Retention policies for LLM telemetry: balancing privacy and insight

Retention model that scales

• Shorter raw retention for request-level records.
• Longer summary retention for trend and planning workflows.
• Explicit plan messaging for upgrade and downgrade effects.

Raw vs summary data (treat them differently)

Retention is not one number. Raw request rows support root-cause analysis, but they are the most sensitive and the most expensive to store.

Aggregated summaries support forecasting and unit economics with much lower privacy risk.

• Raw: request-level events (short window).
• Summary: daily/weekly aggregates by endpointTag, tenant, promptVersion (longer window).
• Exports: include retention metadata so finance understands truncation.

Policy review checklist

1. Document what is stored and what is not stored.
2. Define data deletion request workflow and SLA.
3. Align retention windows with budget and reporting cadence.
4. Ensure exports include retention truncation metadata.

Privacy controls that preserve visibility

• Hash user and tenant identifiers (avoid PII in telemetry).
• Store only what you need: tokens, cost, tags, status, latency.
• Separate environments and dataMode so test traffic can be deleted aggressively.
• Keep an audit record of retention policy changes (effective date + owner).

A practical retention tiering pattern

• Keep raw request events short-lived for privacy and storage control.
• Keep aggregated summaries longer for budget planning and trend analysis.
• Separate environments so staging noise does not pollute production insights.
• Make retention truncation explicit in exports and dashboards.
• Review retention every quarter as traffic volume and compliance needs change.

What to send (payload example)

{
  "externalRequestId": "req_01HZXB6MQZ2WQ9D2KCF9M4V2QY",
  "provider": "provider_id",
  "model": "model_id",
  "endpointTag": "checkout.ai_summary",
  "promptVersion": "summary_v3",
  "userId": "tenant_acme_hash",
  "inputTokens": 540,
  "outputTokens": 180,
  "latencyMs": 892,
  "status": "success",
  "dataMode": "real",
  "environment": "prod"
}

Common mistakes

• Relying on monthly provider totals without request-level ownership.
• Ignoring test/demo traffic when explaining cost variance.
• No audit trail from cost spikes to the underlying deploy/change.
• Measuring spend without unit economics (cost per call / ticket / tenant).

How to verify in Opsmeter Dashboard

1. Use Overview to confirm the variance window and overall spend trend.
2. Use Top Endpoints to attribute variance to feature ownership.
3. Use Top Users to attribute variance to tenant/customer segments.
4. Use Prompt Versions to correlate spend changes with deploy events.

Related guides

• Full guide: CFO-ready AI spend reporting: exports, audits, and retention
• Retention docs
• LLM cost attribution pillar
• LLM budget alerts pillar
• No-proxy telemetry quickstart
• Quickstart (5-minute setup)
• Alternatives and tradeoffs
• Prompt deploy regressions pillar

Evaluation resources

For security and procurement reviews, use our trust summary before final tool selection.