Optimize

Latest Dynatrace
Explanation
3-min read
Published May 26, 2026

Once you know where your consumption is going, optimization is about two questions: how much data do you really need to send and keep, and how efficiently are you querying it?

Optimization is the closing loop of cost management: once you can see and attribute spend, you can reduce it. Two levers drive DPS cost down:

Send and keep less data.
Query your data more efficiently with DQL, dashboards, workflows.

This page points to the best-practice guides that answer those questions for each Dynatrace capability.

If you don't know yet which signal or query surface is driving cost, start with View to see your usage breakdown, then come back here.

Overview

The cards below link to capability-specific best-practice guides to help you achieve the following use cases:

Cut log volume by tuning ingest pipelines, sampling, and retention.
Reduce metric cardinality and trim span metrics.
Right-size retention per data type to match compliance and operational needs.
Detect and tune expensive DQL queries before they accumulate cost.
Optimize dashboards and notebooks to reduce repeat query volume.

Looking for real-world examples? The Dynatrace community examples repository is an open space where practitioners share configurations across Dynatrace apps and capabilities.

Reduce ingested and retained data

Retention

How long you keep data is part of what you pay for. Use this reference to understand defaults and the configurable ranges per data type.

Data retention periods

Reduce query cost

Write efficient queries

DQL best practices

Tune dashboards and notebooks

Optimize performance and costs of dashboards running log queries

Detect expensive queries automatically

Optimize DQL cost with Workflows

Optimize

Latest Dynatrace

Explanation

3-min read

Published May 26, 2026

Once you know where your consumption is going, optimization is about two questions: how much data do you really need to send and keep, and how efficiently are you querying it?

Optimization is the closing loop of cost management: once you can see and attribute spend, you can reduce it. Two levers drive DPS cost down:

Send and keep less data.
Query your data more efficiently with DQL, dashboards, workflows.

This page points to the best-practice guides that answer those questions for each Dynatrace capability.

If you don't know yet which signal or query surface is driving cost, start with View to see your usage breakdown, then come back here.

Overview

The cards below link to capability-specific best-practice guides to help you achieve the following use cases:

Cut log volume by tuning ingest pipelines, sampling, and retention.
Reduce metric cardinality and trim span metrics.
Right-size retention per data type to match compliance and operational needs.
Detect and tune expensive DQL queries before they accumulate cost.
Optimize dashboards and notebooks to reduce repeat query volume.

Looking for real-world examples? The Dynatrace community examples repository is an open space where practitioners share configurations across Dynatrace apps and capabilities.

Optimize

Overview

Reduce ingested and retained data

Logs

Metrics