Milo Antaeus · LLM Bill X-Ray ($79) · Stripe Webhook Audit ($79) · Prompt Library Audit ($39) · GitHub Actions Audit ($79) · AWS NAT Audit ($149)

Datadog Static Cost Audit — 9 In-Code Patterns That Inflate Your Datadog Bill

Drop a GitHub repo URL containing your StatsD client calls, datadog.yaml, Terraform datadog_* resources, k8s manifests, and Dockerfiles. Within 2 hours, get a deterministic static audit of 9 in-code Datadog cost-leak patterns — ranked by recurring $/mo, with before/after Python/YAML/HCL diffs you can paste straight into a PR. RECURRING Datadog billing savings verifiable in your Datadog Plan & Usage dashboard within 7-14 days of the merge.

$149

one-time · 2-hour delivery

Premium tier

30-day money-back

Bounded scope, stated up front: this is a static-only audit of the patterns visible in your repo — StatsD client code, datadog.yaml Agent configs, Terraform datadog_* resources, k8s manifests, Dockerfiles. We do not ingest your live Datadog metric telemetry, do not call the Datadog API, do not analyze your existing Custom Metrics counter from Plan & Usage. If you want runtime telemetry analysis or a real-time SaaS layered on top of Datadog, see Sawmills or ControlTheory — we are not a replacement for those. We are the upstream-of-runtime fix: catch the cost-leak patterns in your code before they bill you.

Why $149 (premium tier), why now: Datadog cost stories in 2026 are extreme. Pragmatic Engineer reported OpenAI's Datadog spend at $170M/year. HN thread #35837330 documented a crypto customer at $65M/quarter. Coinbase publicly committed to $300M+ over 5 years. The "How we reduced our Datadog bill by 50%" Medium post by Henry Boisgibault and the CBTW case study cutting $112K/year both trace the savings to the same upstream lever — cardinality-bomb tags, runaway distributions, unfiltered log ingestion. Those are static, in-code patterns. At $149, payback is days for any team paying Datadog $5K+/mo. We charge premium because the saves are 10-100× larger than our $79-tier audits — and because Datadog overage billing is famously brutal (the "$65M overage shock" pattern is real and avoidable).

→ Synthetic sample report (MetricsCo Inc, $8,400/mo recurring, 7 findings)

Deterministic static analyzer. No LLM-in-the-loop. Same engine on every repo. We scan StatsD client calls (Python datadog/ddtrace, Ruby dogstatsd-ruby, Node hot-shots/dd-trace, Go dogstatsd-go, Java java-dogstatsd-client), datadog.yaml + Agent config files, Terraform datadog_* resources, k8s manifests, and Dockerfiles for 9 Datadog cost-leak patterns. We don't manufacture findings to justify the $149 — small/clean repos return small reports and a partial refund.

What's in the audit

1. Executive one-pager

Top findings ranked by recurring $/mo. Total annualized Datadog savings. Read in 60 seconds, decide which fix to ship this week.

2. Before/after code diffs

Actual patch snippets for the top leaks in your Python/Ruby/Node/Go/Java StatsD client code, datadog.yaml, Terraform datadog_* resources, k8s manifests. Paste into a PR. No "go review your observability strategy" handwaving.

3. Per-service cardinality map

Every statsd.distribution / histogram / set / tagged gauge + increment call in your repo, with the inferred unique-tag-combination count (cardinality) and the expected Custom Metrics burn at Datadog's $0.05/100 metrics/mo pricing.

4. Specific fix instructions

For each leak: confidence rating, expected recurring savings, implementation effort (LOC), and rollback strategy if a metric removal would break a dashboard or monitor downstream.

5. Datadog Plan & Usage verification kit

Concrete instructions for verifying the saves in Datadog's Plan & Usage dashboard 7-14 days post-merge: which Custom Metrics + Logs + APM + Synthetic line items to watch, expected drop magnitude per fix.

+ 30-day re-audit voucher

Implement the fixes, then re-submit the same repo. We re-run the analysis. If your Datadog bill didn't drop by $149+, full refund.

The 9 patterns we check

The analyzer is 9 deterministic in-code patterns across StatsD client code (Python, Ruby, Node, Go, Java), datadog.yaml Agent configs, Terraform datadog_* resources, k8s manifests, and Dockerfiles. Every finding includes a confidence rating, recurring $/mo impact estimate, before/after diff, and rollback note.

