Latency & Performance
Master strategies to optimize response times and deliver fast, responsive AI agents
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0 / 5 completedBreaking Down Latency
Total latency = sum of all components in the request-response pipeline. To optimize, you must measure each part separately: network latency, queue time, model inference, post-processing. The slowest component becomes your optimization target. Without measurement, you're optimizing blind.
Latency Components
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Network Latency: Time for request/response to travel over internet (20-100ms typical)
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Queue Time: Wait time if server is busy processing other requests (0-500ms+)
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Model Inference: Actual LLM processing time (200-2000ms depending on model/tokens)
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Post-Processing: Parsing, formatting, validation after model returns (10-100ms)
Interactive: Latency Breakdown Simulator
Measure each component to identify bottlenecks:
Key Metrics to Track
P50 Latency
Median response time - typical user experience
P95 Latency
95th percentile - catches outliers and slow requests
P99 Latency
Worst 1% of requests - critical for SLA compliance
Time to First Token
For streaming - perceived speed metric
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Instrument Every Step
Add timestamps before/after each major operation: network request, queue wait, model call, post-processing. Export to monitoring tools (Datadog, Prometheus). Set alerts on P95 latency thresholds. Without instrumentation, you won't know what to optimize or when performance degrades.