Datadog Cost Optimization
OhChimp reads your Datadog usage summary to flag high indexed log volume and break down spend by product, with every fix written as a plan.
What OhChimp optimizes for Datadog
Some of the cost levers OhChimp checks for Datadog. Each one becomes a reviewable plan you approve before anything changes.
High indexed log volume
Indexed logs are a common Datadog cost driver, and ingestion creeps up one noisy source at a time. OhChimp reads your usage summary and flags when indexed log events run high for the month, then writes a plan to add exclusion filters and sample the chatty sources so you index what you actually search.
Per-product usage breakdown
Your bill blends logs, metrics, APM, infra hosts, and more into one number that hides where the spend goes. OhChimp reads the monthly usage summary across products, so the line items driving your invoice are visible instead of buried three menus deep.
Month-to-date usage check
A bill spike usually starts as a quiet mid-month climb in one product's usage. OhChimp reads the current month's usage summary so you can see where the month is heading and act before the invoice lands.
Targeted metric and log inspection
When a usage number looks off, you need to find the source behind it. OhChimp can query specific Datadog metrics and search logs by service or status, so a plan points at the exact noisy source rather than guessing at the aggregate.
Worked example
For example, say OhChimp reads your usage summary and sees indexed log events running well over a million for the month, concentrated in one debug-heavy service.
It writes a plan to add an exclusion filter and sample that source down to what you actually query. You press apply. OhChimp cannot yet measure what a single Datadog plan saves, so it does not mark one VERIFIED. The projected saving stays an estimate, and when the 30 days of monitoring end the result reads "Inconclusive."
Datadog cost optimization FAQ
How does OhChimp connect to Datadog?
With a Datadog API key and Application key. OhChimp validates them against Datadog's /api/v1/validate endpoint, then reads your monthly usage summary across products. Your application data, secrets, and telemetry contents are never stored.
Does OhChimp need write access or see my log contents?
OhChimp reads usage and billing metadata from your usage summary, plus targeted metric queries and log searches when a plan needs to pinpoint a source. It does not change your Datadog setup on its own. Your application data, secrets, and telemetry contents are never stored.
What Datadog costs can OhChimp actually reduce?
It reads your usage summary to flag high indexed log volume, then recommends exclusion filters and sampling. It also reads spend per product and the current month's usage, so the line items driving your bill are visible before the invoice lands.
Who applies the changes, and can I roll back?
You do. Every fix is a reviewable plan with a confidence score, a risk level, and rollback steps. Nothing changes until you click apply, and you are always the one who clicks.
How are savings verified, and how is OhChimp priced?
Today Datadog savings stay estimates. OhChimp cannot yet measure what a single Datadog plan saves, so it does not mark one VERIFIED, and when the 30 days of monitoring end the result reads "Inconclusive." The only savings OhChimp measures today come from lowering the CPU and memory requests of a Kubernetes Deployment, StatefulSet, or DaemonSet. Pricing is flat monthly with no cut of your savings, plus a year-one ROI guarantee that lets you request a refund of your first 12 months of subscription fees if the verified savings on your Costs tab fall short of them. Estimated savings do not count.
The indexed-log-volume lever, start to finish: Cut Datadog log costs: indexing bills 17x ingestion.
Related integrations
Teams running Datadog usually run these too. OhChimp finds the waste in each and drafts every fix as a plan you approve.
Grafana
Metric series, log volume, and retention
Coralogix
Log volume, retention tiers, and ingest
New Relic
Data ingest, user seats, and retention