Billing Logic¶
This article will demonstrate the billing logic for various chargeable items and price calculation within the pay-as-you-go billing framework of Guance products.
Concepts¶
| Term | Description |
|---|---|
| Data Storage | Custom settings for data retention periods for different data types. |
| Basic Billing | The unit price of a certain chargeable item is a fixed value. |
| Tiered Billing | The unit price of a certain chargeable item is a dynamic value, which varies based on the selected data storage strategy for the current data type, resulting in different unit prices. |
Billing Cycle¶
The billing cycle for Guance is daily. Usage statistics for each workspace are calculated daily, with settlement occurring at midnight the following day. Daily bills are generated and synchronized to the Guance Billing Center, from which the consumption amount is deducted based on the actual payment method.
Billing Items¶
Time Series¶
Guance's time series engine primarily involves the following basic concepts for storing data:
| Term | Description |
|---|---|
| Measurement | Generally used to represent a collection corresponding to a statistical value, similar to the concept of a table in relational databases. |
| Data Point | In the context of reporting metric data, it refers to a single sample of metric data, analogous to row data in relational databases. |
| Time | Timestamp, representing the time when the data point was generated, which can also be understood as the time when DataKit collected and reported a row protocol for a specific metric data. |
| Metric | Field, typically holding numerical data that changes over time. For example, common metrics in the CPU measurement include cpu_total, cpu_use, cpu_use_percent. |
| Tags | Tags generally store attribute information that does not change with timestamps. For instance, fields like host and project in the CPU measurement are tag attributes used to identify the actual object properties of metrics. |
Example¶
In the above diagram, the measurement CPU has a total of 6 data points based on a single metric. Each data point includes a time field: time, one metric: cpu_use_percent, and two tags: host, project. The first and fourth rows of data both have host named Hangzhou_test1 and belong to the project Guance, indicating the CPU usage (cpu_use_percent). Similarly, the second and fifth rows represent CPU usage for host named Ningxia_test1 and project Guance. The third and sixth rows have host named Singapore_test1 and belong to the project Guance_oversea.
Based on the time series data shown above, there are three combinations of time series based on the cpu_use_percent metric:
"host":"Hangzhou_test1","project":"Guance"
"host":"Ningxia_test1","project":"Guance"
"host":"Singapore_test1","project":"Guance_oversea"
Similarly, to calculate all metrics' time series within the current workspace, sum up the number of time series actually counted.
Data is collected by DataKit and reported to a workspace. Specifically, this refers to data queried in DQL where NameSpace is M.
Billing Item Statistics
New time series counts are tallied every hour within the same day.
Cost Calculation Formula: Daily cost = (Actual billed quantity / 1000) * Unit price (based on the applied data storage strategy)
Logs¶
Any of the following scenarios will generate log data:
- Enabling log data collection and reporting;
- Configuring monitoring, intelligent inspection, SLO anomaly detection tasks or reporting custom events via OpenAPI;
- Initiating availability tests through self-built nodes and reporting test results.
Billing Item Statistics
Log data quantities added within an hour are counted hourly.
Cost Calculation Formula: Daily cost = (Actual billed quantity / 1,000,000) * Unit price (based on the applied data storage strategy)
Warning
For different storage types, extremely large log data will be split into multiple entries for billing:
ES Storage: If log size exceeds 10 KB, the number of billable entries for this log = integer part of (log size / 10 KB)
SLS Storage: If log size exceeds 2 KB, the number of billable entries for this log = integer part of (log size / 2 KB)
If a single entry is smaller than the above limits, it is still counted as 1 entry.
Data Forwarding¶
Supports forwarding log data to Guance or four other external storage methods. Based on data forwarding rules, the volume of forwarded data is aggregated and billed.
Note: Data forwarded to Guance is still retained in records.
Billing Item Statistics
The capacity of data forwarded within the data storage strategy is counted hourly. Default capacity unit: Bytes.
Cost Calculation Formula: Daily cost = (Actual billed capacity / 1,000,000,000) * Corresponding unit price
Network¶
- Enabling eBPF network data collection
Billing Item Statistics
The number of new hosts added within the day is counted hourly.
Cost Calculation Formula: Daily cost = Actual billed quantity * Corresponding unit price
Application Performance Trace¶
- Daily Span data count within the workspace.
Note: In Guance's new billing adjustment, the larger value between "quantity/10" and trace_id count will be used as the daily billing data.
Billing Item Statistics
The number of new trace_id entries added within an hour is counted hourly.
Cost Calculation Formula: Daily cost = (Actual billed quantity / 1,000,000) * Corresponding unit price
Application Performance Profile¶
- Enabling APM Profile data collection
Billing Item Statistics
The number of new Profile data entries added within an hour is counted hourly.
Cost Calculation Formula: Daily cost = (Actual billed quantity / 10,000) * Corresponding unit price
Warning
Profile data mainly consists of two parts: basic attribute data + Profile analysis files.
If there are oversized Profile analysis files, Profile data will be split into multiple entries for billing.
