What should I use to monitor token spend and latency across multiple models?

To monitor token spend and latency across multiple models, you’ll usually want a mix of:

1. App-level logging/metrics
2. A tracing/observability tool
3. Usage dashboards from the model providers
4. A central cost tracker if you use multiple vendors

Good options

If you want an out-of-the-box observability layer

• LangSmith — good for tracing LLM calls, latency, token usage, and debugging chains/agents.
• Helicone — popular for API-level logging, cost tracking, latency, and model comparison across providers.
• Arize Phoenix — strong for tracing/evaluation/observability, especially if you also care about quality and debugging.
• OpenTelemetry + Grafana/Prometheus — best if you want full control and already have an observability stack.

If you mainly need cost tracking

• Helicone
• OpenAI usage dashboard / provider dashboards
• Custom aggregation in your DB/warehouse from logs

If you need multi-model / multi-provider support

• Helicone or OpenTelemetry-based instrumentation
• Langfuse is also a strong choice for multi-model tracing, cost, and prompt/version tracking.

What to track

At minimum, capture:

• model name/provider
• request timestamp
• latency
• input tokens
• output tokens
• total tokens
• estimated cost
• success/error status
• request ID / trace ID
• prompt version / experiment tag

Recommendation

• Small/medium teams: start with Helicone or Langfuse
• Deep debugging and eval workflows: LangSmith or Arize Phoenix
• Enterprise/custom infra: OpenTelemetry + Grafana + warehouse

If you tell me your stack (OpenAI/Anthropic/Gemini/etc., Python/JS, and whether you want self-hosted), I can recommend the best fit.