Monitoring AI Workloads: LLM APIs, Inference Costs, and Timeout Handling
LLMs changed what a 'slow request' means. A database query should return in 10ms. An LLM inference can take 45 seconds legitimately. This breaks every threshold and timeout assumption your existing monitoring was built on.
LLM API calls can take 30 seconds and cost $0.10 each. When they fail, they fail silently in ways traditional monitoring was never designed to catch.
The monitoring challenges specific to LLMs
Latency is variable by design — a short prompt returns in 1s; a long context returns in 60s. P99 latency means something different than for APIs.
Cost is a monitoring dimension — a single misbehaving function can call GPT-4 in a loop and generate a $500 bill before you notice.
Failures are often non-exceptions — the LLM returns a 200 with a malformed JSON that crashes downstream processing.
Rate limits are per-minute, not per-second — a burst of 20 requests can exhaust your RPM quota while each individual request succeeds.
Heartbeat monitoring for LLM-dependent workflows
If your product has LLM-powered scheduled jobs (nightly report generation, daily content moderation), use heartbeat monitoring:
async def run_nightly_analysis():
try:
result = await llm_client.analyze(data)
await save_results(result)
httpx.get(f'https://alertsdock.com/ping/{MONITOR_UUID}')
except Exception as e:
httpx.post(f'https://alertsdock.com/ping/{MONITOR_UUID}/fail',
json={'error': str(e)})
Monitoring LLM API availability
LLM provider APIs have their own reliability profiles. OpenAI, Anthropic, and Google have had outages that lasted hours.
Set up an AlertsDock monitor on your LLM provider's API health endpoint, and route it to a separate alert channel so you immediately know when the problem is provider-side.
Cost anomaly detection
Monitor your LLM API spend daily: - Alert when daily spend exceeds 2x baseline - Alert when tokens-per-request exceeds threshold (prompt injection or runaway loop) - Track cost per user action to catch regression
Timeout strategy for LLM calls
try:
response = await asyncio.wait_for(
llm_client.complete(prompt),
timeout=30.0 # maximum acceptable wait
)
except asyncio.TimeoutError:
# Alert, log, return fallback
requests.post(f'https://alertsdock.com/ping/{uuid}/fail',
json={'reason': 'llm_timeout'})
return get_fallback_response()
Always have a fallback for LLM timeouts. Your users should not wait more than 30s for any response.
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Uptime Monitoring
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