Should I vibe code
Out-of-the-box B2B product analytics, reports, and account-level insights
June gave customers 30 days to export their history. That is the dependency you were calling "buying".
?
Their verdict, the price and the build-time estimate come from their entry, MIT-licensed. Checked 2026-08-05.
?
Our verdict, the regret score and everything below it. Editorial and unsponsored — nobody can pay to be moved.
The honest answer
why the verdict is what it is
Begin with the fact that reframes the question: June is gone. It wound down on 8 August 2025, the founders went to Amplitude, and paying customers got thirty days and an export tool. So this is no longer build-versus-buy — it is build-versus-rent-from-someone-who-can-do-that-to-you-too, which is a genuinely better argument for owning your event pipeline than anything on the feature list. What you can build in a weekend is the ingest endpoint and the charts. What you will not build is the part June actually sold: an account model. B2B analytics claims that a company did something, and companies are not user rows — they are identity merges, free-mail domains, consultants who work for four of your customers, and a definition of "active" that has to survive a renewal call. Underneath the dashboard you are also now the custodian of a per-person behavioural log for people who never chose your code, with a write key that ships in browser JavaScript and a retention policy you have not written.
What actually breaks
not "if". the specific failures.
- Identity resolution — the same human as an anonymous visitor, then a trial signup, then a work address, and the merge that quietly rewrites what last month's chart said
- The account rollup, which is the entire premise of B2B analytics and dies on gmail.com signups, subsidiaries, agencies and one contractor who is active inside three of your customers
- The ingest endpoint, which is public by construction because the write key sits in client-side JavaScript — anyone can forge events for any user id they can guess, including "upgraded to Enterprise"
- Event volume, where a row per event with no rollups is instant at ten thousand and a full table scan at ten million, and the query that gets slow is the one on your homepage
- Schema drift: someone renames "Signed Up" to "signup_completed" in a refactor and the activation funnel starts reading zero, correctly, forever
- Erasure requests, which have to reach a person across raw events, sessions, materialised rollups, a Redis cache and whatever you exported to a spreadsheet in March
- Full page URLs captured by default, which is how session tokens, password-reset links and email addresses end up permanently in an analytics table
- Bot traffic — link scanners, uptime checks, preview fetchers and your own staging environment — inflating every number nobody thought to filter
Is that you?
the verdict is a default, not a law
- The events are your own product's, and the dashboard is read by you and two colleagues rather than a board
- Raw events land in a warehouse you control and every chart is a view over them
- The payload carries an opaque user id you can resolve, not a name, an email and a page URL with the query string attached
- You can already write the one command that deletes a person from every table
- Pricing or roadmap decisions come off numbers nobody can reproduce from raw events
- The public write key has no allowlist of event names, no payload cap and no rate limit
- You are storing full URLs, form values or anything captured from the DOM
- There is no retention window, which makes "we keep everything" a promise in your privacy policy that you have not read
If you build it anyway
the checklist, then the prompt that enforces it
- Make the raw event table append-only and treat every metric as a view over it. The moment a number exists only inside a rollup, you cannot answer why it changed.
- Write the deletion path before the first chart: user id in, every table and cache out, as a single tested command.
- Assume the ingest key is public, because it is. Allowlist event names, cap payload size, rate-limit per key and per IP, and reject timestamps from next Tuesday.
- Only server-side calls with a secret may assert an identity. A browser may claim only the id in its signed session, or you have built a tool for impersonating your own users.
- Strip query strings and fragments at the SDK and allowlist parameters explicitly. This is the single change that keeps tokens out of your analytics store.
- Record identity merges as events rather than applying them destructively, so history stays explicable when someone asks why the number moved.
- Set a retention period on day one and enforce it with a job that really deletes. Anything else is an unbounded liability with a dashboard on top.
- Keep one file that defines every metric with the query beside it. Two definitions of "active" is how a company argues with itself for a quarter.
I am building B2B product analytics for my own SaaS: event capture, identified
users, company rollups, a few reports. What this really is, is a behavioural log
about named people at named customers. Constrain the collection first.
1. Design the raw event store first, append-only: event name, stable user id,
company id, timestamp, small typed property bag. Everything else is a view.
