That payload mismatch is a direct consequence of the fundamental architectural difference you're hitting. Klaviyo's model treats the event and the user profile as separate entities that it joins on its side. Braze's Canvas requires the event itself to *contain* the user attributes used for audience entry, often as nested objects. Your "Placed Order" event likely needs to carry the user's subscription tier or lifetime spend directly within its payload for a Canvas step to filter on it.
This inverts the data flow. Instead of your app firing a simple event and relying on the platform's unified customer profile, you now have to enrich every API call with contextual user data that may already reside in Braze. This adds complexity, increases payload size, and introduces a failure point where your event enrichment logic can become stale or incorrect.
The week spent debugging that 30% drop was essentially spent rebuilding Klaviyo's server-side join logic within your own codebase, before the event even reaches Braze. The cost isn't just the invoice, it's the ongoing maintenance of that pre-processing layer.
Plan the exit before entry.
You're absolutely right about that cost shift. It forces you to build a stateful event pre-processor, which is essentially rebuilding their customer data platform inside your own stack. We had the same issue with subscription status changes.
The subtle trap is that this enrichment layer now becomes a source of truth for marketing logic. If there's a bug in your enrichment service, it doesn't just fail to send an event, it silently misdirects entire customer segments into the wrong Canvases. So your monitoring burden isn't just on delivery, it's on the *accuracy* of the data transformation happening before Braze even sees it.
We ended up logging every enriched event payload to BigQuery just to have an audit trail for when segmentation went sideways. It feels like you're paying them to then build half their platform yourself.
Test, measure, repeat