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You can't optimize what you can't see. Most of the token-saving techniques covered elsewhere in this series — trimming prompts, caching, routing to smaller models — only work if you actually know where your tokens are going in the first place. Monitoring isn't an optional extra step; it's the foundation everything else is built on.
Why This Matters
Without visibility into token usage, cost problems show up in exactly one place: the invoice, weeks after the spending happened. By then, you're debugging a cost spike after the fact instead of catching it as it happens.
A useful monitoring setup answers a few specific questions:
- Which endpoints, features, or task types are consuming the most tokens?
- Is usage trending up in a way that matches growth, or is something behaving unexpectedly?
- Are cached vs. non-cached tokens split the way you'd expect, given your caching setup?
- Are certain users, requests, or prompt patterns disproportionately expensive?
A Simple Framework
- Break down usage by task or feature, not just a single aggregate number
- Set a baseline for "normal" usage once you have a few weeks of real data
- Configure alerts for usage that deviates meaningfully from that baseline
- Review cache hit/miss ratios alongside raw token counts, not in isolation
- Revisit your baseline periodically as your app grows — yesterday's spike is tomorrow's normal
Example
Before: A team discovers a 4x token cost spike only when the monthly invoice arrives. Tracing the cause takes days, because there's no historical breakdown by feature or endpoint — just one big number.
After: The same team has per-endpoint usage metrics and an alert configured for unusual deviation. The spike is caught within hours, traced to a single misconfigured feature that was resending a bloated conversation history, and fixed before it repeats for a second billing cycle.
> Tip: DigitalOcean's inference platform provides visibility into the performance, cost, and reliability of every request, with metrics mapped directly to how usage is billed — set this up before you need it, not after a surprising invoice.
Common Mistakes
- Only checking usage once a month, at invoice time
- Tracking a single aggregate number instead of breaking usage down by feature
- Not configuring any alerting, relying entirely on manual checks
- Ignoring cache metrics, missing an easy signal for whether your caching strategy is actually working
DigitalOcean's Inference features documentation covers the built-in visibility into cost, performance, and reliability per request.
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Every optimization in this series depends on measurement. Build the dashboard before you build the next optimization — it'll tell you exactly which one is worth doing first.