This page describes how you can optimize and monitor your Google Cloud Observability costs. For pricing information, see Google Cloud Observability pricing.
You might also be interested in the following documents:
- Estimate your bills.
- Pricing examples.
- Optimize costs with the Cost Explorer. The Cost Explorer provides current and historical visualizations of cost data and utilization metrics. As such, the data helps you identify optimization opportunities.
Optimize
This section provides guidance about how to reduce or optimize costs associated with Cloud Logging, Cloud Trace, and Google Cloud Managed Service for Prometheus.
Reduce your Cloud Logging costs
To reduce your Cloud Logging storage costs, configure exclusion filters on your log sinks to prevent low-value log entries from being streamed into your log buckets. You can configure a log sink to exclude all log entries that match an exclusion filter, or to exclude only a percentage of the matching log entries. Excluded log entries aren't streamed to your log buckets and they don't count against your storage allotment. To learn more, see Log sink filters.
Cloud Logging storage costs apply only to log data that is stored in log buckets. You can configure your log sinks so that log data isn't stored in log buckets but is instead routed to one of the following destinations:
Cloud Logging doesn't charge to route log entries to the listed destinations. However, you might be charged when log entries are received by a destination.
For information about routing log data, see Route logs to supported destinations.
Optimize costs for Managed Service for Prometheus
Pricing for Managed Service for Prometheus is designed to be controllable. Because you are charged on a per-sample basis, you can use the following levers to control costs:
Sampling period: Changing the metric-scraping period from 15 seconds to 60 seconds can result in a 75% cost savings, without sacrificing cardinality. You can configure sampling periods on a per-job, per-target, or global basis.
Filtering: You can use filtering to reduce the number of samples sent to to the service's global datastore; for more information, see Filtering exported metrics. Use metric-relabeling configs in your Prometheus scrape configuration to drop metrics at ingestion time, based on label matchers.
Keep high-cardinality, low-value data local. You can run standard Prometheus alongside the managed service, using the same scape configs, and keep data locally that's not worth sending to the service's global datastore.
Pricing for Managed Service for Prometheus is designed to be predictable.
You are not penalized for having sparse histograms. Samples are counted only for the first non-zero value and then when the value for bucketn is greater than the value in bucketn-1. For example, a histogram with values
10 10 13 14 14 14counts as three samples, for the first, third and fourth buckets.Depending on how many histograms you use, and what you use them for, the exclusion of unchanged buckets from pricing might typically result in 20% to 40% fewer samples being counted for billing purposes than the absolute number of histogram buckets would indicate.
By charging on a per-sample basis, you are not penalized for rapidly scaled and unscaled, preemptible, or ephemeral containers, like those created by HPA or GKE Autopilot.
If Managed Service for Prometheus charged on a per-metric basis, then you would pay for a full month's cardinality, all at once, each time a new container was spun up. With per-sample pricing, you pay only while the container is running.
Queries, including alert queries
All queries issued by the user, including queries issued when Prometheus recording rules are run, are charged through Cloud Monitoring API calls.
Reduce your Trace usage
To control Trace span ingestion volume, you can manage your trace sampling rate to balance how many traces you need for performance analysis with your cost tolerance.
For high-traffic systems, most customers can sample at 1 in 1,000 transactions, or even 1 in 10,000 transactions, and still have enough information for performance analysis.
Sampling rate is configured with the Cloud Trace client libraries.
Reduce your alerting bill
This section describes strategies you can use to reduce costs for alerting.
Consolidate alerting policies to operate over more resources
Alerting charges a per-condition cost. For this reason, when possible, use one alerting policy to monitor multiple resources instead of creating one alerting policy for each resource.
For example, assume that you have 100 VMs. Each VM generates a time series
for the metric type my_metric. Here are two different ways
you can monitor the time series:
You create one alerting policy that has one condition. The condition monitors
my_metricand aggregates data to the VM level. After aggregation, there is one time series for each VM. Therefore, the condition monitors 100 time series.You create 100 alerting policies and each contains 1 condition. Each condition monitors the
my_metrictime series for one of the VMs, and it aggregates data to the VM level. Therefore, each condition monitors one time series.
The second option, which creates 100 conditions, is more expensive than the first option, which only creates 1 condition. Both options monitor 100 time series.
Aggregate to only the level that you need to alert on
There is a cost for each time series that is monitored by an alerting policy. Aggregating to higher levels of granularity results in higher costs than aggregating to lower levels of granularity. For example, aggregating to the Google Cloud project level is cheaper than aggregating to the cluster level, and aggregating to the cluster level is cheaper than aggregating to the cluster and namespace level.
For example, assume that you have 100 VMs. Each VM generates a time series
for the metric type my_metric. Each of your VMs belongs to one of five
services. You decide to create one alerting policy that has one condition that
monitors my_metric. Here are two different aggregation options:
You aggregate data to the service. After aggregation, there is one time series for each service. Therefore, the condition monitors 5 time series.
You aggregate data to the VM level. After aggregation, there is one time series for each VM. Therefore, the condition monitors 100 time series.
The second option, which monitors 100 time series, is more expensive than the first option, which only monitors five time series.
