Observability

Serverless observability

Debug route handlers, scheduled runs, and async jobs in one place — logs, traces, execution history, and latency views shared across every serverless entry point.

Related guides and docs

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38msp50
91msp95
187msp99
99.4%success
example metrics

Zero-config, full visibility

1

Invoke

Run your function from the UI, CLI, API gateway, or a pipeline — every invocation is captured automatically.

2

Trace

Each invocation produces a detailed trace with timing, logs, output, and the error stack when something fails.

3

Analyze

Aggregate metrics over time, and let the emerging insight helpers point at anomalies and slow functions — treat their suggestions as hints and validate them against your own baselines.

Trace every invocation

Execution traces

Every invocation gets a full trace — steps, log lines, timing, input and output, and error details.

Live streaming

Run status streams live over SSE as your function executes — no polling, no page refreshes.

Latency percentiles

p50 / p95 / p99 latency updates after every run, so you spot regressions before your users do.

Run insights

Heuristics over your run history suggest where latency clusters — pair them with your own reviews before treating the output as authoritative.

Execution trace payload

trace.json
{
  "runId": "run_01hw3k...",
  "functionName": "ai-summarizer",
  "status": "SUCCEEDED",
  "durationMs": 312,
  "startedAt": "2025-03-23T10:41:22.100Z",
  "logs": [
    { "level": "INFO",  "message": "Fetching URL..." },
    { "level": "INFO",  "message": "Calling OpenAI API..." },
    { "level": "INFO",  "message": "Summary generated (824 words)" }
  ],
  "output": {
    "summary": "AI funding surges as...",
    "wordCount": 824
  }
}

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