Embedded Analytics Software
Embedded analytics software renders the dashboard. It has no idea what your AI agent did for the customer looking at it.
Luzmo, Qrvey, AWS QuickSight, and ThoughtSpot all solve the same problem: put a dashboard inside your own product instead of sending customers somewhere else, with row-level security so one customer's view never leaks into another's. None of them ship a definition of deflection rate, cost per resolution, or resolution rate, and none of them know what a LangChain, LlamaIndex, or CrewAI trace event even is. This page verifies what each platform actually charges today, names the AI features they added that get confused with agent monitoring, and covers the build step every one of them leaves for you to do yourself.
The baseline
What embedded analytics software actually renders
Embedded analytics software exists to solve one specific problem: your product has data worth showing a customer, and you would rather render that inside your own app than build a charting engine from scratch or send people to a separate reporting tool. Luzmo and Qrvey built their entire product around this from day one, both marketing themselves directly at SaaS teams that need white-labeled, multi-tenant dashboards with row-level security baked in. AWS QuickSight and ThoughtSpot came at it from the opposite direction, general BI platforms that later added an embedded mode and session-based pricing on top of a product built for internal analysts first.
Every platform in the category handles the same short list of jobs: connect to a data source, let someone design a chart or dashboard once, wrap the result in permissions so a given customer only ever sees rows tagged as theirs, and hand you an SDK or an iframe to drop the finished dashboard into your own interface. What none of them do is tell you which numbers deserve a spot on that dashboard in the first place. For a general SaaS product, that answer is usually obvious: usage, spend, adoption. For a product built around an AI agent, it is not, and that gap is where the real work starts.
What each one actually charges
Four platforms, and what's real about each one's pricing
Verified against each vendor's own current pricing page, not a listicle repeating last year's numbers.
Luzmo
Dropped its old tiered pricing for a single plan, Embedded Everywhere, at €1,995 a month billed annually, including 500 AI conversations and scaling further on monthly active users or added AI conversations. Built specifically for white-label SaaS embedding, not a general BI tool with embedding bolted on.
AWS QuickSight
Prices embedded reader sessions on their own meter: $250 a month for 500 sessions at thirty minutes each, dropping to roughly $20,000 a year for 50,000 sessions on an annual commitment. A separate $250 a month infrastructure fee applies once Q&A features are enabled for a Pro user.
ThoughtSpot
The embedded Developer plan is free for the first year, capped at ten users and twenty five million rows, enough to prototype a real integration before spending anything. Its production Enterprise embedded tier, the one with an SLA, is quoted through sales only, with no published number.
Qrvey, Domo, Sisense, and Tableau
All quote embedded pricing through a sales conversation rather than publishing a number. Qrvey advertises flat-rate licensing for unlimited tenants once you get a quote; the others price closer to a traditional enterprise BI deal, scaled to your data volume and user count.
A name collision worth untangling
The 'AI agent' inside your BI tool is not the AI agent you are trying to monitor
Every major embedded analytics platform shipped its own AI agent recently, and the naming makes the category harder to shop, not easier. Qrvey's latest release is built around Qrvey Sidekick, an assistant that lets someone build a chart or explore a dataset by typing a question instead of dragging fields around. Luzmo calls its version Luzmo IQ, and ThoughtSpot calls its version Spotter. All three do the same job: they are AI acting inside the analytics tool, helping a human build or query a dashboard faster.
None of those three are watching a different AI agent, the one your own product runs for your own paying customers. If your product is a support copilot, a sales assistant, or any other agent sold inside a B2B2C SaaS product, the question your customer actually asks is not "can I build a chart quickly." It is "what did the agent resolve for me, and what did it cost." A query-building assistant embedded inside a BI tool answers neither question, and searching for embedded analytics with AI agents in mind is exactly how a team ends up licensing a platform that solves the wrong problem.
The platforms above are still worth using for what they were built for: rendering scoped dashboards fast. AiAgRe's Node SDK reads the same underlying agent events, tags each one with an organization and a customer identity at ingestion, and ships white-labeled dashboard components with deflection rate and cost per resolution already defined, rather than leaving that definition as a project for whichever embedded platform you picked.
When embedding a dashboard stops being enough
Three signs the platform you picked won't cover the agent-specific part
None of these show up while you're comparing pricing pages. All three show up once a real customer opens the dashboard.
Your engineers are defining deflection rate from scratch
Every embedded platform above gives you a chart builder, not a definition of what counts as a resolved conversation for your specific agent. That definition, and the query that calculates it correctly across edge cases, is work your team does once and then maintains forever.
The AI feature you bought builds queries, not agent reports
Qrvey Sidekick, Luzmo IQ, and Spotter all help someone inside your team ask a question of the data faster. None of them read a LangChain or CrewAI trace, so none of them can tell your customer what their agent actually did last week without you building that pipeline first.
Row-level security isn't the same as agent-tenant isolation
A platform's row-level security keeps one customer's rows out of another customer's view once the data is loaded correctly. It says nothing about tagging agent events with the right customer identity at ingestion, the same tenant-isolation problem covered in the guide to multi-tenant analytics for AI agents.
