Axiom

Modern machine data platform designed for collecting, storing, and analyzing event and telemetry data at massive scale.

Overview

Axiom is a modern machine data platform designed for collecting, storing, and analyzing event and telemetry data at massive scale. It combines a high-performance proprietary data store (EventDB for events, MetricsDB for metrics) with an intuitive console, enabling teams to keep every byte of data without the operational overhead of self-managed clusters.

The platform addresses the core problems that break traditional observability stacks at scale: spiraling costs, forced data sampling, and growing operational complexity. Axiom uses schema-less ingestion with 25–50x compression, serverless query execution, and a single usage-based pricing dial across logs, traces, metrics, and events. It's built for both human engineers and AI agents, with native MCP server support and query languages (APL for events, MPL for metrics) designed for agent reasoning.

Key features

Schema-less event ingestion at petabyte scale
Dual-engine architecture (EventDB + MetricsDB)
APL and MPL query languages with piped syntax
Native MCP server for AI agent integration
Usage-based pricing with automatic volume discounts
Configurable retention and virtual fields
Real-time dashboards and threshold/anomaly alerts
Splunk App for side-by-side migration

Pros

No data sampling—keep every byte
Predictable, sub-linear cost scaling with volume discounts
Fully managed infrastructure with no operational overhead
Built-in AI agent support via MCP and query primitives
Splunk migration path without rip-and-replace
Enterprise features (SSO, RBAC, audit logs) self-serve in-console
Extreme compression (25–50x) reduces storage costs

Cons

Requires learning APL/MPL query languages if migrating from SQL-style systems
Data residency supports write-side multi-region but not automatic query failover
Soft limits on datasets, fields, and query concurrency (can be lifted on request)
Personal plan limited to 30-day retention

Use this if

You need to retain all observability data without sampling, want predictable costs at scale, are migrating from Splunk or Datadog, or need AI agents to query your observability data natively.

Skip this if

You require automatic multi-region query failover, prefer SQL-based query syntax, or need sub-minute data retention windows.

Best for

Teams migrating from Splunk or DatadogHigh-volume observability workloadsAI agents querying observability dataOrganizations needing cost predictability at scaleMulti-signal observability (logs, metrics, traces)Compliance-heavy environments