Limits

Axiom applies certain limits and requirements to guarantee good service across the platform. Some of these limits depend on your pricing plan, and some of them are applied system-wide. This reference article explains all limits and requirements applied by Axiom.

Limits are necessary to prevent potential issues that could arise from the ingestion of excessively large events or data structures that are too complex. Limits help maintain system performance, allow for effective data processing, and manage resources effectively.

Pricing-based limits

The table below summarizes the limits applied to each pricing plan. For more details on pricing and contact information, see the Axiom pricing page.

* Soft limit that can be increased upon request.

If you’re on the Axiom Cloud plan and you exceed the Always Free allowances outlined above, additional charges apply based on your usage above the allowance. For more information, see the Axiom pricing page.

All plans include unlimited bandwidth, API access, and data sources subject to the Fair Use Policy.

To see how much of your allowance each dataset uses, go to Settings > Usage.

For more information on how to save on data loading, data retention, and querying costs, see Optimize usage.

Restrictions on datasets and fields

Axiom restricts the number of datasets and the number of fields in your datasets. The number of datasets and fields you can use is based on your pricing plan and explained in the table above.

If you ingest a new event that would exceed the allowed number of fields in a dataset, Axiom returns an error and rejects the event. To prevent this error, ensure that the number of fields in your events are within the allowed limits.

To reduce the number of fields in a dataset, use one of the following approaches:

• Delete the fields you don’t need.
• Vacuum the fields to remove fields that no longer occur in events within your retention period. If necessary, trim the dataset first.
• Use map fields.

To prevent new fields from being added to an event dataset, lock its schema.

System-wide limits

The following limits are applied to all accounts, irrespective of the pricing plan.

Limits on ingested data

The table below summarizes the limits Axiom applies to each data ingest. These limits are independent of your pricing plan. For metrics, see Limits on ingested metrics.

If you try to ingest data that exceeds these limits, Axiom does the following:

• Replaces strings that are too long with <invalid string: too long>.
• Replaces binary with <invalid data>.
• Truncates maps and slices that nest deeper than 100 levels and replaces them with nil at the cut-off level.
• Converts the following float values to nil:
  • NaN
  • +Infty
  • -Infty

Limits on ingested metrics

The limits below apply to OpenTelemetry metrics that you send to the /v1/metrics endpoint. They’re separate from the event ingest limits above and independent of your pricing plan.

The maximum request size applies to the uncompressed payload. Axiom accepts gzip, zstd, brotli, and deflate compression, and compressing requests reduces the bandwidth you use. Axiom checks the request size after decompressing the request, so compression doesn’t allow you to send more data in a single request.

If a request exceeds the maximum request size, Axiom rejects it with the HTTP status code 413. The OpenTelemetry Collector treats this status code as a permanent error, so it drops the batch instead of retrying it.

To keep requests within the limit, use the batch processor in your OpenTelemetry Collector configuration and set send_batch_max_size. Whether a given batch size stays within 4 MiB depends on the number of attributes that your data points carry. If you receive 413 responses, reduce send_batch_max_size. For an example configuration, see Send OpenTelemetry data to Axiom.

The maximum number of buckets applies to each histogram data point and counts the entries in its explicit_bounds list. The implicit overflow bucket that catches values above the last boundary doesn’t count towards the limit. For summary data points, the limit counts the entries in the quantile_values list.

If a metric name, an attribute, or the number of buckets or quantiles exceeds the limits above, Axiom doesn’t reject the whole request. It rejects the affected data points, responds with the HTTP status code 200, and returns an OpenTelemetry partial_success message that reports the number of rejected data points and the reason. For example, a histogram data point with too many buckets is reported as [http.server.duration] metric limit violation: Bucket count 300 (explicit_bounds/quantile_values) exceeds limit 256.

