Datadog

Keep the Datadog your engineers love. Govern what flows into it.

Datadog is a strong observability and APM platform. We are vendor-agnostic architects who sit in front of it as a control layer, tiering high-signal telemetry into Datadog and routing the rest to cheap storage so cost tracks value.

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Why does Datadog spend climb faster than usage?

Datadog bills across 25+ separately priced products. APM, Infrastructure Monitoring, Log Management, Security Monitoring, and Custom Metrics each carry their own pricing dimension. Predicting the monthly invoice means modeling every active product at once, and most teams underestimate by a wide margin.

Custom metrics are one of the largest line items. Industry benchmarks put them at 30 to 52 percent of total Datadog spend. Engineers who instrument thoroughly get penalized by cardinality, so teams quietly stop collecting telemetry they need. That trades cost pressure for observability blind spots.

Log management charges for ingest and again for indexing, so verbose data nobody queries is paid for twice. The fix is not to leave Datadog. The fix is to decide upstream what deserves a Datadog index and what belongs in low-cost storage.

25+

Separately billed products in a typical Datadog deployment

30-52%

Of total Datadog spend from custom metrics (industry benchmark)

Log charge: separate ingest and indexing on the same data

How do you control Datadog cost without losing observability?

You put a vendor-neutral control layer in front of Datadog and tier the data before it reaches the meter. High-signal logs, traces, and metrics flow into Datadog for full analysis. Verbose and duplicate data lands in object storage at a fraction of the cost and stays replayable.

Logmetry designs and implements that layer at config-level depth, using the right tooling for your stack. Cribl is one platform we use here, alongside native Datadog agent configuration and routing, chosen on the merits of your environment rather than a fixed template.

Tier logs by signal value. Index the high-value subset in Datadog and route the rest to object storage you can replay later, removing the double charge on data nobody queries.
Reduce custom-metric cardinality through aggregation and tag governance before metrics hit the meter, targeting the product that commonly runs 30 to 52 percent of total spend.
Sample and shape APM traces so you keep the spans that matter for latency and error analysis without paying full rate on routine, repetitive traffic.
Route through a drop-in Datadog agent source so existing agents keep running while the control layer handles tiering and shaping upstream.
Get one metering view of every GB by source and destination, replacing the guesswork of reconciling 25+ separate product lines.

How Logmetry governs your Datadog environment

Datadog environments are deeply embedded. Dashboards, monitors, SLOs, and integrations get built over months or years, and every change has to preserve them. Logmetry reviews how your data model, tag taxonomy, and product dependencies interact, then designs a tiering and shaping plan that holds those workflows intact.

Zbigniew Gajuk, our Co-Founder and Chief Observability and Security Architect, has led this work at Fortune 500 scale across 26+ years. The review is free, and the recommendation is honest about tradeoffs so your team makes the call.

1Architecture Review: map the current product breakdown, find the highest-cost line items (custom metrics, log management, APM traces), and quantify the reduction opportunity per product. Your number depends on your environment.

2Design and Recommend: model the control-layer architecture, lay out pros and cons of each routing and tiering option, and let you choose the approach that fits your stack.

3Implement: build the tiering, aggregation, and trace-shaping pipelines with parallel validation so every Datadog dashboard, monitor, and SLO keeps working against the optimized flow.

4Optimize: run ongoing governance with per-source, per-destination cost attribution so you track savings and catch cost anomalies before they reach the invoice.

Frequently asked questions

Are you replacing Datadog?

No. We are vendor-agnostic architects and we are never a SIEM or an APM. Datadog stays your observability and APM platform. We sit in front of it as a control layer that governs what flows in, tiering high-signal data into Datadog and routing the rest to low-cost storage.

How do you reduce custom-metric cost without losing detail?

We aggregate and apply tag governance upstream to cut metric cardinality before it reaches the meter, the product that commonly runs 30 to 52 percent of total Datadog spend. High-value series still flow through in full. Your number depends on your environment.

Can you lower APM-trace cost?

Yes. We shape and sample traces upstream so you keep the spans that matter for latency and error analysis while routine, repetitive traffic stops being charged at full rate. The traces your engineers actually query reach Datadog intact, and the volume that drives the bill drops.

Can you get us onto Datadog from another tool fast?

Yes. With a vendor-neutral control layer in front of your stack, onboarding becomes a parallel run plus a routing change rather than a long rebuild. We send data to Datadog and the existing tool at once, validate dashboards and monitors, then cut over once the new flow is proven.

Ready to explore this further?

Let's discuss how this applies to your environment.

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