Latency Tracking
Time-to-first-token, total duration, per call. Because 'it feels slow' is not a metric.
Questions this answers
- How to measure LLM API latency in terminal?
- Time to first token tracking for AI coding agents
- Why is my AI coding agent slow?
- LLM API response time monitoring tool
How it works
Latency Tracking records two key timestamps for every intercepted API call: time-to-first-token (TTFT), which measures how long it takes for the first byte of the response to arrive, and total duration, which captures the full round-trip time from request sent to response complete. For streaming responses, TTFT reflects when the first streamed chunk arrives.
Both metrics are stored per call and associated with the originating tab, session, and run. This lets you identify slow calls, compare provider performance across sessions, and spot patterns like latency spikes during peak hours or degradation on large context windows.
Why it matters
AI agent responsiveness depends on API latency, and latency varies wildly by provider, model, time of day, and prompt size. Without measurements, you are stuck with subjective impressions. Chau7 records time-to-first-token and total request duration for every API call, so you can make data-driven decisions about which model to use and when.
Frequently asked questions
What is time-to-first-token and why does it matter?
Time-to-first-token (TTFT) measures the delay between sending a request and receiving the first piece of the response. It directly determines how responsive an AI agent feels. A low TTFT means the agent starts producing output quickly even if the full response takes longer.
Can I compare latency across different providers?
Yes. Since latency is tracked per call with provider and model metadata, you can filter and compare performance across providers, models, and time periods through the MCP server or Analytics Dashboard.