Special fields

Axiom creates the following two fields automatically for a new dataset:

• _time is the timestamp of the event. If the data you ingest doesn’t have a _time field, Axiom assigns the time of the data ingest to the events. If you ingest data using the Ingest data API endpoint, you can specify the timestamp field with the timestamp-field parameter.
• _sysTime is the time when you ingested the data.

In most cases, use _time to define the timestamp of events. In rare cases, if you experience clock skews on your event-producing systems, _sysTime can be useful.

Reserved field names

Axiom reserves the following field names for internal use:

• _blockInfo
• _cursor
• _rowID
• _source
• _sysTime

Don’t ingest data that contains these fields names. If you try to ingest a field with a reserved name, Axiom renames the ingested field to _user_FIELDNAME. For example, if you try to ingest the field _sysTime, Axiom renames it to _user_sysTime.

In general, avoid ingesting field names that start with _.

Requirements for timestamp field

The most important field requirement is about the timestamp.

If you include the _time field in the ingested data, follow these requirements:

• Timestamps are specified in the _time field.
• The _time field contains timestamps in a valid time format. Axiom accepts many date strings and timestamps without knowing the format in advance, including Unix Epoch, RFC3339, or ISO 8601.
• The _time field is a field with UTF-8 encoding.
• The _time field isn’t used for any other purpose.

Requirements for log level fields

Stream, query results, and the event timeline use the same rules to choose an event's display level. These rules leave stored event fields and monitor conditions unchanged.

An explicit log level takes priority over words in a message. For example, an event with level: "info" and message: "error error error" displays as info.

Field priority

Axiom checks the following fields in order and stops at the first recognized value. Missing fields and unrecognized values fall through to the next field. Within each table row, fields are checked from left to right.

Axiom reads dotted field names and their nested equivalents, including objects stored as JSON text. For example, both {"record.level": "info"} and {"record": {"level": "info"}} supply record.level. If both forms exist, a non-null dotted field takes precedence.

Recognized values

Text values match exactly, ignoring capitalization. For example, ERROR matches, but errorful and error: request failed don't.

OpenTelemetry numeric fields accept integers from 1 to 24:

These fields also accept decimal strings such as "17". Values such as 0, 25, "017", and "Severity(17)" aren't recognized.

For Convex's data.log_level, DEBUG, INFO, and LOG display as info, WARN as warn, and ERROR as error. The value LOG has no special meaning in other fields or message text.

Message fallback

If no field supplies a recognized level, Axiom checks string values in message, msg, @message, text, record, and data.message, in that order. For each value, Axiom checks only the first 100 characters and first two lines. The first complete severity keyword wins. Axiom doesn't scan the entire event or count repeated keywords.

For example:

If nothing matches, the event displays as neutral unknown.

Inspect a log level

Hover over an event's severity marker to see its display level, the field that determined it, and that field's original value. The tooltip also links to this reference.

The tooltip's format label describes the matching rule: OTel, AWS Lambda, Convex, Common, Message, or Override. It doesn't identify the event's source service. For example, record.level uses the Common rule even when the event comes from AWS Lambda. An unknown level has no deciding field or format label.

In the event details view, click a level badge with a source field to highlight that field. If the value is inside JSON text, Axiom highlights the containing field.

Map a custom field

To use a custom severity field, create a virtual field named level or severity that returns a recognized value. For example, if app_log_level contains values such as INFO and ERROR, create a virtual field named level with this expression:

tostring(app_log_level)

Virtual fields follow the same field priority as stored fields. A virtual field named severity doesn't override a recognized level or an earlier field.

Temporary account-specific limits

If you send a large amount of data in a short amount of time and with a high frequency of API requests, Axiom may temporarily restrict or disable your ability to send data to Axiom. This is to prevent abuse of the platform and to guarantee consistent and high-quality service to all customers. In this case, Axiom kindly asks you to reconsider your approach to data collection. For example, to reduce the total number of API requests, try sending your data in larger batches. This adjustment both streamlines Axiom operations and improves the efficiency of your data ingest. If you often experience these temporary restrictions and have a good reason for changing these limits, please contact Support.