Vercel Custom Metrics

Use Custom Metrics for numeric application and business measurements emitted by server-side code running in a Vercel Function. The workflow is emit a numeric sample with metric() → invoke the deployed function → discover and query the metric with vc metrics or Observability.

Emit a metric

Install or upgrade @vercel/functions, then import metric from its root entry point:

pnpm add @vercel/functions

import { metric } from '@vercel/functions';

export async function POST() {
  const startedAt = performance.now();

  try {
    await createOrder();
    metric('orders.created', 1, { outcome: 'success' });
    return Response.json({ ok: true });
  } catch (error) {
    metric('orders.created', 1, { outcome: 'error' });
    throw error;
  } finally {
    metric('orders.duration_ms', performance.now() - startedAt);
  }
}

The signature is:

metric(name: string, value: number, tags?: Record<string, string>): void

• name identifies one stable measurement, such as orders.created or orders.duration_ms.
• value is the numeric sample. Emit 1 for an increment that will be summed; emit the observed value for a duration, size, or score.
• tags are optional string attributes. After ingestion, discovered tag keys appear as dimensions for filtering and grouping.
• metric() is synchronous and returns void; do not await it.
• The helper is a no-op when the runtime does not expose Custom Metrics support. Verify instrumentation through a deployed Vercel Function invocation, not local execution alone.

Model metrics for useful queries

• Prefer stable, dotted names with a unit suffix where useful: checkout.completed, checkout.duration_ms, queue.batch_size.
• Do not use the reserved vercel. prefix for application-defined names.
• Keep variable data in tags instead of metric names. Use checkout.completed with { plan: 'pro' }, not checkout.completed.pro.
• Keep tag cardinality bounded. Good tags are outcome, plan, provider, or a normalized route. Do not attach user IDs, request IDs, email addresses, raw URLs, or other unique or sensitive values.
• Emit one sample at the point where the outcome is known. For retryable or at-least-once work, decide whether attempts or successful logical operations are the intended measurement and name the metric accordingly.

Choose the query aggregation to match what was emitted:

Discover and query the metric

Run the deployed code at least once, then use the linked project and correct team scope:

vc metrics schema
vc metrics schema orders.duration_ms

vc metrics orders.created -a sum --group-by outcome --since 24h
vc metrics orders.duration_ms -a p95 --since 1h
vc metrics orders.duration_ms -a p95 --group-by outcome --since 24h --format=json

vc and vercel are equivalent. Always inspect the exact metric first with vc metrics schema <name> because the schema reports the available aggregations and discovered tag dimensions. Use -S <team> and -p <project> when the current link or scope is ambiguous; use --all only for a deliberate team-wide query.

Custom Metrics querying requires Observability Plus and availability for the selected team. If a metric is missing:

1. Confirm the function was deployed to Vercel and the instrumented path actually ran.
2. Confirm @vercel/functions exports metric; upgrade it if necessary.
3. Check vc whoami, the selected team, and the linked project.
4. Allow for ingestion delay, then rerun vc metrics schema.
5. Confirm Observability Plus and Custom Metrics are enabled for the team.

Use the right signal

• Use Custom Metrics for numeric values you want to aggregate, trend, and filter.
• Use Web Analytics custom events for user interaction and conversion events in Web Analytics.
• Use OpenTelemetry spans for traces, operation timing, and request causality.
• Use logs for detailed diagnostic context and individual records.

Do not encode detailed event payloads into metric tags. Pair a low-cardinality metric with structured logs or traces when investigation needs per-request detail.