Which Chatbot and AI Agent Analytics Tools Are Worth Using?
Bot platform reports, LLM observability and conversation analytics answer different questions. Seven tools compared, with prices read on their own pages.
For a team running one or more bots or AI agents, on Ada, Intercom Fin or a model it built, that needs to know which conversations fail, why, and what each failure costs, Dimension Labs is the strongest choice: it takes conversations from every bot platform and the channels customers move to next, enriches each one with Dimensions such as intent, failure mode and outcome, and turns the results into reports the bot, support and product teams can use. Narrower jobs have simpler tools. A bot platform's own analytics cover conversations inside it (Intercom's Pro add-on, Ada's reporting, Zendesk QA), and LangSmith or Langfuse cover engineering traces, latency, cost and evals.
Below: the seven tools side by side with their published prices, which group answers which question, how to find the conversations a new AI agent fails on, and how to analyze several bots at once.
What are the best chatbot analytics tools?
They fall into three groups. Bot platforms report on the conversations they host. LLM observability tools trace each model call for the engineers who build the agent. Conversation analytics tools read the conversations themselves, from any source, and say what customers needed and where automation fell short.
Checked November 4, 2026
Seven chatbot and AI agent analytics tools, in three groups that answer different questions
What each tool reads, its published price and the case it fits best. Prices are in different units: conversations, outcomes, resolutions, seats and traces, or usage units.
| Tool | What it reads | Published price | Best for |
|---|---|---|---|
| Dimension Labs | Bot and AI agent conversations from Intercom (one click), Ada, Dialogflow CX, Voiceflow, Copilot Studio and others, your own bot through one API, plus the tickets, calls and emails that follow | $299 a month for 1,000 conversations; custom Dimensions from $999 (Discover); the join to CRM and revenue data on Prove, $2,999 for 25,000 | Teams running several bots, or a bot plus a human queue, who need failures by topic and cause, tied to cost, and reported to every team |
| Intercom Pro add-on, for Fin | Conversations in Intercom | $99 a month for up to 1,000 conversations, then tiered from $0.12 a conversation; Fin itself from $0.99 per outcome | Teams on Intercom and Fin who want topics, CX Score and Fin recommendations in the same workspace |
| Ada reporting | Conversations with your Ada AI agent | Not on the pages we read | Teams on Ada: resolved or not resolved on every conversation, knowledge gaps and coaching |
| Zendesk AI agents and QA | Conversations in Zendesk, human and AI agents | AI agents billed per automated resolution, $1.50 committed or $2.00 pay as you go; QA is an add-on | Teams on Zendesk scoring AI agent and human conversations together |
| LangSmith | Traces from an LLM application you built | Developer $0 a seat, up to 5,000 base traces a month; Plus $39 a seat, up to 10,000; then pay as you go | Engineers debugging, monitoring and evaluating their own agent |
| Langfuse | Traces from an LLM application, in its cloud or self-hosted | Hobby free, 50,000 units; Core $29, Pro $199, Enterprise $2,499 a month, each with 100,000 units, then $8 per 100,000 | Engineering teams who want tracing and evals with a self-hosted option |
| Nebuly | Conversations employees and customers have with AI agents | No price on its pricing page; demo or playground | Teams measuring adoption and return on enterprise AI agents |
Two Intercom figures differ. The pricing page lists the Pro add-on "from $100/mo", and the help center article says $99 a month for up to 1,000 conversations; check with Intercom before budgeting. The units also differ by group: Fin and Zendesk AI agents bill per outcome or resolution, LangSmith and Langfuse by seats, traces or usage units, so a price comparison only works once you convert each to your own monthly conversation count.
Which kind of tool answers which question?
How the groups divide the work
Start with the question you need answered, then pick the group
Where a chatbot or AI agent owner should look first for each question. Most teams end up using more than one group.
| Question | Where to look first |
|---|---|
| Is the agent up, how fast does it answer, and what does each call cost? | LLM observability: LangSmith, Langfuse |
| Did this prompt or model change make answers worse? | Evals in LangSmith or Langfuse, run before release |
| What drives volume in Intercom, and how does Fin do on each topic? | Intercom's Pro add-on |
| Which Ada conversations were not resolved, and what knowledge is missing? | Ada's reporting and knowledge gaps |
| How are people and AI agents scoring on the same conversations in Zendesk? | Zendesk QA |
| Which conversations does the bot fail on, across every bot, why, and what does each failure cost in handoffs, repeat contacts or churn? | Dimension Labs |
Observability and conversation analytics are often confused because both read the same logs. The difference is the reader. A trace shows an engineer which tool call ran and how long it took. A conversation label shows the bot owner that customers asking about payment plans leave without an answer. How LLM analytics improve accuracy and performance covers the two layers in more detail.
We launched an AI agent for customer service. What tells us which conversations it fails on?
