Anyone else find the iboss query builder UI painfully slow for ad-hoc exploration?

I've been conducting a security audit that requires correlating threat events with specific user activity over the last quarter. Naturally, this involves heavy use of the iboss query builder for ad-hoc log exploration. While the platform's underlying data is robust, the UI's performance during iterative query building has become a significant bottleneck in my workflow.

The primary pain points I'm encountering include:

* **Latency after each UI interaction:** Adding a filter, changing an operator, or even toggling a field selection often triggers a 3-5 second delay before the interface is responsive again. This destroys the flow state needed for exploratory analysis.
* **Slow field/value dropdown population:** Clicking "Select Value" for a common field like `user.email` can take upwards of 10 seconds to populate the list of distinct values, even for a constrained time window.
* **Lack of a "raw query" or advanced editor mode:** The step-by-step UI is great for beginners, but for power users, not being able to directly write or paste a structured query (like a JSON or SQL-like snippet) feels restrictive. Every adjustment requires waiting for the slow UI.

I've resorted to using the API directly for bulk exports, which is performant, but that defeats the purpose of *ad-hoc* exploration. The workflow now looks like: formulate hypothesis in UI, get frustrated by lag, script a one-off API call, parse JSON, repeat. It's inefficient.

My question is twofold:

1. **Are others experiencing this, or is it potentially a configuration/scale issue on our deployment?** We have a substantial log volume, but the UI slowness seems disconnected from the actual query execution time.
2. **Has anyone developed any workarounds or external tools to bridge this gap?** I'm considering building a simple local web app that uses the iboss API to mimic a more responsive query builder, perhaps with query templating and saved fragments.

For example, a programmatic approach via their API is snappy:
```python
# Example of a direct API call - this executes and returns far faster than building the equivalent in the UI.
import requests

query_params = {
"query": {
"timeRange": {"from": "2024-01-01T00:00:00Z", "to": "2024-04-01T00:00:00Z"},
"filters": [
{"field": "event.type", "operator": "eq", "value": "threat_detected"},
{"field": "user.department", "operator": "eq", "value": "engineering"}
]
}
}
# Actual call would use your auth token and endpoint
response = requests.post('https://.iboss.com/api/v1/query/logs', json=query_params, headers=auth_headers)
```
This stark contrast in performance between the UI and the API layer is what's so puzzling and frustrating.

Is the community just accepting this as the trade-off for the visual builder, or are there strategies to mitigate it? I'm leaning towards building a custom connector layer for my team's use if this is a universal experience.

API first.