LLM observability and cost control

LLM observability and cost control usually means tracking what your models are doing, how well they’re doing it, and how much they’re costing so you can improve quality without runaway spend.

What to observe

Key signals to collect:

• Requests
  • prompt text / template version
  • user/session/app metadata
  • model name and version
  • temperature, max tokens, tools used
• Latency
  • end-to-end response time
  • time to first token
  • tool/function-call latency
• Token usage
  • input tokens
  • output tokens
  • total tokens
• Quality
  • success/failure rate
  • user ratings / thumbs up-down
  • hallucination or policy-violation flags
  • task-specific accuracy or pass rate
• Reliability
  • retries
  • timeouts
  • rate-limit errors
  • fallback model usage
• Cost
  • per request
  • per user / tenant / feature
  • per conversation
  • per model / endpoint
• Safety and compliance
  • PII detection/redaction
  • toxicity / unsafe content
  • prompt injection attempts
  • data retention / access logs

Cost control tactics

Ways to reduce spend:

• Use smaller/cheaper models first
  • route easy queries to cheaper models
  • escalate only hard cases to larger models
• Limit tokens
  • cap output tokens
  • keep prompts concise
  • summarize long conversation history
• Cache aggressively
  • semantic cache for repeated questions
  • response caching for deterministic prompts
• Batch and compress
  • batch embeddings or classification calls
  • shorten tool outputs before sending back to the LLM
• Optimize prompts
  • remove redundant instructions
  • test prompt variants with the same task
• RAG discipline
  • retrieve fewer, more relevant chunks
  • chunk and rank better to avoid long contexts
• Control retries
  • retry only on transient errors
  • avoid blind repeated calls
• Set budgets and alerts
  • per-team/per-tenant spend caps
  • anomaly detection on token spikes
• Measure cost per outcome
  • cost per resolved ticket
  • cost per completed workflow
  • cost per successful answer

Observability stack

A typical setup includes:

• Tracing
  • OpenTelemetry-style traces across app, retriever, LLM, tools
• Metrics
  • Prometheus/Grafana or cloud metrics
• Logs
  • structured logs with request IDs and redaction
• LLM-specific tools
  • prompt/response capture
  • evaluation dashboards
  • experiment tracking
  • human review queues

Practical KPIs

Useful KPIs to watch:

• p50 / p95 latency
• token usage per request
• cost per 1k requests
• cache hit rate
• fallback rate
• task success rate
• user satisfaction
• error rate
• average context size

Best practices

• Tag everything with tenant, feature, model, and prompt version
• Store redacted prompts/responses when possible
• Build dashboards by workflow, not just by model
• Add alerts for token spikes, latency spikes, and error spikes
• Run A/B tests on prompts and routing policies
• Review outliers manually to catch regressions

Simple rule of thumb

To control LLM cost, optimize in this order:

1. Reduce unnecessary calls
2. Reduce tokens
3. Use cheaper models
4. Cache repeated work
5. Improve routing and retrieval
6. Monitor and alert continuously

If you want, I can also give you:

• a reference architecture
• a dashboard template
• a cost-control policy
• or a sample implementation using OpenTelemetry + Prometheus