AI App Analytics: The 10 Metrics Every LLM Application Must Track
Building an AI-powered product? Google Analytics tracks page views. Mixpanel tracks button clicks. But neither tells you if your AI is actually helping users. Here are the metrics that actually matter.
Why Traditional Analytics Fall Short for AI Apps
Traditional product analytics tools were built for deterministic software. Click a button, get a predictable result. But AI applications are fundamentally different:
- ✕Same input can produce different outputs every time
- ✕"Success" isn't binary — AI responses exist on a quality spectrum
- ✕Costs are variable and per-request, not fixed infrastructure
- ✕Quality can silently degrade without any visible errors
You need AI app analytics — purpose-built metrics that capture what matters for LLM-powered experiences.
The 10 Essential AI App Metrics
Quality Metrics
Response Quality Score
An automated score (0-1) measuring how relevant, accurate, and helpful each LLM response is. This is the single most important metric for any AI application.
How to track: Use Phospho's automatic quality scoring, which evaluates every response in real-time.
Hallucination Rate
The percentage of responses that contain fabricated or incorrect information. Critical for applications where accuracy is non-negotiable (healthcare, finance, legal).
Target: Below 5% for most applications. Below 1% for high-stakes domains.
User Satisfaction Rate
The ratio of positive to negative user feedback (thumbs up/down, ratings). The ground truth for whether your AI is actually helping users accomplish their goals.
How to track: Phospho collects inline feedback and correlates it with specific interactions.
Task Completion Rate
What percentage of user sessions result in the user's goal being achieved? This combines AI quality with UX design to measure end-to-end effectiveness.
Tip: Track at the session level, not individual message level.
Cost Metrics
Cost Per Interaction
Total API cost for each user query, including all LLM calls, embeddings, and processing. This is how you catch cost spikes before they blow your budget.
Formula: (input_tokens × input_price) + (output_tokens × output_price) per call
Cost Per User
Monthly API spend per active user. Essential for understanding unit economics and setting sustainable pricing for your AI product.
Watch for: Power users who generate 50-100x the average cost.
Token Efficiency
Output quality relative to tokens consumed. Are you getting good results with efficient prompts, or are you burning tokens on bloated system prompts and unnecessary context?
Optimization: Track this metric before and after prompt changes.
Performance Metrics
Time to First Token
How quickly does the AI start responding? For streaming applications, this is the most important latency metric — it determines the user's perception of speed.
Target: Under 500ms for great UX. Over 2s feels sluggish.
End-to-End Latency
Total response time including all processing: retrieval, model inference, post-processing, and response formatting. Track p50, p95, and p99 percentiles.
Tip: Break this down by pipeline stage to identify specific bottlenecks.
Error Rate
Percentage of failed or timed-out LLM calls. Includes API errors, rate limit hits, and content filter rejections. Even failed calls cost you money.
Target: Below 1%. Above 5% indicates a systemic issue.
Track all 10 metrics with Phospho
Purpose-built AI app analytics. Two lines of code. Real-time dashboard with every metric that matters for your LLM application.
Get Phospho Pro — $49/moHow to Implement AI App Analytics
You need a platform built specifically for LLM applications. Traditional analytics tools can't capture prompt/response pairs, score quality automatically, or correlate user feedback with specific interactions.
import phospho
# Initialize once
phospho.init(api_key="ph_your_key")
# Log interactions with full metadata for analytics
phospho.log(
input=user_query,
output=llm_response,
user_id=user_id,
session_id=session_id,
metadata={
"model": "gpt-4",
"tokens_in": usage.prompt_tokens,
"tokens_out": usage.completion_tokens,
"latency_ms": elapsed_ms,
"feature": "chat_assistant",
}
)
# Phospho automatically calculates quality scores,
# cost metrics, and performance analyticsBuilding a Metrics-Driven AI Product Culture
The best AI product teams don't just track metrics — they build feedback loops:
Stop guessing. Start measuring.
Your AI app is only as good as your ability to understand it. Get real AI app analytics with Phospho.
Get Phospho Pro — $49/mo Early AccessFounding member pricing locked in forever.