How to Monitor Claude API Latency Effectively

Monitoring Claude API latency means tracking how long requests take from the moment you send them until you get a usable response, broken down by time-to-first-token, total completion time, and network overhead. You do this by instrumenting your own request/response cycle with timestamps, logging per-request metadata (model, token counts, streaming vs non-streaming), and feeding that data into a dashboard or alerting system so you catch regressions before users complain.

This matters because latency on LLM APIs is not a single number. A request that takes 8 seconds might feel instant if the first token arrives in 300ms and you're streaming the rest, or it might feel broken if the user stares at a blank screen for 8 seconds waiting for a non-streaming response. Below is a practical approach to measuring, logging, and alerting on Claude API latency in production.

What to actually measure

Before writing any code, decide which latency metrics matter for your use case:

• Time to first byte (TTFB): how long until the connection responds at all.
• Time to first token (TTFT): for streaming responses, how long until the first content chunk arrives. This is the number that determines perceived responsiveness.
• Total request time: full duration from request sent to stream closed (or response received for non-streaming calls).
• Tokens per second: total output tokens divided by generation time, useful for spotting throughput degradation independent of queueing delays.
• Queue/network time vs model time: separating "the request sat somewhere" from "the model was actually generating."

If you're building a chat UI, TTFT is your north star. If you're doing batch summarization overnight, total request time and throughput matter more.

Instrumenting requests

The simplest approach is wrapping every API call with timestamps and logging the result. Here's a pattern for streaming requests:

async function callClaudeWithTiming(payload) {
  const start = performance.now();
  let firstTokenAt = null;
  let tokenCount = 0;

  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();
  const decoder = new TextDecoder();

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;
    if (firstTokenAt === null) firstTokenAt = performance.now();
    tokenCount += decoder.decode(value).split("\n").length;
  }

  const end = performance.now();

  return {
    ttft_ms: firstTokenAt - start,
    total_ms: end - start,
    approx_tokens: tokenCount,
  };
}

Log the output of every call (model, prompt length, TTFT, total time, status code) to whatever you already use for observability — structured logs, a time-series database, or a simple append-only table. The key is consistency: every request should produce the same shape of record so you can aggregate later.

Separating your latency from Anthropic's latency

A common mistake is treating all latency as "the API is slow" when a chunk of it is your own code: serialization, middleware, retry logic, or a slow downstream database call that happens before you even send the request. Log timestamps at each boundary:

1. Request received by your server
2. Request sent to Claude
3. First byte received from Claude
4. Response fully processed and sent to your client

This lets you distinguish network/API latency from your own processing overhead, which is critical when debugging — you don't want to escalate a false alarm about "Claude API is slow" when the real issue is your own middleware doing unnecessary JSON parsing.

Watching for latency patterns, not just averages

Averages hide problems. A P50 latency of 1.2 seconds can coexist with a P99 of 14 seconds, and your angriest users are living in that P99. Track percentiles (P50, P95, P99) rather than just the mean, and segment by:

• Model (different models have different generation speeds)
• Prompt length (longer context windows take longer to process)
• Time of day (shared infrastructure can have peak-hour slowdowns)
• Streaming vs non-streaming

If you see latency spikes correlated with specific prompt lengths or specific times, that's actionable — you can adjust context truncation strategies or add retry logic with backoff for peak periods.

Alerting without noise

Set alert thresholds based on your own historical baselines, not arbitrary numbers. If your P95 TTFT is normally 800ms, alert when it crosses 2-3x that for a sustained window (not a single slow request — single outliers happen and self-resolve). A reasonable starting point:

• Warning: P95 TTFT > 2x rolling 7-day baseline for 5+ minutes
• Critical: error rate > 5% over a 5-minute window, or P99 total time > 30 seconds sustained

Route these into whatever on-call tool you already use (PagerDuty, Opsgenie, or even a Slack webhook for smaller teams).

If you're routing Claude access through SubToAPI

If your Claude access runs through SubToAPI, you get the same /v1/messages interface with usage metadata attached to every response, which simplifies this kind of monitoring — you don't need to separately track token counts and cost alongside latency, since both come back in the same payload. The streaming endpoint documented at /docs/streaming returns standard SSE events you can time exactly like the example above, and the full request/response shape is in /docs/messages. Teams managing multiple application keys also use the dashboard to spot which sub_live_ key is seeing elevated latency, which is useful when latency issues are isolated to one app or integration rather than affecting everything.

For teams just getting started with structured API access, /docs/quickstart covers the basic request pattern that you can wrap with the timing instrumentation described above.

Building a minimal dashboard

You don't need a full observability stack to get value from this. A simple approach:

1. Log every request's TTFT and total time to a database table with a timestamp.
2. Run a scheduled query every few minutes computing P50/P95/P99 over trailing windows.
3. Plot it on a basic time-series chart (Grafana, or even a simple internal page).
4. Set up one alert rule for sustained P95 degradation.

This gets you 80% of the value of a full APM setup with a fraction of the complexity, and it's enough to catch most real-world latency regressions before they become support tickets.

questions

What's a normal latency for the Claude API? It varies by model and prompt length, but time-to-first-token for streaming requests is typically under a second for short prompts, with total completion time scaling with output length. Track your own baseline rather than relying on a universal number.

Should I monitor streaming and non-streaming separately? Yes. Non-streaming requests only give you total time, which can mask a fast model that's just generating a long response. Streaming gives you TTFT as a separate, more user-relevant signal.

Can I monitor latency without building custom tooling? You can start with structured logging and a scheduled aggregation query — no dedicated APM product is required. If you're using a gateway like SubToAPI, usage metadata returned with each response (/docs/messages) reduces the instrumentation you need to add yourself.