If the Profile analysis file size exceeds 300 KB, the billing count = integer part of (Profile analysis file size / 300 KB).
If the analysis file is smaller than the limit, it is still counted as 1 entry.
User Access PV¶
- Daily count of Resource, Long Task, Error, Action data within the workspace.
Note: In Guance's new billing adjustment, the larger value between "quantity/100" and PV will be used as the daily billing data.
Billing Item Statistics
The number of new PV data entries added within an hour is counted hourly.
Cost Calculation Formula: Daily cost = (Actual billed quantity / 10,000) * Price (based on the applied data storage strategy)
Session Replay¶
- Enabling session replay collection
Billing Item Statistics
The number of new sessions added within the day is counted hourly.
Cost Calculation Formula: Daily cost = (Actual billed quantity / 1,000) * Corresponding unit price
Warning
If there are overly active sessions, they will be split into multiple entries for billing based on time_spent.
If time_spent > 4 hours, billing count = integer part of (time_spent / 4 hours);
If time_spent is less than 4 hours, it is still counted as 1 session.
Availability Monitoring¶
- Initiating availability tests and returning test results via Guance provided testing nodes
Billing Item Statistics
The number of new test data entries added within an hour is counted hourly.
Cost Calculation Formula: Daily cost = (Actual billed quantity / 10,000) * Corresponding unit price
Warning
Since availability test data is currently stored in the default log index, DQL queries or statistics need to add the following filter conditions to query test data:
index = ['default'], source = ['http_dial_testing', 'tcp_dial_testing', 'icmp_dial_testing', 'websocket_dial_testing'].
Task Triggers¶
-
Enabling monitors, SLO, etc., for periodic detection tasks, where monitor anomaly detection, interval detection, outlier detection, and log detection each count as 5 task triggers per detection, while other detection types count as 1 trigger. Additionally, if the detection interval exceeds 15 minutes, any excess is charged at 1 trigger for every additional 15 minutes;
-
Intelligent monitoring: Host, log, application intelligent detection counts as 10 triggers per execution; user access intelligent detection counts as 100 triggers per execution.
Calculation Examples
Monitor Trigger Counts:
- Normal scenario example: Executing one mutation detection counts as 5 task triggers.
- Exceeding detection interval example: If the detection interval is 30 minutes, exceeding parts are added incrementally every 15 minutes. For instance, executing one outlier detection counts as 6 task triggers.
- Multiple detections and exceeding detection interval example: Executing two interval detections with a combined detection interval of 60 minutes counts as 13 triggers (2 detections * 5 + 3 extra intervals).
Intelligent Monitoring Trigger Count Example: Executing one host intelligent monitoring counts as 10 task triggers.
- Each DataKit/OpenAPI query counts as 1 task trigger;
- Each metric generation query counts as 1 task trigger;
- Each advanced function query provided by Func center counts as 1 task trigger.
Billing Item Statistics
The number of new task triggers added within an hour is counted hourly.
Cost Calculation Formula: Daily cost = (Actual billed quantity / 10,000) * Corresponding unit price
SMS¶
- Configuring alert strategies for SMS notifications
Billing Item Statistics
The number of new SMS sent within an hour is counted hourly.
Cost Calculation Formula: Daily cost = (Actual billed quantity / 10) * Unit price
Billing Example¶
Assume Company A uses Guance to monitor its IT infrastructure and application systems comprehensively.
Assume Company A has a total of 10 hosts (each host has a default daily active timeline of 600), generating 6,000 timelines, 2 million log entries, 2 million Trace entries, 20,000 PV entries, and 20,000 task schedules daily. The data storage strategy used is as follows:
| Chargeable Item | Metrics (Timeline) | Logs | APM Trace | User Access PV |
|---|---|---|---|---|
| Data Storage Strategy | 3 days | 7 days | 3 days | 3 days |
Specific details are as follows:
| Chargeable Item | Daily Billing Quantity | Billing Unit Price | Billing Logic | Daily Billing Cost |
|---|---|---|---|---|
| Timeline | 6,000 entries | $0.6 per thousand entries | (Actual billed quantity / 1,000) * Unit price i.e., (6,000 entries / 1,000 entries) * $0.6 |
$3.6 |
| Logs | 2 million entries | $1.2 per million entries | (Actual counted quantity / billing unit) * Unit price i.e., (2 million / 1 million) * $1.2 |
$2.4 |
| Trace | 2 million entries | $2 per million entries | (Actual counted quantity / billing unit) * Unit price i.e., (2 million / 1 million) * $2 |
$4 |
| PV | 20,000 entries | $0.7 per ten thousand entries | (Actual counted quantity / billing unit) * Unit price i.e., (20,000 / 10,000) * $0.7 |
$1.4 |
| Task Scheduling | 20,000 times | $1 per ten thousand times | (Actual counted quantity / billing unit) * Unit price i.e., (20,000 / 10,000) * $1 |
$2 |
Note: Since timelines are incremental billing items, changes in the number of timelines generated by the company will lead to changes in costs.
More examples of timeline quantity calculations can be found in Timeline Example.