Axiom measures the resources used to execute queries in terms of GB-hours.

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What GB-hours are

When you run queries, your usage of the Axiom platform is measured in query-hours. The unit of this measurement is GB-hours which reflects the duration (measured in milliseconds) serverless functions are running to execute your query multiplied by the amount of memory (GB) allocated to execution. This metric is important for monitoring and managing your usage against the monthly allowance included in your plan.

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How Axiom measures query-hours

Axiom uses serverless computing to execute queries efficiently. The consumption of serverless compute resources is measured along two dimensions:

  • Time: The duration (in milliseconds) for which the serverless function is running to execute your query.
  • Memory allocation: The amount of memory (in GB) allocated to the serverless function during execution.

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What counts as a query

In calculating query costs, Axiom considers any request that queries your data as a query. For example, the following all count as queries:

  • You initiate a query in the Axiom user interface.
  • You query your data with an API token or a personal access token.
  • Your match monitor runs a query to determine if any new events match your criteria.

Each query is charged at the same rate, irrespective of its origin.

Each monitor run counts towards your query costs. For this reason, the frequency (how often the monitor runs) can have a slight effect on query costs.

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Run queries and understand costs

When you run queries on Axiom, the cost in GB-hours is determined by the shape and size of the events in your dataset and the volume of events scanned to return a query result. After executing a query, you can find the associated query cost in the response header labeled as X-Axiom-Query-Cost-Gbms.

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Determine query cost

Send a POST request to the Run query endpoint with the following configuration:

  • Content-Type header with the value application/json.

  • Authorization header with the value Bearer API_TOKEN. Replace API_TOKEN with your Axiom API token.

  • In the body of your request, enter your query in JSON format. For example:

    {
  "apl": "telegraf | count",
  "startTime": "2024-01-11T19:25:00Z",
  "endTime": "2024-02-13T19:25:00Z"
}

    apl specifies the Axiom Processing Language (APL) query to run. In this case, "telegraf | count" indicates that you query the telegraf dataset and use the count operator to aggregate the data.

    startTime and endTime define the time range of your query. In this case, "2024-01-11T19:25:00Z" is the start time, and "2024-02-13T19:25:00Z" is the end time, both in ISO 8601 format. This time range limits the query to events recorded within these specific dates and times.

In the response to your request, the information about the query cost in GB-milliseconds is in the X-Axiom-Query-Cost-Gbms header.

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Example of GB-hour calculation

As an example, a typical query analyzing 1 million events might consume approximately 1 GB-second. There are 3,600 seconds in an hour which means that an organization can run 3,600 of these queries before reaching 1 GB-hour of query usage. This is an example and the actual usage depends on the complexity of the query and the input data.

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Plan and GB-hours allowance

Your GB-hours allowance depends on your pricing plan. To learn more about the plan offerings and find the one that best suits your needs, see Axiom Pricing.

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Optimize queries to lower costs

This section explains ways you can optimize your queries to save on query costs. For more information optimizing your queries for performance, see Optimize performance.

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Optimize the order of field-specific filters

Field-specific filters narrow your query results to events where a field has a given value. For example, the APL query where ["my-field"] == "axiom" filters for events where the my-field field takes the value axiom.

Include field-specific filters near the beginning of your query for modest savings in query costs. For more information, see Poor filter order in queries

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Optimize search operator and non-field-specific filters

Non-field-specific filters narrow your query results by searching across multiple datasets and fields for a given value. Examples of non-field-specific filters are the search operator and equivalent expressions such as where * contains or where * has.

Using non-field-specific filters can have a significant impact on query costs. For more information, see Use the search operator efficiently and Regular expressions when simple filters suffice.

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Optimize dashboard refresh rates

Each time your dashboard refreshes, it runs a query on your data which results in query costs. Selecting a short refresh rate (such as 15s) for a long time range (such as 90 days) means that your dashboard frequently runs large queries in the background.

To optimize query costs, choose a refresh rate that is appropriate for the time range of your dashboard. For more information, see Select refresh rate.