When you configure your alerting policies, choose aggregation levels that work best for your use case. For example, if you care about alerting on CPU utilization, then you might want to aggregate to the VM and CPU level. If you care about alerting on latency by service, then you might want to aggregate to the service level.
Don't alert on raw, unaggregated data
Monitoring uses a dimensional metrics system, where any metric has total cardinality equal to the number of resources monitored multiplied by the number of label combinations on that metric. For example, if you have 100 VMs emitting a metric, and that metric has 10 labels with 10 values each, then your total cardinality is 100 * 10 * 10 = 10,000.
As a result of how cardinality scales, alerting on raw data can be extremely expensive. In the previous example, you have 10,000 time series returned for each execution period. However, if you aggregate to the VM, then you have only 100 time series returned per execution period, regardless of the label cardinality of the underlying data.
Alerting on raw data also puts you at risk for increased time series when your metrics receive new labels. In the previous example, if a user adds a new label to your metric, then your total cardinality increases to 100 * 11 * 10 = 11,000 time series. In this case, your number of returned time series increases by 1,000 each execution period even though your alerting policy is unchanged. If you instead aggregate to the VM, then, despite the increased underlying cardinality, you still have only 100 time series returned.
Filter out unnecessary responses
Configure your conditions to evaluate only data that's necessary for your alerting needs. If you wouldn't take action to fix something, then exclude it from your alerting policies. For example, you probably don't need to alert on an intern's development VM.
To reduce unnecessary incidents and costs, you can filter out time series that aren't important. You can use Google Cloud metadata labels to tag assets with categories and then filter out the unneeded metadata categories.
Use top-stream operators to reduce the number of time series returned
If your condition uses a PromQL query, then you can use a top-streams operator to select a number of the time series returned with the highest values:
- PromQL:
topk
For example, a topk(metric, 5) clause in a PromQL query limits
the number of time series returned to five in each execution period.
Limiting to a top number of time series might result in missing data and faulty incidents, such as:
- If more than N time series violate your threshold, then you will miss data outside the top N time series.
- If a violating time series occurs outside the top N time series, then your incidents might auto-close despite the excluded time series still violating the threshold.
- Your condition queries might not show you important context such as baseline time series that are functioning as intended.
To mitigate such risks, choose large values for N and use the top-streams operator only in alerting policies that evaluate many time series, such as incidents for individual Kubernetes containers.
Increase the length of the execution period (PromQL only)
If your condition uses a PromQL query, then you can modify the length
of your execution period by setting the
evaluationInterval field in the
condition.
Longer evaluation intervals result in fewer time series returned per month; for example, a condition query with a 15-second interval runs twice as often as a query with a 30-second interval, and a query with a 1-minute interval runs half as often as a query with a 30-second interval.
Monitor
This section describes how to monitor your costs by creating alerting policies. An alerting policy can monitor metric data and notify you when that data crosses a threshold.
Monitor monthly log bytes ingested
To create an alerting policy that triggers when the number of log bytes written to your log buckets exceeds your user-defined limit for Cloud Logging, use the following settings.
| New condition Field |
Value |
|---|---|
| Resource and Metric | In the Resources menu, select Global. In the Metric categories menu, select Logs-based metric. In the Metrics menu, select Monthly log bytes ingested. |
| Filter | None. |
| Across time series Time series aggregation |
sum |
| Rolling window | 60 m |
| Rolling window function | max |
| Configure alert trigger Field |
Value |
|---|---|
| Condition type | Threshold |
| Alert trigger | Any time series violates |
| Threshold position | Above threshold |
| Threshold value | You determine the acceptable value. |
| Retest window | Minimum acceptable value is 30 minutes. |
Monitor total metrics ingested
It can't create an alert based on the monthly metrics ingested. However, you can create an alert for your Cloud Monitoring costs. For information, see Configure a billing alert.
Monitor monthly trace spans ingested
To create an alerting policy that triggers when your monthly Cloud Trace spans ingested exceeds a user-defined limit, use the following settings.
| New condition Field |
Value |
|---|---|
| Resource and Metric | In the Resources menu, select Global. In the Metric categories menu, select Billing. In the Metrics menu, select Monthly trace spans ingested. |
| Filter | |
| Across time series Time series aggregation |
sum |
| Rolling window | 60 m |
| Rolling window function | max |
| Configure alert trigger Field |
Value |
|---|---|
| Condition type | Threshold |
| Alert trigger | Any time series violates |
| Threshold position | Above threshold |
Threshold value |
You determine the acceptable value. |
| Retest window | Minimum acceptable value is 30 minutes. |
Configure a billing alert
To be notified if your billable or forecasted charges exceed a budget, create an alert by using the Budgets and alerts page of the Google Cloud console:
-
In the Google Cloud console, go to the Billing page:
You can also find this page by using the search bar.
If you have more than one Cloud Billing account, then do one of the following:
- To manage Cloud Billing for the current project, select Go to linked billing account.
- To locate a different Cloud Billing account, select Manage billing accounts and choose the account for which you'd like to set a budget.
- In the Billing navigation menu, select Budgets & alerts.
- Click Create budget.
- Complete the budget dialog. In this dialog, you select Google Cloud projects and products, and then you create a budget for that combination. By default, you are notified when you reach 50%, 90%, and 100% of the budget. For complete documentation, see Set budgets and budget alerts.