Define the workload envelope
Group requests by user workflow, such as short questions, long-context inputs, tool-assisted work, and extended generation. For each group, record arrival patterns, concurrent activity, input and output size, cancellations, and retries. An average request can hide the expensive part of the workload, so define separate scenarios for ordinary traffic, bursts, and recovery after a burst. Set acceptable latency and error criteria from your application’s needs before running a load exercise. For a hypothetical case, if a burst contains more long inputs than usual, treat that mix as a variable to test, not as a fact about future traffic. A first concurrency estimate can be based on arrival rate multiplied by end-to-end service time; validate it with controlled replay or a workload generator that reflects your own request mix.
Trace work across the request
Give each application request an internal correlation identifier and record timestamps at meaningful boundaries: application entry, queue admission, dependency calls, first visible output, and completion. Break total latency into queueing, connection, generation, and application handling only where your instrumentation can distinguish those phases. When using NexoToken for model API access, measure what your application actually observes and do not assume undocumented response fields or platform interfaces. Keep a request ledger with the metadata needed to interpret your own traces, while excluding prompt and response content unless a documented debugging need and privacy review justify it. You can compare your ledger with platform usage history, but first confirm its granularity and time coverage in current main platform documentation. If entries cannot be matched request by request, compare aggregates only and record that the source of any discrepancy is unresolved.
Measure streams and classify failures
For streaming workflows, record request start, time to first content, intervals between later content, completion, and client disconnects as distinct events. If an application proxy buffers output, instrument both sides of that boundary; otherwise the apparent first-content delay may reflect buffering rather than model work. Classify failures using evidence your application can see, such as timeouts, connection failures, application cancellations, parsing failures, and returned error categories. Preserve whether a retry occurred so retry amplification is visible in traces. In a hypothetical run, if first-content delay rises while later intervals remain steady, inspect queueing, connection setup, and buffering; if both worsen, test whether shared processing pressure is involved. If error rates rise only for a particular request group, isolate that group before changing the capacity assumption for all traffic. Keep logs privacy-safe: redact credentials and identifiers, avoid storing raw prompts or outputs by default, and apply access and retention rules.
Turn observations into a capacity plan
Run controlled replay with representative request groups and increase load in deliberate steps, recording the environment and actual outcomes each time. Watch concurrent work, queue depth, phase latency, error classes, cancellations, retries, and the application resources that serve the workload. If the queue keeps growing while completed work does not keep pace, check application concurrency limits, dependency waits, and retry behavior before concluding that more processing capacity is needed. If a failure condition appears only under a specific mix, repeat that scenario and state the boundary clearly; do not generalize it to every workload. Document the target criteria, observation window, failure signals, and rollback action alongside the capacity estimate. Re-run the checks when traffic shape, instrumentation, or application behavior changes, and consult current main platform documentation for platform-specific limits. Treat the resulting plan as conditional on the measured environment, not as a guarantee of future performance.
Sources & further reading
NexoToken platform and community
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