I need LLM monitoring with alerting for latency, timeout, and token spend spikes

Here’s a practical way to set up LLM monitoring with alerting for latency, timeouts, and token spend spikes.

What to monitor

1) Latency

Track:

• p50 / p95 / p99 latency
• latency by:
  • model
  • endpoint / feature
  • tenant / customer
  • request type (chat, embedding, tool call, etc.)

Alert on:

• sustained p95 latency above threshold
• sudden regression compared with baseline
• per-model or per-tenant latency anomalies

2) Timeouts

Track:

• request timeout count
• timeout rate = timeouts / total requests
• upstream provider timeouts vs app timeouts
• retries and retry exhaustion

Alert on:

• timeout rate above threshold
• burst of timeouts in a short window
• increase in retries leading to degraded UX

3) Token spend

Track:

• input tokens
• output tokens
• total tokens
• cost estimates by model
• tokens by tenant, feature, user, and prompt version

Alert on:

• sudden token spike vs historical baseline
• daily spend approaching budget
• unusual token-per-request increase
• high-cost model usage surge

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Recommended metrics

A solid minimum set:

• llm_requests_total
• llm_request_latency_ms
• llm_request_timeouts_total
• llm_request_errors_total
• llm_input_tokens_total
• llm_output_tokens_total
• llm_total_tokens_total
• llm_estimated_cost_usd_total

Useful labels:

• model
• provider
• endpoint
• tenant
• environment
• status
• reason
  Avoid high-cardinality labels like raw user IDs unless you aggregate carefully.

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Alert examples

Latency alert

Trigger if:

• p95 latency > 2s for 5 minutes
• or latency increases > 50% over 1 hour baseline

Example:

• Warning: p95 > 1500 ms for 10 min
• Critical: p95 > 3000 ms for 5 min

Timeout alert

Trigger if:

• timeout rate > 2% for 5 minutes
• or > 10 timeouts in 1 minute

Token spend spike alert

Trigger if:

• tokens per minute > 2x trailing 24h average
• or daily estimated cost crosses 80% of budget
• or one tenant exceeds 3x normal usage

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Best practice architecture

Instrumentation

Add telemetry in your LLM wrapper or middleware:

• start timer at request start
• count tokens from prompt + completion
• record status, retries, timeout reason
• attach model/provider metadata

Storage / dashboards

Send metrics to one of:

• Prometheus + Grafana
• Datadog
• OpenTelemetry + backend
• CloudWatch / Azure Monitor / GCP Monitoring

Alerts

Route alerts to:

• Slack / Teams
• PagerDuty / Opsgenie
• email for budget warnings

Dashboards

Create panels for:

• request volume
• latency percentiles
• timeout rate
• token usage rate
• cost by model/tenant
• top outlier requests

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A good starting threshold set

If you don’t have baselines yet:

• Latency

  • warn: p95 > 1.5s for 10 min
  • critical: p95 > 3s for 5 min

• Timeouts

  • warn: timeout rate > 1%
  • critical: timeout rate > 3%

• Token spend

  • warn: 24h spend > 75% of budget
  • critical: 24h spend > 90% of budget
  • spike alert: > 2x 7-day rolling average for 15 min

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Example alerting logic

IF p95_latency_5m > threshold
  ALERT "LLM latency high"

IF timeout_rate_5m > threshold
  ALERT "LLM timeouts increasing"

IF token_usage_15m > 2 * avg_token_usage_24h
  ALERT "LLM token spend spike"

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If you want a production-ready setup

I can help you with any of these next:

1. Prometheus/Grafana metrics and alert rules
2. Datadog monitors
3. OpenTelemetry instrumentation in Python/Node/Go
4. A complete LLM observability schema
5. Budget guardrails and anomaly detection logic

If you tell me your stack, I can generate the exact config and code.