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Get Started Free →Monitor Claude API calls — log tokens, latency, costs, errors, and Use when working with observability patterns. set up alerts for production Claude integrations. Trigger with "anthropic monitoring", "claude observability", "track claude usage", "anthropic logging".
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -44% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -7% | 0% |
Every messages.create call should be instrumented. Track tokens, latency, cost, model, and errors.
typescriptimport Anthropic from '@claude-ai/sdk'; const client = new Anthropic(); async function trackedCreate(params: Anthropic.MessageCreateParams) { const start = performance.now(); try { const message = await client.messages.create(params); const durationMs = Math.round(performance.now() - start); const log = { timestamp: new Date().toISOString(), model: message.model, input_tokens: message.usage.input_tokens, output_tokens: message.usage.output_tokens, cache_read_tokens: message.usage.cache_read_input_tokens || 0, duration_ms: durationMs, stop_reason: message.stop_reason, estimated_cost: estimateCost(message.model, message.usage), }; console.log('anthropic_request', JSON.stringify(log)); return message; } catch (err) { const durationMs = Math.round(performance.now() - start); console.error('anthropic_error', JSON.stringify({ timestamp: new Date().toISOString(), model: params.model, error_type: err instanceof Anthropic.APIError ? err.error?.type : 'unknown', status: err instanceof Anthropic.APIError ? err.status : null, request_id: err instanceof Anthropic.APIError ? err.headers?.['request-id'] : null, duration_ms: durationMs, })); throw err; } } function estimateCost(model: string, usage: Anthropic.Usage): number { const rates: Record<string, [number, number]> = { 'claude-opus-4-20250514': [15, 75], 'claude-sonnet-4-20250514': [3, 15], 'claude-haiku-4-5-20251001': [0.80, 4], }; const [inputRate, outputRate] = rates[model] || [3, 15]; return (usage.input_tokens * inputRate + usage.output_tokens * outputRate) / 1_000_000; }
| Metric | Source | Alert Threshold | |--------|--------|----------------| | Error rate | error logs | > 5% over 5 minutes | | p95 latency | duration_ms | > 10s (Sonnet) | | Daily cost | estimated_cost sum | > 2x daily average | | 429 rate | error_type = rate_limit | > 10/minute | | 529 rate | error_type = overloaded | > 5/minute | | Token usage | input_tokens + output_tokens | > daily budget |
| Error | Cause | Solution | |-------|-------|----------| | API Error | Check error type and status code | See clade-common-errors |
See Logging Wrapper with trackedCreate(), estimateCost() function, Key Metrics table with alert thresholds, and Anthropic Console Monitoring section above.
See clade-incident-runbook for when things go wrong.
clade-install-authEach section contains production-ready code examples. Copy and adapt them to your use case.
Integrate the patterns that match your requirements. Test each change individually.
Run your test suite to confirm the integration works correctly.
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