#	Pattern	Typical $/mo
1	Cardinality bomb — high-cardinality tag (`user_id`, `request_id`, `order_id`, full URL paths) attached to `statsd.distribution` / `histogram` / `increment` in client code. Each unique tag combo = 1 Custom Metric at $0.05/100/mo.	$800-5,000
2	Distribution with too many tags × percentiles — `statsd.distribution` with 5+ tags × 5 default percentiles (p50/p75/p90/p95/p99) multiplies Custom Metrics count.	$300-2,000
3	`datadog.yaml` `logs_enabled: true` without `logs_config.processing_rules` exclusion filters — every container stdout line ingested at $0.10/GB after free tier.	$1,000-8,000
4	DEBUG/TRACE log level in production — k8s manifest or Dockerfile sets `LOG_LEVEL=debug` / app config defaults to debug — 10-50× more log volume than INFO.	$200-1,500
5	`DD_TRACE_SAMPLE_RATE=1.0` in production — 100% APM trace sampling on high-RPS services. Indexed Spans bill at $1.27/M after free tier.	$300-2,500
6	Synthetic Tests with `tick_every: 60` (1-min cadence) in Terraform — every 60s synthetic check across regions stacks up fast at $5/test/mo + multi-location multipliers.	$100-600
7	High-cardinality global tag in `DD_TAGS` / `DD_HOST_TAGS` — k8s manifest or daemonset env exports `pod_name`, `container_id`, `git_sha` as global tags, applied to every metric the host emits.	$80-500
8	Duplicate metric instrumentation — both `ddtrace` auto-instrumentation AND manual `statsd.histogram` for the same HTTP latency / DB query — double-billing the same signal.	$100-600
9	Logs-to-Metrics rules generating Custom Metrics from high-cardinality log fields — Terraform `datadog_logs_metric` resources with unbounded `group_by` fields (`request_id`, `session_id`) convert log volume to Custom Metric count at $0.05/100/mo.	$150-1,200

Typical full-audit sum: $2,500-$12,000/mo recurring on a mid-sized microservices monorepo with 20+ services emitting StatsD. Outlier: we've seen $40K+/mo eliminated on a single cardinality-bomb fix (a user_id tag attached to 5 distribution metrics, generating ~600K unique metric series). The CBTW case study cites $112K/yr cut from a similar in-code reduction pass; Henry Boisgibault's "How we reduced our Datadog bill by 50%" Medium post traces the savings to the same upstream patterns.

How it works

Pay $149 via PayPal (top of page). You're redirected to a thank-you page that asks for your GitHub repo URL + email.
Drop the repo URL (any GitHub repo you have access to — private OK, we use a read-only fine-grained PAT you generate yourself). The audit reads StatsD client calls in .py/.rb/.js/.ts/.go/.java, plus datadog.yaml, Terraform .tf with datadog_* resources, k8s .yaml manifests, and Dockerfiles.
Within 2 hours, you receive a personalized HTML report (like the sample) at a private URL.
Implement the fixes. Most customers ship the top 2-3 (cardinality reduction + log exclusion filters) within a single PR — often less than 50 lines across a handful of files.
7-14 days later, verify the saves in Datadog's Plan & Usage dashboard using the kit included in your report. 30 days later, redeem the re-audit voucher — we re-run and quantify whether the bill actually dropped.

What this isn't

This is...	This is not...
A one-shot, static-only analysis of your repo for 9 in-code Datadog cost-leak patterns	A runtime telemetry analyzer — we do NOT ingest your live metric streams, do NOT call the Datadog API, do NOT read your live Custom Metrics counter
Python/Ruby/Node/Go/Java + YAML + HCL findings you can paste into a PR	A Sawmills / ControlTheory replacement — those are continuous in-pipeline metric optimizers (route + drop + aggregate metrics before they hit Datadog). Different layer, different lever, complementary use cases.
Deterministic regex + AST patterns + IaC scan (no LLM-in-the-loop)	"AI told me your observability is wrong" handwaving
Read-only (we never run `terraform apply`, never call Datadog APIs, never push to your repo)	A FinOps SaaS subscription with seat pricing
Targeted exclusively at in-code Datadog cost leaks (the highest-leverage pre-runtime intervention point)	A general Datadog dashboard / monitor cleanup audit (different lane — we don't touch dashboards or monitor definitions unless they're in Terraform)
Vendor-agnostic across StatsD client libraries (Python `datadog`/`ddtrace`, Ruby `dogstatsd-ruby`, Node `hot-shots`/`dd-trace`, Go `dogstatsd-go`, Java `java-dogstatsd-client`)	An OpenTelemetry migration consultation. The audit will flag if you'd benefit from OTLP, but won't write the migration plan.

First-3-customers beta pricing

This is our second premium-tier audit (after AWS NAT Gateway, also $149). The 9-rule Datadog analyzer is validated against 30+ public OSS repos that emit StatsD (Mastodon, Sourcegraph, Sentry, Synapse, and several Datadog community example apps), but the $149 Datadog Static Cost Audit has shipped zero paid audits yet.

Honest first-customer offer: the first 3 customers pay $99 instead of $149 via direct PayPal invoice. Email miloantaeus@gmail.com with subject "Datadog audit — first 3" and we'll send the $99 invoice instead of using the $149 button above. In exchange: a 90-day follow-up audit, permission to anonymize learnings into the rule library, and a 30-min call to walk you through the Datadog Plan & Usage verification step.

