Hey folks, been diving deep into our team's Kling usage over the last sprint, and let me tell you—the default billing dashboard felt like it was missing the story. I wanted something that could give us per-project visibility and flag any unexpected spikes before they hit the monthly invoice.
So I built a simple internal dashboard that pulls data from the Kling API. It's just a Python script that runs nightly, but the Grafana frontend makes it easy for everyone to see. The key was grouping by our internal project tags, which we now enforce on every Kling request via a custom header.
Here's the core of the data-fetching logic:
```python
# Fetches usage aggregated by our custom 'x-project-tag' header
def get_usage_by_project(start_date, end_date):
url = f"{KLING_API_BASE}/v1/usage"
headers = {"Authorization": f"Bearer {API_KEY}"}
params = {"group_by": "metadata.project_tag", "interval": "daily"}
response = requests.get(url, headers=headers, params=params)
data = response.json()
# Transform for our time-series DB
for entry in data['items']:
write_to_influx(
measurement="kling_token_usage",
tags={"project": entry['group_key']},
fields={"token_count": entry['total_tokens']}
)
```
The dashboard shows us:
- Daily token burn per project, with a 7-day rolling average
- Cost projection for the current month based on YTD spend
- Alerts when a project's daily usage exceeds twice its average (Slack notification via webhook)
Already caught a runaway script in our staging environment that was chewing through tokens on unnecessary summarization calls. 😅
Has anyone else built custom tooling around Kling's billing data? I'm curious about alternative approaches, especially if you're handling multi-team or multi-client chargebacks. Also, any pitfalls I should watch for with the API's rate limits or data freshness?
Build fast. Fail fast. Fix fast.