LLM Analytics and Testing for AI Startups
Purpose-built analytics for AI-native products. Track LLM cost, latency, and quality. Run prompt and model A/B tests. Ship eval gates in CI. Know what your AI spends, how it performs, and whether it's getting better.
Cost and ROI
- Total LLM cost: Daily cost by model over a selected period.
- Cost by model: GPT vs Claude vs Gemini vs OpenRouter — head-to-head spend.
- Cost by feature: World generation, support bot, design agent, analytics agent, etc.
- Cost by user: Find high-cost users and abusive usage patterns.
- Cost per successful output: Cost per published world, completed game, solved ticket.
- AI feature ROI: Cost of an AI feature vs activation, retention, or revenue impact.
Performance and Tracing
- Latency analysis: AI response time by model, feature, or prompt version.
- Token usage: Input/output tokens by model, prompt, user, or workflow.
- Trace analysis: Full LLM traces for debugging multi-step agents.
- Bad output triage: Identify low-quality, unsafe, or failed outputs.
Quality and Evals
- Evaluation scoring: Run evals against LLM events; generate eval reports.
- Prompt quality analysis: Compare prompt versions by eval, cost, latency, downstream conversion.
- Model quality comparison: Models compared on eval score, user action, cost, and latency.
- Review queue and clustering: Items needing human review; cluster outputs into themes.
Engagement Tiers
- Diagnostic (2 weeks): Audit tracing, evals, cost, and quality coverage. Identify the three biggest blind spots.
- Build (12 weeks, 10 hrs/week): Embedded as fractional AI product lead service. Analytics + testing engine live. Prompt and model experiments running. Library of agents, skills, and apps shipped.
- Operate (monthly): Monthly readout on cost, quality, experiments, and what to test next.
Frequently Asked Questions
What is LLM analytics?
LLM analytics is the practice of tracking, measuring, and optimizing large language model usage in production — covering cost, latency, token analysis, trace inspection, quality evaluation, and prompt/model A/B testing.
What LLM cost tracking is included?
Total LLM cost, cost by model, cost by feature, cost by user, cost per successful output, and AI feature ROI.
How does prompt and model A/B testing work?
Compare different prompt versions or different LLMs by evaluation score, cost, latency, and downstream user conversion, using proper exposure accounting and sequential analysis.
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