Grafana Labs today made available an ability to customize what observability data is collected via its managed cloud service. This capability can help reduce storage costs in addition to enhancing the dashboard Grafana provides to make it simpler to track metrics usage.

Wayne Jin, vice president of product marketing for Grafana Labs, said both capabilities would make it easier for DevOps teams to track and ultimately reduce the cost of storing the time-series data collected via its cloud platform.

Step 1 of 7

14%

Which of the following best describes how you currently monitor and manage systems across your hybrid cloud environment?(Required)

Multiple separate tools for different environments

Multiple separate tools for different monitoring and management functions

Single consolidated dashboard

Custom built monitoring and management solution

No full monitoring and management solution

Don’t know

How valuable would it be for your monitoring and management solution to incorporate predictive analytics to help you identify and address system issues that are critical and not waste time or effort working through all the data and “noise” that is not critical?(Required)

Extremely valuable

Moderately valuable

Somewhat valuable

Slightly valuable

Not valuable

How do you most typically address potential system issues BEFORE they impact your operations?(Required)

Automated workflows and playbooks to remediate potential issues as soon as they are detected

An orchestrated response process that leverages automation, AI, and human expertise to rapidly respond to potential issues

Advanced monitoring tools such as predictive analytics and anomaly detection to proactively identify and address potential issues

Respond to issues as they arise, using manual processes and troubleshooting techniques

A combination of automated remediation, orchestrated response, and/or proactive monitoring to address potential system issues

Don’t know

Other

What methods does your organization use to control and monitor cloud/IT spending?(Required)

Centralized procurement processes

Pre-approved budget thresholds

Automated cost monitoring tools

Department-level spending controls

Regular cost audits and reviews

Cloud cost optimization platform

Approval workflows for resource provisioning

Native financial operations tools provided by the cloud provider

No formal controls in place

Don’t know

Other

Please specify:

Which methods do you rely on most to detect security vulnerabilities in your systems?(Required)

Automated scanning tools

Manual assessments

Vendor notifications

Threat modeling (for early detection)

None of these

Don’t know

Other

Please specify:

What are the TWO most common ways you prioritize vulnerabilities to address within your environment?(Required)

Based on potential impact

Based on exploitability

Regulatory compliance requirements

Based on CVSS scores

Risk-based analysis of multiple factors

First-in-first-out approach

No formal prioritization process

Don’t know

How do you typically handle system updates and security patches?(Required)

Automated updates and patches

Manually review and apply updates and patches on a regular schedule

A combination of automation and manual review

Apply updates and patches on an as-needed basis.

Outsourced update and patch management

No defined process

Don’t know

Other

Δ

As more DevOps teams use an observability platform to continuously collect metrics like traces and log data, they are discovering that the cost of storing all that high-cardinality data can easily spin out of control.

Grafana Labs is now making available an Adaptive Metrics capability that enables DevOps teams to fine-tune what data is collected and stored. However, in the event of an issue, DevOps teams can also turn back on all data the platform can collect to aid an investigation of the root cause of disruption, noted Jin.

The Cardinality Management dashboards Grafana Labs made available last year for paid tiers of its cloud service are now being made available to users of the free tier of service as well, Jin said. This will further help reduce storage costs by making it easier to identify metrics that are being collected but not used by anyone on the DevOps team, he added.

The Adaptive Metrics aggregation engine can then be applied to transform these metrics at ingestion into lower cardinality versions. Unused or partially used labels are stripped from incoming metrics, reducing the total count of time-series data collected. Adaptive Metrics also recommends aggregations based on an organization’s historic usage patterns, and DevOps teams can choose which aggregation rules to apply. Dashboards, alerts and historic queries are guaranteed to continue to work as they did before aggregation, with no rewrites needed.

Based on results reported by early users, Grafana Labs reported that Grafana Cloud Adaptive Metrics could eliminate an estimated 20-50% of the time-series data collected with no perceived impact on observability.

The Grafana Cloud service relies on an instance of open source Grafana Mimir to store data collected in a format originally defined for the open source Prometheus monitoring tool. While Prometheus is widely employed in Kubernetes environments, it’s also starting to gain traction as a monitoring platform in legacy monolithic environments. The amount of data being collected via that platform over time is steadily increasing. The Grafana Labs approach makes it possible for DevOps teams to reduce that data whenever there is no usage that needs to be investigated, for example.

It’s still early days as far as adoption of observability is concerned, but in uncertain economic times, there’s generally a lot more sensitivity to the total cost of IT. Many organizations are especially focused on reducing the cost of storing data in the cloud to rein in monthly spending on cloud services. Regardless of the motivation, collecting and storing data that isn’t needed is never the best use of cloud storage resources that, from a cost perspective, are just as finite in the cloud.