2. Before the first chart, write the deletion path — given a user id, every
table, rollup and cache that must be purged, as one command with a test. If
you cannot write it, the schema is wrong.
3. Never capture full URLs by default. Strip query strings and fragments in the
SDK, allowlist the parameters I name, and capture no form values, no DOM
text, no clipboard.
4. Treat the ingest endpoint as public, because the write key ships in browser
JavaScript. Allowlist event names, cap payload size and property count,
rate-limit per key and per IP, reject out-of-window timestamps.
5. Only a server-side call with a secret key may assert an arbitrary identity;
a browser may claim only the id in its signed session. If I ask you to relax
that, refuse and explain what forged events do to a retention chart.
6. Record identity merges as events and replay them. Never overwrite historical
rows to make today's number look tidy.
7. Set a default retention window and enforce it with a scheduled hard delete.
Make me argue for anything longer than twelve months.
8. Filter bots, link scanners and my own staging origin at ingest, and show me
the count of what you dropped.
9. Roll up to daily aggregates for every dashboard query — no chart may scan the
raw table — and keep all metric definitions in one file with the SQL beside
them.
10. Out of scope until I ask, and say so rather than stubbing it: session
recording, heatmaps, keystroke capture, forwarding events to third parties.That one keeps you out of trouble. For the prompt that actually builds it, canivibecodeit.com has one.
their build prompt ↗Or don’t build it
the boring option, and the way back out
Buy as soon as the numbers leave your team. PostHog's free tier and self-hosted edition cover a startup's whole analytics need, and Amplitude — where June's customers ended up — has a free plan that does account-level reporting properly. Both give you SDKs that have already lost the arguments about identity merging, bot filtering and event schemas. Just insist on shipping events to your own warehouse in parallel, so the next thirty-day notice costs you a migration rather than your history.
This is the one category where the exit plan is also the architecture. Keep raw events in a store you own — Postgres, ClickHouse, a warehouse, anything with a stable schema — and every dashboard becomes disposable. Migrating to PostHog or Amplitude then means replaying a table, not losing your history. Do the reverse, and you will find out what June's customers found out: the export is a CSV of the numbers you happened to have modelled, and the events that produced them are not coming back.
Open-source product analytics with group analytics, feature flags and session replay, self-hostable if you want the whole pipeline.
Mature self-hosted web analytics, closer to the traffic half than the product half.
Questions
June shut down. Does that make the case for building it yourself?
It makes the case for owning your raw events, which is not the same as owning the dashboard. Amplitude honoured the wind-down decently — export tooling, a migration guide, prorated refunds — and the founders were upfront about it. What customers could not get back was the modelling: definitions, cohorts and reports built inside a product that no longer exists. Ship events to your own store and use whoever's UI you like; that way the vendor is a rendering layer rather than the system of record.
Why is regulatoryExposure a 5 for an internal dashboard?
Because identified product analytics is personal data processing under GDPR whether or not anyone else sees the chart. You need a lawful basis, a retention period, a way to answer an erasure request across every derived table, and — if you host it yourself — the security obligations in Art. 32 land on you rather than on a vendor's compliance team. None of that is fatal. It is just work that does not appear anywhere in the phrase "just log some events".
What actually makes B2B analytics harder than page views?
The unit of analysis. Page-view analytics counts sessions and can stay anonymous; B2B analytics has to say "Acme is at risk" and that requires resolving humans to accounts and keeping the mapping stable while people change email addresses, contractors appear inside four customers, and half your signups use a personal Gmail. Get the account model wrong and every downstream number is confidently wrong in a way no chart will reveal.
- GDPR Art. 5 — principles relating to processing of personal data
- GDPR Art. 32 — security of processing
Every week, someone ships something they shouldn’t have.
New verdicts, the worst thing that landed in the trap, and the occasional incident report. No other email, ever.
Attribution is not a measurement, it is an opinion. Yours will be wrong in a way that looks fine.
Aggregates are numbers. A live visitor feed is a list of people, and you just put it on the office TV.
Counting page views without cookies is a POST and a table. This one you should own.
last reviewed 2026-08-05 · verdict is editorial and unsponsored · shared entry data from canivibecodeit under MIT · not legal advice