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cohere-observability Set up comprehensive observability for Cohere API v2 with metrics, traces, and alerts.
Use when implementing monitoring for Chat/Embed/Rerank operations,
setting up dashboards, or configuring alerts for Cohere integrations.
Trigger with phrases like "cohere monitoring", "cohere metrics",
"cohere observability", "monitor cohere", "cohere alerts", "cohere tracing".
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name cohere-observability description Set up comprehensive observability for Cohere API v2 with metrics, traces, and alerts.
Use when implementing monitoring for Chat/Embed/Rerank operations,
setting up dashboards, or configuring alerts for Cohere integrations.
Trigger with phrases like "cohere monitoring", "cohere metrics",
"cohere observability", "monitor cohere", "cohere alerts", "cohere tracing".
allowed-tools Read, Write, Edit version 1.0.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","ai","nlp","cohere"] compatible-with claude-code
Cohere Observability
Overview
Set up production observability for Cohere API v2 with Prometheus metrics, OpenTelemetry tracing, and AlertManager rules. Tracks per-endpoint latency, token usage, error rates, and costs.
Prerequisites
Prometheus or compatible metrics backend
OpenTelemetry SDK installed
cohere-ai SDK v7+
Instructions
Step 1: Metrics Collection
import { Registry , Counter , Histogram , Gauge } from 'prom-client' ;
const registry = new Registry ();
const requestCounter = new Counter ({
name : 'cohere_requests_total' ,
help : 'Total Cohere API requests' ,
labelNames : ['endpoint' , 'model' , 'status' ],
registers : [registry],
});
const requestDuration = new Histogram ({
name : 'cohere_request_duration_seconds' ,
help : 'Cohere request duration' ,
labelNames : ['endpoint' , 'model' ],
buckets : [0.1 , 0.25 , 0.5 , 1 , 2.5 , 5 , 10 , 30
registers
const
new
Counter
name
'cohere_tokens_total'
help
'Total tokens consumed'
labelNames
'endpoint'
'model'
'direction'
registers
const
new
Counter
name
'cohere_errors_total'
help
'Cohere errors by status code'
labelNames
'endpoint'
'status_code'
registers
const
new
Gauge
name
'cohere_rate_limit_remaining'
help
'Remaining rate limit capacity'
labelNames
'endpoint'
registers
Step 2: Instrumented Client Wrapper import { CohereClientV2 , CohereError , CohereTimeoutError } from 'cohere-ai' ;
const cohere = new CohereClientV2 ();
async function instrumentedCall<T>(
endpoint : string ,
model : string ,
operation : () => Promise <T>
): Promise <T> {
const timer = requestDuration.startTimer ({ endpoint, model });
try {
const result = await operation ();
requestCounter.inc ({ endpoint, model, status : 'success' });
timer ();
const usage = (result as any )?.usage ?.billedUnits ;
if (usage) {
if (usage.inputTokens ) {
tokenCounter.inc ({ endpoint, model, direction : 'input' }, usage.inputTokens );
}
if (usage.outputTokens ) {
tokenCounter.inc ({ endpoint, model, direction : 'output' }, usage.outputTokens );
}
}
return result;
} catch (err) {
requestCounter.inc ({ endpoint, model, status : 'error' });
timer ();
if (err instanceof CohereError ) {
errorCounter.inc ({ endpoint, status_code : String (err.statusCode ) });
} else if (err instanceof CohereTimeoutError ) {
errorCounter.inc ({ endpoint, status_code : 'timeout' });
}
throw err;
}
}
const response = await instrumentedCall ('chat' , 'command-a-03-2025' , () =>
cohere.chat ({
model : 'command-a-03-2025' ,
messages : [{ role : 'user' , content : query }],
})
);
Step 3: OpenTelemetry Tracing import { trace, SpanStatusCode , SpanKind } from '@opentelemetry/api' ;
const tracer = trace.getTracer ('cohere-client' , '1.0.0' );
async function tracedCohereCall<T>(
endpoint : string ,
model : string ,
operation : () => Promise <T>
): Promise <T> {
return tracer.startActiveSpan (
`cohere.${endpoint} ` ,
{ kind : SpanKind .CLIENT },
async (span) => {
