These are illustrative scenarios showing how the products are used, not customer case studies.
The same sequence, twice
Today
- One dataset dominates writes
- Scale the whole database
- Or migrate its storage
- Decision deferred again
With SynaptaGrid
- Pin that schema alone
- Its own provisioned database
- API calls unchanged
- Rest of the model untouched
The situation
Scaling the whole database to keep one demanding part comfortable is expensive, and separating it out means a storage migration plus a change to every client that reads it. So the decision keeps being deferred, and the tuning gets more specific to that one table each quarter.
How it is approached
Each schema’s tables live in their own namespace, and a schema can be pinned to its own provisioned database when it needs capacity of its own — from a dedicated logical database up to a dedicated cluster. How the APIs are called does not change. If that database cannot be reached the request fails rather than quietly falling back to the main one, and low-priority volume runs in its own work pool so it cannot starve urgent work.
What changes
The demanding part of the model gets capacity without moving the rest, and misbehaves within a smaller radius when it does. Storage layout stops being a reason to redesign.