Tracking AI Usage Metrics in a SaaS Backend

AI usage can grow quietly inside a SaaS product.
One feature adds summarization. Another adds support replies. A third adds classification. Each model call may look small, but the total pattern can become difficult to understand if the backend does not track usage consistently.
The goal is not to overbuild analytics on day one. The goal is to create a clean usage event that every AI workflow can emit.
Useful fields include workflow name, feature owner, model profile, latency, estimated tokens, request status, and account or plan segment.
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Disclosure: this article includes an external referral link for readers who want to explore the platform.
Example:
type AIUsageEvent = {
workflow: "ticket_summary" | "sales_email" | "doc_search";
feature: string;
modelProfile: string;
latencyMs: number;
estimatedInputTokens: number;
estimatedOutputTokens: number;
status: "success" | "error";
};

async function runSummary(input: string) {
const startedAt = Date.now();

try {
const result = await aiClient.generate({
model: "balanced-summary-profile",
input,
});

await usageLog.write({
  workflow: "ticket_summary",
  feature: "support",
  modelProfile: "balanced-summary-profile",
  latencyMs: Date.now() - startedAt,
  estimatedInputTokens: estimateTokens(input),
  estimatedOutputTokens: estimateTokens(result.text),
  status: "success",
});

return result;

} catch (error) {
await usageLog.write({
workflow: "ticket_summary",
feature: "support",
modelProfile: "balanced-summary-profile",
latencyMs: Date.now() - startedAt,
estimatedInputTokens: estimateTokens(input),
estimatedOutputTokens: 0,
status: "error",
});

throw error;

}
}
This pattern gives engineering and product teams a shared view.
Instead of asking only “Why did AI spend increase?”, teams can ask which workflow changed, which feature drove usage, and whether the growth matches product value.