Winner of the Embedded Analytics Solution of the Year at the Data Breakthrough Awards 2026
Yes - faster than ever. A team with good agents can scaffold a customer-facing dashboard in an afternoon. Which is exactly how the expensive lesson starts: the demo was never the hard part.
Charting libraries are excellent. Agents write the glue code. A working prototype lands inside a sprint - and it genuinely works.
Nearly every team that comes to us having tried the in-house route tells the same story, and it starts with a demo everyone loved. A chart, a real query behind it, live customer data on screen. Two weeks, maybe three. The build-vs-buy meeting ends there - because the remaining 80% is invisible from week two.
But a chart with a query behind it is not what your customers asked for. They asked for analytics in the product: numbers they can bet their own decisions on, dashboards that load fast at 9am, views that respect who’s allowed to see what. Filters, exports and drill-downs that behave like the rest of your app. The ability to go further than the dashboard you gave them - change the date range, compare periods, save a view of their own. Increasingly, the ability to simply ask: a question in plain language, answered from their data. And all of it in every timezone, at every screen size, for every tenant.
That’s not a chart. That’s a production data system with a UI on top. AI has made the visible 20% of that system nearly free - which makes the other 80% easier than ever to underestimate.
Six systems every in-house build ends up owning. None of them are visible in week one. All of them arrive.
A bar chart is a weekend. A component library is ‘other’ grouping, colour assignment that stays stable across charts, timezones, translations, number formats, and empty, loading and error states - times every widget type you’ll ever ship.
Customer A must never see customer B’s rows - not in a chart, not in a filter dropdown, not in a cached result. That means row-level security, scoped tokens, and a permissions model you re-verify on every change. A miss here isn’t a bug ticket; it’s a disclosure.
Customer A wants different columns. Customer B wants different metrics. Customer C wants a chart hidden entirely. Add saved views, defaults and versioning, and “a dashboard” quietly becomes a configuration system - with migrations you own forever.
One dashboard is one query. Hundreds of tenants refreshing at 9am is caching, pre-aggregation and connection pooling - against production databases you must not slow down. Fast in the demo is free. Fast at the 95th percentile, in month twelve, is infrastructure.
When numbers drive customer decisions, “why does this say 47?” is an escalation. You need metric definitions that agree on every surface, and audit logs and lineage that answer who saw what, what changed, and whether it was allowed.
The requests never stop: change the date range, add a filter, build a custom view, export it. Each one is reasonable. Together they’re a governed self-serve product bolted onto your roadmap - flexibility for users, control for you.
We’ve written up the component iceberg in detail: the complexity teardown and the long-tail feature list.
An in-house analytics build is never finished. Libraries deprecate. Browsers change. Schemas evolve. Every per-customer request lands in your backlog, and every change has to ship without breaking the saved dashboards paying customers already rely on. When the engineer who built it moves on, the system stays.
The real cost was never the first build. It’s the roadmap you don’t ship while maintaining it.
An honest split, from a team that builds with agents daily.
Agents genuinely collapse the cost of code you own. Scaffolding components, wiring a data model, iterating on layout - work that took weeks now takes days, sometimes an afternoon. If you’re building on the right foundation, that’s a real, compounding advantage.
What agents don’t collapse is the cost of what you operate. Everything in the list above is an operational liability, not a code-generation problem. An agent can write a caching layer in minutes - but you’re the one on call for it. An agent can write row-level-security rules - but if they’re subtly wrong, the failure mode isn’t a bug, it’s a breach.
And agents add a requirement of their own. Once agents are changing configuration and querying customer data, “what happened, and was it allowed?” becomes a question your board asks. Auditability stops being a nice-to-have precisely when you start automating.
AI didn’t settle build vs buy. It moved the line - and raised the price of owning the wrong side of it.
Embeddable is built for teams who want full control of the product experience - without rebuilding the analytics system under it.
It embeds in one tag - a web component, native to your app, not an iframe. And speed to market isn’t the trade-off: your agents work in your repo, with your review process, just as fast as building it yourself - except everything underneath already works. The demo still lands in an afternoon. Production you never have to own alone.
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