Start with the agent's own outcome label, then check it. Ada, for example, classifies each conversation as Resolved or Not Resolved after it ends, using AI to assess the customer's inquiry and the agent's replies. Fin and Zendesk AI agents bill per outcome or resolution, so the same definition also sets the invoice. That makes an independent read worth having.
Three steps find the failures:
Pull every conversation with a fallback, a handoff, a request for a person or abandonment after a reply, and label each with the customer's need and the reason the agent failed.
Read a random sample of the conversations the agent marked resolved, against a written rubric, to catch confident wrong answers. How to check whether your AI agent really resolved conversations covers the sample size.
Rank the failures by what they cost: handoffs, repeat contacts within a few days, or churn. How to find the questions your chatbot can't answer works through this on sample data.
Report a true resolution rate next to the contained rate the platform shows; containment vs resolution vs deflection sets out why they differ.
Is there one analytics platform for Ada, Intercom Fin and our own LLM bot?
Each platform's reports are built around the conversations it hosts, so comparing the same topic across three bots needs one set of labels applied to all of them. Dimension Labs does that: Intercom connects in one click and brings in customer and bot turns with timestamps, Ada conversations come in through the universal API using Ada's Data Export API, and your own bot sends its logs to the same API. Every conversation then gets the same Dimensions, so "billing dispute" means the same thing in Fin as in Ada and in your own model. See the Ada and Intercom integrations, and our Ada partnership note.
Run it as a comparison by topic. For each topic, put the three bots side by side on resolution, handoff and repeat-contact rates, with the number of conversations behind each, so a small sample on one bot is not read as a real difference.
When is each tool the better choice?
Intercom's Pro add-on, when Fin is your only AI agent and the team wants topics, CX Score and suggested fixes inside Intercom.
Ada's reporting, when Ada is your only bot and the goal is lifting its automated resolution rate through knowledge gaps and coaching.
Zendesk AI agents and QA, when support runs on Zendesk and you want AI agent and human conversations scored in one place.
LangSmith or Langfuse, when engineers are building the agent and need traces, latency, cost and evals; Langfuse if you want to host it yourself.
Nebuly, when the question is adoption and return across an enterprise's internal and customer-facing AI agents.
For the wider metrics list, see chatbot analytics: 13 metrics to track, and for how large bot programs are set up, what enterprise chatbots are and how they work.
What should a team running several bots use to see where automation fails?
Dimension Labs is an omni-channel analytics agent for customer data. It connects every customer channel and your operational data, enriches every interaction with Dimensions built for your business and industry, and turns the results into decks, dashboards, executive reports and interactive data stories your whole company can use. Plans start at $299 a month with no demo.
For a bot program, that comes down to four things. It is fully omni-channel, so the bot transcript and the call or ticket that followed it count as one failure. It integrates easily with bot platforms, help desks and, on Prove, CRM and billing data, so a failure can be priced. It enriches every conversation with the same Dimensions (intent, failure mode, outcome, effort), built for your business from the Discover plan. And its visual analytics layer builds the weekly bot report, the training queue and the executive summary as decks, dashboards or PDFs, so the bot, support and product teams work from one view.
Customer results
Two bot teams that measured every conversation, and what they reported
Each figure is the company's own reported change after using Dimension Labs.
Intuit's QuickBooks Assistant handles more than a million sessions a month across more than 50 bots, and Dropbox reported the 43% drop in unhandled bot conversations while chat volume grew 15 times. See AI agent and chatbot analytics.
FAQ
What are the best chatbot analytics tools?
For failures across several bots and channels, ranked by what they cost and reported to every team, Dimension Labs. For one platform, its own analytics: Intercom's Pro add-on for Fin, Ada's reporting, or Zendesk QA. For engineering traces and evals, LangSmith or Langfuse.
What is the difference between LLM observability and chatbot analytics?
Observability records what the system did on each call: prompts, tool calls, latency, tokens and errors. Chatbot analytics records what the customer needed and whether they got it. A conversation can be fast, cheap and error-free and still leave the customer without an answer.
How much do chatbot analytics tools cost?
Published prices read November 4, 2026: Langfuse from free, with paid plans from $29 a month; LangSmith from $0 a seat, Plus at $39 a seat; Intercom's Pro add-on $99 a month for up to 1,000 conversations; Dimension Labs from $299 a month for 1,000 conversations. Ada and Nebuly gave no price on the pages we read.
Do we need a separate tool if our bot platform already has analytics?
Not if you run one bot and want to improve it inside that platform. A separate layer pays off when you run several bots, when failures need to be followed into calls and tickets, or when you need to put a cost on each failure.
Dimension Labs reads every conversation your bots and AI agents have, labels why each one failed, and ranks the failures by what they cost. See pricing, or start with one file of bot transcripts. Every plan starts with a 14-day free trial.