Axiom

What is Axiom?

Axiom is a modern machine data platform that provides petabyte-scale, schema-less ingest on a fully managed event store for developers and engineering teams. It is designed so teams can keep every byte of their machine data without the operational cost of running the underlying infrastructure themselves.

Key features

• Schema-less ingest at petabyte scale: Axiom accepts machine data without requiring a predefined schema, and the platform is built to handle petabyte-scale volumes on a fully managed event store.
• Query language with pipe syntax: Queries use a pipe-based syntax, as shown in examples such as filtering a dataset with where, then aggregating with summarize count() by a field. This supports filtering, aggregation, percentiles, and sorting in a readable chain.
• Dataset library: A searchable library organizes data sources as datasets, with examples including api-gateway-prod, auth-service, billing-events, checkout-service, cloudflare-logs, frontend-web-vitals, k8s-events, kafka-consumer-lag, lambda-invocations, nginx-access-logs, otel-spans, payment-service, postgres-slow-queries, redis-metrics, stripe-webhooks, and vercel-edge-logs.
• Dashboards: The platform includes dashboards alongside query, stream, and monitor capabilities for visualizing machine data.
• Monitors: Monitors are a first-class part of the product, supporting alerting-style workflows on top of stored event data.
• Query Stream: A stream view complements query, dashboards, and monitors as part of the core interface.
• Dataset Builder and Editor: A builder and editor environment supports composing and running queries, with controls for time ranges such as "Last 30 mins," run, cancel, clear, and save.
• AI-assisted investigation: An axiom-agent interface and an axiom-mcp connection appear in the product, illustrated by an example prompt asking why checkout p95 spiked at a given time during an incident.

Who is it for?

Axiom targets engineering teams that generate large volumes of machine data and are hitting the limits of their current observability stack. The source describes the problem directly: what worked at terabytes does not work at petabytes, costs spiral with volume, logs get sampled to stay under budget, older data freezes into rehydration tickets, every new source means pipeline work, and the team managing the stack keeps growing alongside the data. It suits teams that want to retain full-fidelity event data without self-managing clusters.

Use cases

• API and service monitoring: Query an api-gateway-prod dataset for requests with status codes at or above 500 and summarize counts over time to track error rates.
• Latency analysis by route: Summarize p95 request duration by route and sort descending to find the slowest endpoints.
• Payment failure investigation: Filter a payment-service dataset for charge.failed events and summarize counts by decline reason.
• Frontend audience analysis: Summarize distinct session counts by country from a frontend-web-vitals dataset and sort by user count.
• Rate-limit tracking: Filter nginx-access-logs for status 429 and summarize hits by client IP.
• Incident diagnosis: Use the axiom-agent to investigate questions such as why checkout p95 spiked at a specific time, within an incident context.

Pricing

The source does not list public pricing figures. It shows a "Start free" call to action and a "Talk to sales" path, indicating a free entry point and a sales-led option, but no specific prices or plan details are documented.

What's new and trending

The source does not publish dated release notes or version numbers. It does show current product surface areas including the axiom-agent, an axiom-mcp connection, and incident-oriented investigation workflows, but no launch dates or recent release announcements are documented.

Getting started

The source offers a "Start free" path and a "Talk to sales" contact route from the homepage. A public getting-started guide or step-by-step onboarding documentation was not listed in the source material.

Alternatives

The source does not name any alternative products or competitors.

FAQ

What is Axiom used for? Axiom is used to ingest, store, and query machine data such as logs, events, and telemetry at petabyte scale. Teams use it for querying datasets, building dashboards, setting up monitors, and investigating incidents.

Is Axiom free? The source shows a "Start free" call to action, indicating a free entry point, but it does not publish specific pricing figures or plan details. Public pricing was not listed.

Does Axiom require a schema? No. Axiom is described as offering schema-less ingest on a fully managed event store, so data can be ingested without a predefined schema.

What query language does Axiom use? Axiom queries use a pipe-based syntax, chaining operations such as where for filtering and summarize for aggregation, including percentile calculations and sorting.

Does Axiom support dashboards and monitors? Yes. Dashboards and monitors are listed alongside query and stream as core parts of the Axiom interface, and the platform also includes a dataset library, builder, and editor.