Send and verify events with Vercel AI SDK telemetry
Use Vercel AI SDK telemetry where your app knows the final result. Collect only the fields you need, then verify a test event before building charts.
- 1
Choose the outcome
Streaming LLM analytics
- 2
Define the contract
feature, provider, model, prompt_version, and finish_reason
- 3
Log the final result
Record the final generation result after the stream finishes so token usage and finish reason are complete.
- 4
Check the stored event
Exercise a known fixture, then inspect llm_request_completed for one correctly typed terminal row.
Before you start
Before you start
- AI SDK generation or streaming calls running on your server
- A stable feature and prompt-version taxonomy for comparing generations
- An allowlist for telemetry metadata that excludes prompts, responses, headers, and tool payloads
Delivery setup
Install and initialize server-side
Import telemetry-sh in server-only code and initialize it once with process.env.TELEMETRY_API_KEY. Keep ingestion credentials out of browser bundles, client-visible environment variables, source control, logs, and exception messages.
npm installation
npm install telemetry-sh- 1Create one reusable server-side client. Set its timeout and retry limit.
- 2Log an event when the operation succeeds, fails, retries, or times out.
- 3Send test events with known results and inspect the stored rows before enabling an alert.
Snippet
Start with one structured event
Add this shape where the workflow completes, fails, or retries. Then build the dashboard from real fields.
Vercel AI SDK telemetry event
await telemetry.log("llm_request_completed", {
provider: "openai",
model: "gpt-4.1-mini",
feature: "draft_reply",
prompt_version: "draft-reply-v4",
input_tokens: 924,
output_tokens: 218,
latency_ms: 1380,
finish_reason: "stop",
status: "success",
accepted: true,
});Event schema
feature, provider, model, prompt_version, and finish_reason
input_tokens, output_tokens, total_tokens, estimated_cost_usd, and latency_ms
status, retry_count, time_to_first_token_ms, accepted, and release
Check your setup
Checkpoint 1
Record the final generation result after the stream finishes so token usage and finish reason are complete.
Checkpoint 2
Use AI SDK lifecycle callbacks or a telemetry integration for consistent coverage instead of duplicating ad hoc logging around every call.
Checkpoint 3
Review the SDK's experimental telemetry behavior during upgrades and explicitly exclude prompt, response, request-header, and tool-argument attributes.
Verification
Prove the event arrived
Run this after exercising known success and failure cases. Replace the fallback table name if your final event contract differs from the snippet.
Vercel AI SDK telemetry verification query
SELECT *
FROM llm_request_completed
ORDER BY timestamp_utc DESC
LIMIT 20;Implementation references
Review the event contract, data-safety guidance, and upstream primary documentation before enabling a new production path.
Where to log
Keep the outcome event small and recoverable
This pattern provides
- Record the outcome as an event you can query with SQL.
- Stable fields for dashboards, alerts, and cross-event correlation.
- Test events for checking success, failure, retries, and timeouts.
This pattern does not provide
- An OTLP exporter, automatic collection pipeline, or replacement for detailed traces and diagnostic logs.
- Exactly-once delivery merely because the payload contains an event ID.
- Permission to collect raw provider payloads, user content, credentials, or regulated data.
Example event schemas
Event schemas for this workflow
Check what each event records, when to send it, and which field types it needs. Review the example payload and privacy checklist before using it in production.
Use these queries in Telemetry
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