Schema-less machine-data ingest
Accept logs, traces, metrics, and events without requiring a fixed schema at ingestion. This preserves raw fields and accommodates changing telemetry sources.
Axiom
Axiom is a fully managed machine-data observability platform for collecting, retaining, querying, and monitoring logs, traces, metrics, and events. It helps development and operations teams analyze high-volume telemetry without running their own observability cluster.
Managed observability for high-volume machine data
Axiom is a managed machine-data and observability platform for collecting, retaining, querying, and monitoring logs, traces, metrics, and events. It uses schema-less ingestion on a fully managed event store, allowing teams to keep raw machine data while avoiding the cluster operations and ingest-time schema decisions associated with self-managed systems.
The platform combines datasets, streams, a query builder and editor, dashboards, and monitors. APL provides a piped, sequential query experience for log-oriented data, while MPL is designed for metrics. Virtual fields support query-time transformations, making the platform suitable for telemetry sources whose fields and event shapes change over time.
Core capabilities
Managed event storage
Store machine data in a fully managed event store with a stream-oriented, schema-on-read architecture. Axiom states that the platform provides petabyte-scale ingest and 95%+ compression.
APL and MPL query languages
Use APL for piped, sequential, log-oriented analysis and MPL for metrics on a purpose-built metrics engine. The site also presents virtual fields for query-time transformations, joins, and scheduled queries.
Investigation and monitoring workflows
Explore datasets through query and stream views, then use dashboards and monitors to turn recurring investigations into ongoing visibility and alerts.
Usage-based cost controls
Use automatic volume discounts, spend alerts, and configurable hard caps on Axiom Cloud. The pricing model combines a platform fee with usage rather than a large set of separate product SKUs.
Practical use cases
High-volume log management
Centralize application, infrastructure, container, and service telemetry, then retain the underlying events for troubleshooting instead of relying on sampling or moving older data into a separate rehydration workflow.
Incident response and performance analysis
Investigate an incident by filtering events, grouping failures, calculating latency percentiles, and examining fields such as route, region, status, or service. Dashboards and monitors can support follow-up visibility after the investigation.
Distributed-service observability
Combine OpenTelemetry-style logs, metrics, and traces with event data from services and infrastructure. Query-time fields help teams analyze telemetry from sources with different or evolving structures.
Telemetry cost management
Model ingest, query compute, storage, and retention usage before scaling a workload. Teams can use volume discounts, pre-purchased credits, spend alerts, and hard caps to control the financial impact of growing telemetry volumes.
Pros and Cons
Pros
- Schema-less ingestion and schema-on-read reduce the need to finalize data models before ingesting telemetry.
- The fully managed event store is designed for high-volume, petabyte-scale data without requiring customers to run a self-managed observability cluster.
- APL, MPL, datasets, streams, dashboards, and monitors cover both ad hoc investigation and recurring operational monitoring.
- Axiom offers a permanent free Personal plan, a paid cloud plan, automatic volume discounts, and self-serve enterprise add-ons.
Cons
- The usage-based pricing model requires teams to understand ingest, query-compute, storage, and retention usage; Axiom provides in-console estimates and spend controls to manage this.
- APL and MPL are distinct query languages, so teams may need to learn Axiom's query workflows when moving from another tool.
FAQ
What is Axiom?
Axiom is a managed machine-data platform for collecting, querying, and analyzing logs, traces, metrics, and other events. It is designed for teams that need to retain and investigate high-volume telemetry without operating their own observability cluster.
How does Axiom handle changing data schemas?
Axiom uses schema-less ingestion and a schema-on-read approach. Virtual fields support query-time transformations, so teams can work with changing event structures without making ingest-time schema decisions.
How can teams query and monitor data in Axiom?
Axiom provides APL for log-oriented queries and MPL for metrics. The platform also includes query, stream, dashboards, monitors, datasets, and builder/editor workflows for exploring and monitoring machine data.
Which teams and projects is Axiom suited for?
The Personal plan is intended for individual developers and small projects, while Axiom Cloud is intended for teams and adds configurable retention, larger included allowances, spend controls, and self-serve enterprise add-ons. The source also describes a permanent free tier and a paid cloud plan.
Quick Facts
- Category
- Machine-data observability platform
- Primary data
- Logs, traces, metrics, and events
- Deployment
- Fully managed cloud service
- Query languages
- APL and MPL
- Free offering
- Permanent Personal plan with included usage allowances
- Enterprise controls
- SSO, RBAC, Directory Sync, and Audit Logs available as Axiom Cloud add-ons
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