LIVE REAL-REPO RUN — ZERO-FINDING DEMO · Ran the $149 Datadog Cost Audit analyzer against github.com/DataDog/dd-trace-py (Datadog's own Python APM tracer — 948 files scanned, 942 contained the datadog / ddtrace client import). 0 findings · $0/mo — the engine correctly reported zero cost-leak patterns. The team that designed Datadog's billing SKUs does not fall into their own product's cost traps; the analyzer correctly does not fabricate $/mo savings to justify the audit fee. Compare with the DataDog/terraform-provider-datadog demo (1 LOW finding, $100/mo on 201 .tf files — one missing-metadata governance smell on an example file). Two real Datadog-authored OSS repos, same deterministic engine, honest near-zero output. Order your own $149 audit → Every order includes a 30-day re-audit voucher — ship the fixes, then re-run free to validate.
0 ranked Datadog cost-leak findings across
948 relevant file(s)
(942 client-library source files,
0 Datadog YAML configs,
0 Terraform Datadog-provider file(s)).
Implementing the top 0 could save approximately
$0/month —
$0/year.
RECURRING Datadog billing savings verifiable in
your Datadog "Plan & Usage" page next billing cycle. Filter:
https://app.datadoghq.com/billing/usage
— look for custom_metrics_count (Patterns 1-2), indexed_logs_gb (Patterns 3-4),
apm_indexed_spans (Pattern 5), and synthetics_browser_test_runs / synthetics_api_test_runs
(Patterns 6-7). Each finding's dollar claim maps to a specific Plan & Usage SKU line item.
All savings estimates use conservative confidence ratings (0.55-0.90).
#
Opportunity
Severity
$/mo saved
No Datadog cost leaks detected by v1 rules. See notes below for context.
TOTAL ESTIMATED MONTHLY SAVINGS: $0
No Datadog cost leaks detected
The v1 rule set found no Datadog cost-optimization opportunities in this repo. This either means the configuration is already cost-optimized OR uses patterns the v1 rules don't catch yet (e.g., custom tag-cardinality limits via the Agent API, or programmatic sampling controls). Per the refund policy: if no findings means no actionable value for you, full refund — email miloantaeus@gmail.com.
How Datadog billing works (and how to verify these savings)
Datadog charges by SKU, each with its own per-unit price and quota structure. The most
common cost drivers (and what this audit targets):
Custom metrics: $0.05/series/month after the per-host quota (100 series/host on Pro).
This is the SKU most exploded by Patterns 1 + 2. A single metric with a high-cardinality tag
like user_id across 10K users = 10,000 billable series = $500/month for one metric.
Distributions auto-generate 5 percentile sub-metrics (p50/p75/p90/p95/p99); high-arity tags fan
this out multiplicatively.
Indexed logs: $1.27 to $3.75 per million events, depending on retention (15-day to
1-year). This is what Patterns 3 + 4 target. A typical app emits 50-500GB/day of logs;
exclusion filters can drop 90%+ of that before it hits the index.
Ingested logs (scan): $0.10/GB scanned. Less expensive than indexed but still
material at high volume. Pattern 4 (debug log level) inflates this 3-10x.
APM indexed spans: $1.27-$1.70 per million spans. Pattern 5 (DD_TRACE_SAMPLE_RATE=1.0)
sets this on fire for high-RPS services — 1000 RPS at 100% sampling = ~2.6B spans/month
= $3-4K/month per service.
Synthetics: $7.20 per 10,000 browser test runs, $5.00 per 10,000 API test runs.
Patterns 6 + 7 target sub-5-minute cadences and 5+ location fan-out.
Hosts: $15-$31/host/mo depending on tier. Not directly targeted by this audit but
worth knowing — many of the patterns above were originally cost-optimized to fit within
the host-quota model.
To verify any finding's savings claim, open
https://app.datadoghq.com/billing/usage,
filter by date range (a 30-day window before-and-after each fix is ideal), and watch the
relevant SKU line item drop. Custom-metrics fixes show up immediately on next-day usage
graphs; log/APM fixes show up over a 24-72 hour window as agent restarts propagate.
30-day re-audit voucher
Included with your $149 audit: a voucher for a free re-audit 30 days after delivery.
Implement the recommended Datadog config + code changes, then re-submit the same repo URL via
reply email — we re-run the analysis and confirm the cost-leak patterns are resolved. If
we still flag any of the CRITICAL findings from this report, refund issued automatically.
Why this matters: Datadog savings only materialize once the code/config changes land in
production AND the agent restarts pick them up. The re-audit voucher creates an accountability
loop — we can't claim "issue resolved" unless the v1 ruleset agrees on re-scan. Same
deterministic engine, same file paths, same line numbers. No moving goalposts.
Verification path for customers: after applying changes, watch the relevant SKUs at
https://app.datadoghq.com/billing/usage
over a 7-30 day window. Custom-metric counts drop within hours of agent restart;
log-exclusion savings appear within 24-72 hours as the new rules propagate; APM-sampling
savings show on the next ingestion summary (usually 4-6 hours). We can supply the exact
Plan & Usage filter for each finding on request.