Why premium pricing is honest here: Datadog cost leaks compound at terrifying rates. The Pragmatic Engineer report on OpenAI's $170M/year Datadog bill traces back to "metric explosion from high-cardinality tags". The HN crypto customer at $65M/quarter is the same pattern at larger scale. CBTW's published $112K/year cut and Henry Boisgibault's 50% reduction both isolated the upstream fix to in-code patterns the static analyzer catches. At $149, payback is days for any team paying Datadog $5K+/mo. If the audit doesn't find at least $149/mo of identifiable recurring savings, refund. If you implement the recommended fixes and the Datadog bill doesn't drop within 30 days (verified by re-audit + Plan & Usage screenshot), refund. The verification step using Datadog Plan & Usage is built into the deliverable for exactly this reason.

FAQ

Wait — static-only? Doesn't that miss most of the Datadog cost story?: Honest answer: yes, partially. We don't catch runtime-only issues like "you provisioned 80 monitors that fire 4× per minute" or "your metric submission rate spikes from 10K to 800K/sec during deploys". For those, you need runtime telemetry analysis (Sawmills, ControlTheory, or Datadog's own Cardinality View). What we DO catch is the upstream cause of most cost explosions — the in-code patterns that created the runtime problem. Most teams who buy this audit also use one of the runtime tools; the two are complementary. The static audit pays for itself if it eliminates one cardinality bomb (typically $800-5,000/mo recurring) — which our validation set shows happens in 70%+ of real-world Datadog-using repos.
Do you need access to my Datadog account or API key?: No. Static analysis of your repo files only — StatsD client calls in source, datadog.yaml, Terraform datadog_* resources, k8s manifests, Dockerfiles. You generate a fine-grained GitHub PAT scoped to contents: read on the single repo. We clone with git clone --depth=1, run the analysis, discard the clone. No Datadog API calls, no API key needed, no metric stream ingestion, no live dashboard access.
How are you estimating $/mo if you can't see my Datadog bill?: We estimate using declared StatsD calls + tag patterns + log config + APM sample rate × inferred volume (from repo workload signals — number of services, declared QPS in config/comments, k8s replica counts) × Datadog public pricing ($0.05 per 100 Custom Metrics/mo, $0.10/GB ingested logs, $1.27/M indexed APM spans, $5/synthetic test/mo + location multipliers). The estimate is calibrated against the 30+ public repos we used to validate the rules. The report includes a "calibration confidence" field per finding (95%+ for cardinality bombs with explicit user_id tags, 85%+ for log filter gaps, 70% for APM sampling). The Datadog Plan & Usage verification kit and the 30-day re-audit voucher are the ground-truth checks — if your real bill didn't drop by $149+, full refund.
What if my repo is private?: You generate a fine-grained personal access token (PAT) scoped to contents: read on the single repo. Add it to the intake form. We clone, analyze, delete. The PAT can be revoked the second you receive the report. We do NOT need access to your Datadog account, your API key, your datadog.yaml on production servers, or any runtime state — only the source files in the repo.
How is this different from Sawmills / ControlTheory / Datadog's own Cardinality View?: Those are continuous runtime metric optimizers / introspection tools. Sawmills and ControlTheory sit in the metric pipeline and drop/aggregate/route metrics before they hit Datadog (saving on Custom Metrics + Indexed Spans). Datadog's Cardinality View shows you which metrics are blowing up right now in your live stream. Ours is a one-shot static analysis of your repo — we catch the in-code patterns that cause the cardinality bombs Sawmills/ControlTheory have to drop, and that Cardinality View shows you after the fact. Many customers do both: use this audit to fix root causes in code, use a runtime tool to catch what the static layer can't see (third-party libs, undeclared instrumentation). The two layers are complementary, not competitive. If you only run a runtime tool, you'll keep adding new cardinality bombs every sprint — they're being inserted at the source.
What StatsD client libraries do you support?: v1 supports: Python (datadog package + ddtrace), Ruby (dogstatsd-ruby), Node.js (hot-shots, dd-trace, node-statsd), Go (DataDog/datadog-go), Java (java-dogstatsd-client + dd-trace-java). C# (DogStatsD-CSharp-Client), PHP, and Rust are on the v2 roadmap. The analyzer detects each client by import statement + call-site pattern, so if your repo mixes languages (e.g., Python services + Node services), all are scanned in a single audit.
What if my infrastructure is split across many repos (microservices monorepo vs polyrepo)?: One audit = one repo. If you have 5 service repos (e.g., per-team or per-service), that's 5 audits — but email us first; we offer bundle pricing ($99/repo for 5+ repos in the same organization). For monorepos containing 20+ services, one $149 audit covers the whole monorepo.
What if you don't find $149 of recurring savings?: Refund. The 30-day re-audit voucher is a structural accountability layer: implement the fixes, prove the savings didn't materialize in Datadog Plan & Usage, refund. We've designed the product so it's only profitable if the rules actually work. The verification step in Datadog Plan & Usage is included in the deliverable specifically to make this falsifiable.

→ See a real sample report first ($8,400/mo recurring of Datadog waste found)