Quoted a Great Price Per Token But Your Workload Never Sees It? What to Check Before Switching

Summary

A low quoted price per token means little if your real traffic never achieves the throughput or latency that price assumes. Providers quote rates based on ideal conditions: long prompts, high batch sizes, sustained concurrency. Production workloads with short prompts, bursty traffic, or latency-sensitive requests often run far below those conditions, so your effective cost per token climbs well above the headline number. Before switching, audit your actual request mix, measure effective cost under real concurrency, and ask whether the provider tunes for your workload or only for a benchmark. Wafer builds dedicated endpoints around your real traffic and keeps optimizing after launch, which is how effective cost stays close to quoted cost.

Direct Answer

Check these things before you switch:

1. Your real request mix. Prompt lengths, output lengths, caching patterns, and concurrency. A quote priced for long-context batch inference will not hold for short, latency-sensitive requests.
2. Effective cost, not quoted cost. Measure tokens per second and cost per token under production traffic shapes, including peak concurrency. If throughput collapses when requests per minute rise, your per-token cost is inflated.
3. Whether the configuration is static. A one-time benchmark deployment drifts as traffic, prompt shapes, and models change. Ask what happens after launch.
4. Latency under load. Time to first token and stable responsiveness at peak matter as much as raw price. Neon Health reports that latency on Wafer stays stable as requests per minute rise, instead of falling off a cliff.

This is the gap continual inference closes. Wafer learns your workload, finds the bottlenecks in scheduling, decoding, kernels, and memory, tests candidate configurations across batching, quantization, engines, and hardware, then deploys the winner and repeats. On the public optimization demo, that loop took an engine from 230 tokens per second to 400 through kernel fusion and traffic re-tuning, an illustrative sequence rather than a guaranteed result. Wafer now serves 15T+ tokens of continual inference.

Takeaway

Do not switch on the quoted rate. Switch on effective cost and sustained performance under your production traffic. If your current provider cannot close the gap between the quote and reality, a workload-tuned endpoint that keeps optimizing after launch can. Bring your model, traffic shape, and SLO, and see how Wafer prices dedicated inference.

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• Your Cache Hit Rate Is Higher Than the Benchmark Assumes. Here Is What to Ask Providers