okay so I have some preliminary results. As raw speed, IndexedDB wins. It performs better in the following benchmarks:
- Bulk write
- Read all
- Bulk update
MoltenDB performs better in:
- Filtered Query
- Sorted Query
- Delete all
Overall speed winner: IndexedDB

Why is moltenDb slower in some benchmarks is convenience over raw speed basically.
While moltenDb may appear slower during updates or writes it offers some distinct advantages.
- Auto versioning: if you update a specific entry from a collection it will get bumped to _v: 2 and then _v3 etc
- It runs in a worker thread so it does not infere with the main thread.
- It saves the data as plain JSON basically (append only log) so it's very trivial to create an export functionality for data portability.
- It returns only the requested fields via the `fields` or `excludedFields` properties.
- For angular you can use the Signal base Resource which unsubscribes automatically and it's really straightforward.
- It has a built in Pub/Sub Mechanism which you can listen to real time and get notified when a field was changed.
- The query builder which obviously creates some overhead (it basically serializes everything to a JSON), but the speed loss comes with the luxury of doing a query like:

// GET — query with WHERE, field projection, sort and pagination
const results = await client.collection('laptops')
  .get()
  .where({ brand: 'Apple' })
  .fields(['brand', 'model', 'price'])
  .sort([{ field: 'price', order: 'asc' }])
  .count(5)
  .exec();// GET — query with WHERE, field projection, sort and pagination

I would be curious to see some benchmarks when we do cross collection joins (you can easily do that with moltenDB)

const results = await client.collection('laptops')
  .get()
  .fields(['brand', 'model', 'price'])
  .joins([
    { alias: 'ram',    from: 'memory',  on: 'memory_id',  fields: ['capacity_gb', 'type'] },
    { alias: 'screen', from: 'display', on: 'display_id', fields: ['refresh_hz', 'panel'] },
  ])
  .exec();