I need LLM observability for a customer support bot with strict latency and cost limits

For a customer support bot with strict latency and cost limits, the best observability setup is usually lightweight, sampled, and mostly async—not “log everything.”

Recommended approach

1) Track only the essentials on every request

Capture a minimal event for each LLM call:

• request_id / conversation_id
• model
• prompt_tokens, completion_tokens, total_tokens
• latency_ms
• status / error_code
• tool_calls count
• retrieval_docs_used count, if applicable
• customer_tier or route only if needed for debugging

This gives you cost and performance visibility with very low overhead.

2) Sample expensive artifacts

Do not store full prompts/responses for every turn.

Use:

• 1–5% sampling for full traces
• higher sampling for:
  • errors
  • slow requests
  • escalations
  • low-confidence outputs
  • policy violations

This keeps storage and compute costs down.

3) Separate synchronous vs asynchronous telemetry

To protect latency:

• Emit metrics using a non-blocking buffer
• Send logs/traces async
• Use a background worker or fire-and-forget queue
• Never wait on the observability backend in the request path

4) Use metrics first, traces second

For support bots, the most useful SLOs are usually:

• p50 / p95 / p99 latency
• token usage per ticket
• cost per resolved conversation
• tool call rate
• fallback / escalation rate
• answer acceptance rate
• retrieval hit rate

Use traces only when you need root-cause analysis.

5) Add targeted quality signals

Instead of expensive human review on every interaction, add cheap signals like:

• user re-ask / repetition rate
• thumbs up/down
• escalation to human
• abandoned conversation
• “issue resolved” click
• hallucination heuristics for risky intents

6) Keep prompt/response storage bounded

If you must store content:

• redact PII
• truncate to relevant spans
• retain only recent interactions
• store hashes or embeddings for search where possible
• use configurable retention windows

Minimal architecture

Request path

1. Bot processes user input
2. LLM call happens
3. Lightweight telemetry event is queued locally
4. Response returns immediately

Async path

1. Telemetry worker batches events
2. Sends to metrics/logging backend
3. Optional sampling sends full traces to trace store
4. Alerting runs on metrics, not raw logs

Practical stack options

If you want low overhead:

• OpenTelemetry for traces/metrics
• Prometheus + Grafana for metrics
• ClickHouse / BigQuery / Datadog for sampled logs
• Kafka / SQS / Redis queue for async telemetry buffering

For LLM-specific observability:

• Langfuse
• Helicone
• Arize Phoenix
• Datadog LLM Observability
• OpenTelemetry-based custom instrumentation

Cost-saving rules of thumb

• Log full text only for failures and a small sample
• Avoid synchronous remote calls from the bot service
• Batch telemetry
• Drop low-value events during load spikes
• Use adaptive sampling: more sampling on errors, less on healthy traffic

A good default policy

• 100% metrics on all requests
• 10% traces on normal traffic
• 100% traces on errors/slow requests
• 30-day retention for metrics
• 7-day retention for sampled traces
• PII redaction before storage

If you want, I can give you:

1. a reference architecture,
2. a minimal OpenTelemetry schema, or
3. a vendor/tool comparison optimized for low latency and cost.