span.setAttribute ('cohere.model' , model);
span.setAttribute ('cohere.endpoint' , endpoint);
try {
const result = await operation ();
const usage = (result as any )?.usage ?.billedUnits ;
if (usage) {
span.setAttribute ('cohere.tokens.input' , usage.inputTokens ?? 0 );
span.setAttribute ('cohere.tokens.output' , usage.outputTokens ?? 0 );
}
span.setStatus ({ code : SpanStatusCode .OK });
return result;
} catch (err : any ) {
span.setStatus ({ code : SpanStatusCode .ERROR , message : err.message });
span.recordException (err);
if (err instanceof CohereError ) {
span.setAttribute ('cohere.error.status' , err.statusCode ?? 0 );
}
throw err;
} finally {
span.end ();
}
}
);
}
Step 4: Structured Logging import pino from 'pino' ;
const logger = pino ({ name : 'cohere' , level : process.env .LOG_LEVEL ?? 'info' });
function logCohereCall (
endpoint : string ,
model : string ,
durationMs : number ,
status : 'success' | 'error' ,
meta ?: Record <string , unknown >
) {
logger[status === 'error' ? 'error' : 'info' ]({
service : 'cohere' ,
endpoint,
model,
durationMs,
status,
...meta,
});
}
async function observedCall<T>(
endpoint : string ,
model : string ,
fn : () => Promise <T>
): Promise <T> {
return tracedCohereCall (endpoint, model, () =>
instrumentedCall (endpoint, model, async () => {
const start = Date .now ();
try {
const result = await fn ();
logCohereCall (endpoint, model, Date .now () - start, 'success' , {
tokens : (result as any )?.usage ?.billedUnits ,
});
return result;
} catch (err) {
logCohereCall (endpoint, model, Date .now () - start, 'error' , {
error : err instanceof CohereError ? err.statusCode : 'timeout' ,
});
throw err;
}
})
);
}
Step 5: Alert Rules
groups:
- name: cohere
rules:
- alert: CohereHighErrorRate
expr: |
rate(cohere_errors_total[5m]) /
rate(cohere_requests_total[5m]) > 0.05
for: 5m
labels:
severity: warning
annotations:
summary: "Cohere error rate > 5%"
description: "{{ $labels.endpoint }} error rate: {{ $value | humanizePercentage }} "
- alert: CohereRateLimited
expr: rate(cohere_errors_total{status_code="429"}[5m]) > 0.1
for: 2m
labels:
severity: warning
annotations:
summary: "Cohere rate limiting detected"
- alert: CohereHighLatency
expr: |
histogram_quantile(0.95,
rate(cohere_request_duration_seconds_bucket[5m])
) > 10
for: 5m
labels:
severity: warning
annotations:
summary: "Cohere P95 latency > 10s"
- alert: CohereAuthFailure
expr: cohere_errors_total{status_code="401"} > 0
for: 1m
labels:
severity: critical
annotations:
summary: "Cohere authentication failure — check API key"
- alert: CohereHighTokenBurn
expr: rate(cohere_tokens_total[1h]) > 100000
for: 15m
labels:
severity: warning
annotations:
summary: "Cohere token burn rate > 100K/hour"
Step 6: Metrics Endpoint
import express from 'express' ;
const app = express ();
app.get ('/metrics' , async (req, res) => {
res.set ('Content-Type' , registry.contentType );
res.send (await registry.metrics ());
});
Dashboard Panels (Grafana) Panel Query Type Request Rate rate(cohere_requests_total[5m])Time series Error Rate rate(cohere_errors_total[5m]) / rate(cohere_requests_total[5m])Stat P50/P95 Latency histogram_quantile(0.95, rate(cohere_request_duration_seconds_bucket[5m]))Time series Token Usage rate(cohere_tokens_total[1h])Bar chart Errors by Code sum by (status_code)(rate(cohere_errors_total[5m]))Pie chart
Output
Prometheus metrics for requests, latency, tokens, and errors
OpenTelemetry traces with Cohere-specific attributes
Structured JSON logging with pino
AlertManager rules for error rate, latency, auth, and cost
Error Handling Issue Cause Solution Missing token metrics Usage not in response Check response.usage.billedUnits High cardinality Too many model labels Use model family, not exact version Alert storm Threshold too low Tune thresholds for your traffic Trace gaps Missing context propagation Ensure OTel context flows through async
Resources
Next Steps For incident response, see cohere-incident-runbook.
