Observability Metrics Retention Trimmer
$9,320.00 in monthly observability bill reductions identified across across 3 items.
User UUIDs appended to metric tags resulted in 1.25M distinct time-series ($5,200.00). Stripping tag recovers $4,160.00/mo (80%).
Method1Audited metric tag dictionaries for high-cardinality UUID and IP address patterns.
2Generated Datadog agent filter YAML to drop unindexed telemetry at the collection edge.
| Reference | Description | Monthly_Savings |
|---|---|---|
| api-checkout-service | Unbounded UUID Cardinality (Checkout Service) | $4,160.00 |
| batch-worker-daemon | Unfiltered Debug Logging (Batch Worker) | $3,240.00 |
| payment-service | Client IP Tag Cardinality (Payment Service) | $1,920.00 |
Datadog usage attribution CSV and custom metrics cardinality reports, plus logging indexing exclusion rules
Metric exclusion filter configuration and estimated monthly telemetry bill reduction (Resolution Dossier, Findings Schedule)
Run Observability Metrics Retention Trimmer free on the last 12 months of files. If it finds money, the fee is 15 percent of what is actually recovered, nothing on what is not. After that, ongoing monitoring is $1,250 a month. 30% goes to the referring partner.
Read in memory for the session, never stored, never used to train a model.
Outputs are computed from your inputs and the tool's rules. Check them before you rely on them.
Free scan. Pay only on cash recovered.
Run Observability Metrics Retention Trimmer free on the last 12 months of files. If it finds money, the fee is 15 percent of what is actually recovered, nothing on what is not. After that, ongoing monitoring is $1,250 a month.
On a sample file: $9,320.00 in monthly observability bill reductions identified across across 3 items.
Why not just use ChatGPT?
Rules that stay current: the rate cards, tariffs, code sets and regulations it checks against are kept up to date for you; a one-off prompt starts from nothing each time. Evidence that stands up: every finding cites the line, the file and the rule, so it holds up with a vendor, a payer or an auditor. The same answer every time: the checks are written rules, not a fresh guess, so this month's result can be compared with last month's. Nobody has to own it: no one inside has to build, test and maintain a home-made tool, and files are not pasted into a public chatbot.
Get a free scan
If you would rather talk it through, write to dev.sathya@baseloom.app; that reaches Dev Sathya, founder.