Related reading
Where this fits next to agent monitoring and evaluation
Embedded analytics platforms are not the only category that gets confused with agent-specific tooling. AI agent monitoring tools like Langfuse and Helicone trace what an agent did for your own engineers, a different audience than the customer-facing dashboards this page covers. Evaluation tools score an agent before it ships, not after a real customer is using it. The guide to customer-facing analytics for AI agents covers how the three fit together for a team shipping an agent inside a paid product.
FAQs
Embedded analytics software: frequently asked questions
Common questions from teams shortlisting an embedding platform before realizing the agent-specific metrics are a separate build.
What is embedded analytics software, exactly?
Embedded analytics software is a platform you license so you can put charts, dashboards, and reports inside your own product instead of sending customers to a separate tool. Luzmo, Qrvey, AWS QuickSight, ThoughtSpot, and Tableau all sell a version of this: you connect your data, design a dashboard once, and the platform renders a scoped copy of it inside your app for each of your own customers, usually behind row-level security so one customer never sees another's numbers. What it does not do is decide which numbers belong on that dashboard. That data model, the actual metrics worth showing, is still yours to define.
How much does embedded analytics software actually cost?
It varies more than most shortlists admit. Luzmo dropped its old tiered pricing for a single plan called Embedded Everywhere, currently €1,995 a month billed annually, which includes 500 AI conversations and scales further on either monthly active users or additional AI conversations. AWS QuickSight prices embedded reader sessions separately from its own dashboards: $250 a month covers 500 embedded sessions on the monthly plan, with each session worth thirty minutes of access, and volume discounts on annual commitments bring the per-session cost down as you scale. ThoughtSpot's embedded Developer plan is free for the first year, capped at ten users and twenty five million rows, but its Enterprise embedded tier, the one with production SLAs, is quoted through sales only, the same as Qrvey, Domo, Sisense, and Tableau's own embedded offering. None of the big enterprise names publish a real number.
Do I still need to build anything after I pick a platform?
Yes, and this is the part every comparison guide skips past. An embedded analytics platform gives you a rendering and permissions layer: connect a data source, drag together a chart, wrap it in row-level security, done. It has no idea what a deflection rate is, what counts as a resolved conversation for your specific agent, or how to turn a raw trace event into a dollar figure your customer would recognize as their return on what they are paying you. You still have to define those metrics, calculate them from your agent's actual event stream, and pipe the result into whichever platform you embedded. The platform solves where the dashboard lives. It does not solve what belongs on it.
Are the "AI agents" inside these tools the same thing as monitoring my own AI agent?
No, and mixing the two up is an easy mistake to make while shopping. Qrvey's latest release is explicitly framed as bringing AI agents to embedded analytics, meaning a built-in assistant, marketed as Qrvey Sidekick, that helps a user build a chart or explore data by asking a question in plain language. Luzmo has the same idea under the name Luzmo IQ, and ThoughtSpot calls its version Spotter. All three are AI acting inside the analytics tool, helping someone build or query a dashboard faster. None of them trace, score, or report on a separate AI agent that your own product runs for your own customers. If what you actually want is visibility into what your customer-facing agent did, an AI-assisted query builder inside a BI tool is not that, no matter how the vendor's homepage phrases it.
Why not just embed one of these and build the agent metrics myself?
Plenty of teams do exactly that, and it works, eventually. The real cost is the months it takes an engineering team to define deflection rate and cost per resolution correctly, wire an ingestion pipeline from LangChain, LlamaIndex, or CrewAI events into the embedded platform's data source, and get the row-level security right so one customer's agent data never leaks into another customer's view. AiAgRe's Node SDK reads the same underlying agent events any of these platforms would eventually need fed to them, tags each one with an organization identity and a customer identity at ingestion, and ships white-labeled dashboard components with deflection rate, cost per resolution, and resolution rate already defined and already isolated per tenant. It replaces the build step, not the embedding idea itself.
Can I run AiAgRe alongside an embedded analytics platform I already bought?
Yes, and most teams that already committed to Luzmo, Qrvey, or QuickSight for their general product analytics keep it running for that job. General product usage, billing dashboards, and feature adoption charts are a good fit for a general embedding platform. AI agent performance shown to the specific customer whose traffic produced it is a narrower, harder problem, closer to what an agent monitoring tool already tracks for your own team than to a generic BI dashboard, just scoped outward to a paying customer instead of inward to an engineer. Running both together is normal, and neither one replaces the other.
Which embedded analytics platform should I pick if I still want a general one?
Pick Luzmo or Qrvey if white-labeling and multi-tenant SaaS embedding is the whole point, since both were built for that from the start rather than bolted onto an internal BI tool later. Pick AWS QuickSight if you are already deep in the AWS ecosystem and want session-based pricing that scales down for a small embedded footprint. Pick ThoughtSpot's free Developer tier if you want to prototype an embedded dashboard before committing budget, then expect a sales conversation once you need the production tier. None of the three, or any other platform in the category, ship the agent-specific metric layer this page keeps coming back to. That part stays a separate decision regardless of which one wins your shortlist, and the full embedded analytics tools shortlist covers the rest of the category beyond these four.
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