Hi,
Nocode full stack platforms like AppSheet, bubble and glideapps recurring costs are variable as product scales often times becoming unpredictable(specially in case of bubble) Bubble has the Workload Units (WU)system where your base monthly plan includes a quota and beyond that you are charged for more via higher plans or buying more workload units. Similarly in glideapps there are ‘updates’ charged for every CRUD operation if you are using a google sheet for database and if you use glide tables then certain workflows and 3rd party integrations cost consume ‘updates’ which are again allocated per plan with a certain quota and beyond that it’s 0.02$ per update which can again pile up as a mildly complex app starts scaling. When you compare the above costing to the one in a split stack solution like Weweb+supabase or flutter flow + supabase, what is the equivalent of glideapps updates in these split stack solutions? I’m guessing because there’s less technical debt here these ‘updates cost’ or ‘WU’ cost would be significantly less as app scales ? Please share your thoughts/guide on this?
The user is comparing the pricing models of no-code platforms like Bubble and Glideapps, which use workload units and updates, respectively, with split-stack solutions like WeWeb + Supabase or FlutterFlow + Supabase. They are seeking information on the equivalent cost metrics in split-stack solutions and whether these setups offer more predictable and lower scaling costs.
The user is comparing pricing/scaling models of all-in-one no-code platforms (Bubble, Glide, AppSheet) versus split-stack setups (like WeWeb + Supabase or FlutterFlow + Supabase).
They explain how: Bubble uses Workload Units (WU) Glide uses “updates” (each CRUD operation, workflows, integrations, etc.) These costs can become unpredictable and expensive as apps scale.
They are asking: What is the equivalent of “updates” or WU in split-stack tools like Supabase setups? How are costs measured there (database reads/writes, storage, auth, functions, bandwidth, etc.)?
Whether split stacks generally have lower and more predictable scaling costs due to less platform overhead. They want guidance and opinions on how scaling economics compare between these two approaches.