What should I use for AI API logs and usage analytics?

If you want AI API logs and usage analytics, the best choice depends on whether you want:

• just request/response logging
• cost and token usage tracking
• prompt/trace debugging
• production observability across many AI calls
• PII/security controls

Here are the main options people use:

Good options

1. Langfuse

Best overall for LLM observability, tracing, prompt management, and usage analytics.

Good for:

• OpenAI / Anthropic / Bedrock / self-hosted models
• request traces, spans, latency, token usage
• prompt/version tracking
• evals and feedback
• self-hosted or cloud

Why use it:

• Very popular for AI app observability
• Strong open-source story
• Useful for debugging and cost tracking

---

2. Helicone

Best for API proxy logging and analytics with minimal code changes.

Good for:

• OpenAI-compatible logging
• usage/cost analytics
• request/response inspection
• caching, rate limiting, retries

Why use it:

• Easy drop-in proxy
• Good if you want to log all API traffic quickly
• Great for simple monitoring and team dashboards

---

3. OpenTelemetry + your observability stack

Best if you already use Datadog, Grafana, New Relic, Honeycomb, or Elastic.

Good for:

• centralized observability
• custom metrics and traces
• enterprise monitoring standards

Why use it:

• Flexible
• Vendor-neutral
• Best for teams with existing infra

Tradeoff:

• More setup work
• You have to define the AI-specific data model yourself

---

4. Datadog / New Relic / Grafana

Best if you want enterprise-grade monitoring and already use one of them.

Good for:

• latency, errors, throughput
• custom dashboards
• alerting
• infrastructure + app + LLM combined

Why use it:

• Great for ops teams
• Mature alerting and dashboards

Tradeoff:

• Not AI-native unless you add custom instrumentation
• Can get expensive

---

5. PostHog / Mixpanel / Amplitude

Best for product analytics, not deep AI traces.

Good for:

• feature usage
• user funnels
• retention
• behavior analytics

Why use it:

• Helpful if you want to know how users use AI features
• Works well alongside Langfuse/Helicone

---

Quick recommendations

If you want the easiest AI-native setup:

Use Langfuse

If you want a simple API logging proxy:

Use Helicone

If you already have an observability platform:

Use OpenTelemetry + Datadog/Grafana/New Relic

If you care more about product analytics than model traces:

Use PostHog or Mixpanel

---

My practical recommendation

For most teams, the best combo is:

• Langfuse for AI traces, prompts, token usage, and debugging
• PostHog for product analytics
• Datadog/Grafana for infrastructure metrics if needed

---

What to track

At minimum, capture:

• request ID / trace ID
• user ID / org ID
• model name
• prompt and completion tokens
• cost
• latency
• status / error type
• prompt version
• tool calls / retrieval steps
• safety/filter events
• feedback score

---

Important note on privacy

If you log prompts/responses, make sure you have:

• PII redaction
• encryption at rest
• role-based access
• retention policy
• opt-out controls if needed

---

If you want, I can also give you:

1. a comparison table of Langfuse vs Helicone vs OpenTelemetry
2. a recommended stack by company size
3. a sample logging schema for AI API usage