RUBIKSCORPAI in SaaS
Why your AI feature is slow and the API bill keeps growing
Cost and latency creep up when every request carries too much context, calls the largest model, or repeats work already done. Features that felt cheap in a demo behave very differently with real traffic and long conversations.
Measure where time and tokens go
Measure the complete user journey rather than the model call alone. Retrieval, tool calls, network retries and post-processing can all contribute to a slow response. Compare simple and complex requests separately. Averages often hide the long waits that make an otherwise useful feature frustrating. Record the model, input size, output size and number of calls for each stage without logging unnecessary personal data.
What to check
- Compare typical latency with the slower end of the distribution and identify the stage responsible for each delay.
- Count repeated retrievals, duplicate requests, retries and long prompts that carry irrelevant history.
- Separate background work from work the user must wait for. Check whether streaming improves the perceived wait.
What to change
Remove irrelevant context before trying a different model. Set deliberate limits on retries and generated output. Cache stable public information where appropriate, with expiry and access boundaries. Test a smaller model only against a quality benchmark for that particular task. Keep a larger model for cases where its additional capability is actually needed.
How to evaluate the result
Compare cost per completed task, latency and task success together. A cheaper request that causes three retries or sends more cases to support may cost more overall. Include tool and retrieval costs when they are material.
What the result does not establish
A speed improvement must preserve permission checks and correctness. Never share cached private answers between customers, and do not remove human review from a high-impact step merely to shorten response time.
Further reading
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