How to Monitor Claude API Response Times

If you're asking how to monitor Claude API response times, the short answer is: capture timestamps at request start and stream-end, log them with metadata (model, token counts, endpoint), and aggregate into percentiles (p50/p95/p99) rather than averages. You need this because a single slow request tells you nothing — what matters is the distribution over time, and whether it's trending worse.

This matters more than it sounds. Claude API latency varies by model, prompt length, output length, time of day, and whether you're streaming or waiting for a full response. Without monitoring, you'll only notice problems when users complain, by which point you've already lost trust in your product's reliability. This article covers what to measure, how to instrument it yourself, and when a managed layer makes more sense than building your own observability stack.

What "response time" actually means for an LLM API

Before building anything, define your metrics precisely. For Claude (and LLM APIs generally), there are at least three distinct timings worth tracking separately:

• Time to first byte (TTFB) — how long until the connection opens and headers return. Usually network/infra latency, not model latency.
• Time to first token (TTFT) — for streaming responses, the time between sending the request and receiving the first content chunk. This is what users feel as "responsiveness."
• Total completion time — time until the full response (or stream) finishes. Dominated by output token count, since generation is roughly linear in tokens produced.

If you only track one number, track TTFT for interactive use cases (chat UIs) and total completion time for batch/background jobs. Conflating the two will make your dashboards misleading — a summarization job with a 4,000-token output will always look "slow" compared to a one-line chat reply, even if both are performing normally.

Manual instrumentation with curl and timing

For quick diagnostics, curl's built-in timing variables are enough to spot obvious problems:

curl -s -o /dev/null -w \
  "dns: %{time_namelookup}s connect: %{time_connect}s ttfb: %{time_starttransfer}s total: %{time_total}s\n" \
  https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{"model":"claude-3-5-sonnet-20241022","max_tokens":100,"messages":[{"role":"user","content":"ping"}]}'

This tells you connection overhead versus server processing time. Run it a few times across the day — if time_starttransfer is consistently high relative to time_connect, the delay is on the model side, not your network.

Instrumenting your own code

For anything beyond spot-checks, wrap every API call with timing logic and emit structured logs. In Node.js:

async function callClaudeWithMetrics(payload) {
  const start = performance.now();
  let firstTokenAt = null;

  const response = await fetch("https://api.anthropic.com/v1/messages", {
    method: "POST",
    headers: {
      "x-api-key": process.env.ANTHROPIC_API_KEY,
      "anthropic-version": "2023-06-01",
      "content-type": "application/json",
    },
    body: JSON.stringify({ ...payload, stream: true }),
  });

  const reader = response.body.getReader();
  let chunks = 0;

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;
    if (!firstTokenAt && value.length > 0) {
      firstTokenAt = performance.now();
    }
    chunks++;
  }

  const end = performance.now();

  logMetric({
    model: payload.model,
    ttft_ms: firstTokenAt ? firstTokenAt - start : null,
    total_ms: end - start,
    chunks,
    timestamp: new Date().toISOString(),
  });
}

Push logMetric output to whatever you already use — Datadog, a Postgres table, CloudWatch, or even a flat file you parse later. The important part is capturing TTFT and total duration per request, not just an average at the end of the day.

Aggregating into percentiles, not averages

Averages hide tail latency. If 95% of your requests complete in 1.2 seconds but 5% take 8 seconds, the average looks fine while a meaningful chunk of users have a bad experience. Compute p50, p95, and p99 over rolling windows (hourly or daily) and alert when p95 crosses a threshold — that's usually the earliest signal of a real degradation, well before averages move.

A simple SQL aggregation if you're logging to a table:

SELECT
  date_trunc('hour', timestamp) AS hour,
  percentile_cont(0.5) WITHIN GROUP (ORDER BY total_ms) AS p50,
  percentile_cont(0.95) WITHIN GROUP (ORDER BY total_ms) AS p95,
  percentile_cont(0.99) WITHIN GROUP (ORDER BY total_ms) AS p99
FROM api_metrics
WHERE timestamp > now() - interval '7 days'
GROUP BY 1
ORDER BY 1;

Correlate latency with request shape

Response time isn't a flat property of "the API" — it's heavily dependent on request characteristics. Track max_tokens, actual output tokens used, and model name alongside every latency measurement. A claude-3-opus call generating 2,000 tokens will naturally take longer than a claude-3-5-haiku call generating 50. Without this context, your monitoring will flag false alarms every time usage patterns shift.

When to skip building this yourself

Building reliable latency monitoring — correct timestamp capture around streaming, percentile aggregation, alerting, dashboards per team or environment — is a non-trivial amount of plumbing for something that isn't your core product. If you're already routing Claude access through an API layer, usage metadata (including timing) should come with it rather than being bolted on separately.

This is one of the reasons teams put SubToAPI in front of their Claude usage: every request made through a sub_live_... key is logged with usage metadata in the dashboard, so response time and token data is already there instead of something you have to wire up and maintain. It's not a replacement for deep observability tooling, but it removes the need to build the basic instrumentation layer from scratch. See the docs and quickstart for how requests and streaming are structured, or check pricing if you're evaluating it for a team.

Questions

What's a normal response time for the Claude API? It depends heavily on model and output length. Time to first token is typically under a second for smaller models; total completion time scales with output tokens, often 20–40 tokens/second depending on model.

Should I monitor streaming and non-streaming requests the same way? No. For streaming, track time-to-first-token as your primary UX metric. For non-streaming, total response time is the only number available, so compare it against expected output length to judge if it's actually slow.

How often should I check Claude API latency? Continuously if you're running production traffic — log every request and aggregate hourly. For low-volume or exploratory use, daily or weekly curl-based spot checks are usually enough to catch major regressions.