# Trodo

> Trodo is an AI agent analytics and AI product analytics platform. We help product and engineering teams trace AI agent runs, measure tool call success, monitor LLM and agent observability signals, and tie AI behavior to product outcomes like retention and conversion.

Keywords: AI agent analytics, AI product analytics, AI observability, LLM observability, agent tracing, tool call analytics, product analytics for AI-native products.

## Primary

- [Trodo home](https://trodo.ai): AI agent analytics and AI product analytics platform overview.
- [Pricing](https://trodo.ai/pricing): Simple, transparent pricing for Trodo.
- [Blog index](https://trodo.ai/blog): Guides on AI agent analytics, AI product analytics, and AI observability.

## AI Agent Analytics

- [Why Agentic AI Products Struggle With Retention (and How to Fix It)](https://trodo.ai/blog/agentic-ai-user-retention): Agentic AI products face unique retention challenges that traditional product strategies miss. Here is what drives churn in AI-native apps and the measurement frameworks that fix it.
- [Agent Analytics vs Agent Observability vs LLM Observability: Clear Definitions](https://trodo.ai/blog/agent-analytics-vs-agent-observability-vs-llm-observability): Agent analytics, agent observability, and LLM observability get used interchangeably — but they are not the same. Here is a clear disambiguation of each term, the audiences they serve, and how they fit together in a production AI stack.
- [AI Agent Observability vs Analytics: What's the Difference?](https://trodo.ai/blog/ai-agent-observability-vs-analytics): AI agent observability and AI agent analytics are often confused but serve different audiences and answer different questions. Here is how to think about both and when you need each.
- [How to Measure AI Agent Performance: Traces, Tool Calls & KPIs](https://trodo.ai/blog/how-to-measure-ai-agent-performance): A practical guide to the KPIs, metrics, and measurement approaches that product teams use to evaluate AI agent performance in production — from trace-level data to business outcomes.
- [Best AI Agent Analytics Tools in 2026: Monitor LLM Agents at Scale](https://trodo.ai/blog/ai-agent-analytics-tools-2026): A practical comparison of the leading AI agent analytics tools in 2026 — covering what each does, who it is for, and how to choose the right stack for your product team.
- [What Is AI Agent Analytics? The Complete Guide for Product Teams](https://trodo.ai/blog/what-is-ai-agent-analytics): AI agent analytics explained: how to trace LLM agents, measure tool call success, connect agent performance to product outcomes, and why flat event tracking is no longer enough.
- [Agent Analytics: Measuring AI Agents, Tools, and Traces in Production](https://trodo.ai/blog/agent-analytics-ai-agents-production): A practical guide to agent analytics: tracing orchestrations, tool calls, latency, and failure modes so you can ship reliable AI features and prove value with data.

## AI Product Analytics

- [How Product Analytics Changes When You Ship AI Features](https://trodo.ai/blog/product-analytics-ai-features): Shipping AI features changes what you need to measure, how you measure it, and how you act on the data. Here is how to evolve your product analytics practice for AI-powered products.
- [Product Analytics for Chatbots and AI Copilots](https://trodo.ai/blog/product-analytics-for-chatbots): How to measure, improve, and grow products built around chatbots and AI copilots — the product analytics approach that goes beyond session counts to trace-level behavioral insight.
- [Best AI Product Analytics Tools in 2026: A Buyer's Guide](https://trodo.ai/blog/best-ai-product-analytics-tools-2026): A practical comparison of the leading AI product analytics tools in 2026. We cover what each platform does well, what it misses, and how to pick the right stack for AI-native product teams.
- [How to Measure AI Feature Adoption in Your Product](https://trodo.ai/blog/ai-feature-adoption-metrics): Measuring AI feature adoption requires different metrics than traditional feature tracking. This guide covers the frameworks, signals, and analytics approaches that work for AI-powered products.
- [AI Product Analytics vs Traditional Product Analytics: What Actually Changes](https://trodo.ai/blog/ai-product-analytics-vs-traditional-analytics): Traditional product analytics was built for apps with clicks and screens. AI product analytics is built for apps with prompts, tool calls, and agents. Here is what changes — events, funnels, retention, and the questions each can answer.
- [AI Product Analytics: The 2026 Guide for AI-Native Teams](https://trodo.ai/blog/ai-product-analytics-guide-2026): Everything product teams need to know about AI product analytics in 2026 — what it measures, how it differs from traditional analytics, and how to build a measurement foundation for AI-native applications.
- [AI Product Analytics: Unifying Usage Data, Models, and Agent Performance](https://trodo.ai/blog/ai-product-analytics-unified-view): Why AI product analytics blends traditional product metrics with model and agent signals—and how to build a coherent measurement stack for AI-native products.

## AI Observability

- [LLM Observability vs Product Analytics: Two Tools, One Goal](https://trodo.ai/blog/llm-observability-vs-product-analytics): LLM observability and product analytics answer different questions for different teams, but they share one goal: making AI products better. Here is how to use both effectively.
- [Agent Observability Best Practices for Production AI in 2026](https://trodo.ai/blog/agent-observability-best-practices): A practical playbook for agent observability: what to instrument, which signals matter, how to connect agent traces to product KPIs, and the mistakes most teams make in their first six months.
- [Trodo vs LangSmith: AI Product Analytics or LLM Observability?](https://trodo.ai/blog/trodo-vs-langsmith): A direct comparison of Trodo and LangSmith — what each does, who it serves, and how to decide which one (or both) your AI team needs in 2026.
- [Best AI Observability Tools in 2026: A Practical Comparison](https://trodo.ai/blog/ai-observability-tools-2026): A practical 2026 comparison of AI observability tools — Arize, Langfuse, Helicone, Datadog LLM, Honeycomb, and Trodo — covering what each does well, where they fall short, and how to choose the right one for your AI product.
- [What Is AI Observability? The 2026 Guide for Product & Engineering Teams](https://trodo.ai/blog/what-is-ai-observability): AI observability explained: what it is, how it differs from traditional observability and LLM observability, the signals that matter for AI agents and LLM-powered products, and how it fits alongside AI product analytics.

## Fundamentals & Comparisons

- [Mixpanel vs Amplitude vs Trodo: Which Product Analytics Platform in 2026?](https://trodo.ai/blog/mixpanel-vs-amplitude-vs-trodo): A direct comparison of Mixpanel, Amplitude, and Trodo for product analytics in 2026 — covering strengths, weaknesses, and which platform fits which team, especially those shipping AI features.
- [Product Analytics for SaaS: Funnels, Retention & Feature Tracking](https://trodo.ai/blog/product-analytics-for-saas): A practical guide to product analytics for SaaS companies — covering the core frameworks, key metrics, and modern approaches for teams building products that include AI features.
- [What Is Product Analytics? Funnels, Retention, and Event Data Explained](https://trodo.ai/blog/product-analytics-fundamentals): Learn what product analytics means in practice: events, funnels, cohorts, and how teams use them to improve activation and retention—with a clear lens on modern product intelligence.

## More articles

- [Trodo vs PostHog: Which Product Analytics Tool for AI-Native Teams?](https://trodo.ai/blog/trodo-vs-posthog): Trodo and PostHog both call themselves product analytics platforms, but they target different problems. Here is a clear comparison for teams shipping AI-native products in 2026.

## About

Trodo gives product and engineering teams a single analytics layer for AI-native products — prompts, tool calls, agent traces, user sessions, and product events in one place. If you build chatbots, copilots, or autonomous agents and need to answer "is it working, for whom, and what should we build next?", Trodo is built for that question.