. These are table stakes fixes before you touch quantization or distillation.

How do we detect model drift in production?

Track distribution shifts (embedding stats, answer length, topic mix), quality on rolling golden sets, and rising safety block rates. Alert on deltas beyond thresholds and trigger re-evals. Tools like WhyLabs/Arize help; you can also roll your own with Prometheus + offline jobs.

What if our legal team blocks spot instances?

Keep on-demand for baseline capacity and restrict spot to overflow. Use priority classes and preemption-safe draining so you don’t violate SLOs. If spot is a hard no, squeeze cost out of routing, caching, and quantization—those typically deliver bigger wins anyway.

Ai-delivery · Nov 29, 2025 · 10 minute read

Stop Burning GPUs: Cost Controls for AI Inference That Don’t Tank Quality

Q: Is vLLM actually cheaper than managed APIs?

At steady, predictable volume with batch-friendly workloads, vLLM’s throughput and KV cache can be 2–5x better per GPU than naive hosting, often beating API costs. If your load is spiky and compliance allows, managed APIs with strict budgets might win overall due to elasticity and lower ops overhead.

Q: Where do teams overspend first?

No caching, no batching, and sending everything to a premium model. Also: sloppy RAG (duplicate chunks), missing timeouts, and retries without jitter. These are table stakes fixes before you touch quantization or distillation.

Q: How do we detect model drift in production?

Track distribution shifts (embedding stats, answer length, topic mix), quality on rolling golden sets, and rising safety block rates. Alert on deltas beyond thresholds and trigger re-evals. Tools like WhyLabs/Arize help; you can also roll your own with Prometheus + offline jobs.

Q: What if our legal team blocks spot instances?

Keep on-demand for baseline capacity and restrict spot to overflow. Use priority classes and preemption-safe draining so you don’t violate SLOs. If spot is a hard no, squeeze cost out of routing, caching, and quantization—those typically deliver bigger wins anyway.

I’ve watched teams 10x their AI bills overnight without moving a single KPI. Here’s how to design cost optimization into your AI stack—without shipping garbage.

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Stop Burning GPUs: Cost Controls for AI Inference That Don’t Tank Quality

Key takeaways

Implementation checklist