llms.txt
# Axiom
> The modern machine data platform — Petabyte-scale, schema-less ingest on a fully managed event store, so you keep every byte without the operational cost of running it yourself.
[Axiom](https://axiom.co) ingests logs, metrics, traces and events at petabyte scale, stores them cheaply with long retention, and makes them queryable by humans (APL query language, dashboards, monitors) and AI agents (native MCP server).
## Platform
- [Integrations](https://axiom.co/platform/integrations): Native support for OpenTelemetry, every major collector, AWS services, and SDKs in 8 languages. No agents to install. No proprietary format. Data flows in and is queryable immediately.
- [Query](https://axiom.co/platform/query): APL (Axiom Processing Language) is piped, log-friendly, and built for the workflows engineers run every day. Logs, events, and traces in one language; MPL for metrics. Schema-on-read, no indexes to manage. Queryable by your AI agents through the native MCP server.
- [Metrics](https://axiom.co/platform/metrics): Capture every dimension your systems actually emit. Axiom's metrics datastore is purpose-built for high-cardinality time-series and queried in MPL right next to your logs, events, and traces.
- [Governance](https://axiom.co/platform/governance): Turn on enterprise governance the day you need it. Identity, access, audit, and compliance unlock in-app on Axiom Cloud, without a sales conversation or contract amendment.
- [Dashboard-alerts](https://axiom.co/platform/dashboard-alerts): Get real-time visibility into every signal you load, with alerts that act on what you see. Dashboards and monitors share one query surface, so the patterns engineers run ad-hoc become the basis for production monitoring.
- [Overview](https://axiom.co/platform/overview): A petabyte-scale, schema-less event store with the query power of APL, delivered as a fully managed service so your team isn't running the cluster.
- [mcp](https://axiom.co/platform/mcp): Wire your AI agents to the same data your engineers query, with the same languages. Axiom's MCP server is designed around how agents actually use machine data: wide schemas, sensible cell budgets, and full access scope.
## Use cases
- [Product Analytics](https://axiom.co/use-cases/product-analytics): A product manager sees the drop in adoption. An engineer sees the spike in errors. Today those are two tools and a meeting. In Axiom, it is one dataset and one query.
- [Splunk Migration](https://axiom.co/use-cases/splunk-migration): Keep every byte at petabyte scale, on a fully managed event store with usage-based pricing your procurement team can verify. Migrate workload by workload, on your timeline.
- [Observability](https://axiom.co/use-cases/observability): Logs, traces, metrics, and events on one event store. Petabyte ingest with no sampling. APL for power-user query. Usage-based pricing with a bill you can predict. Fully managed.
- [Log Management](https://axiom.co/use-cases/log-management): Keep every byte at petabyte scale, on a fully managed event store with pricing that grows sub-linearly with your volume. No sampling, no DIY clusters, no SKU gating visibility.
- [Custom Event Streams](https://axiom.co/use-cases/custom-event-streams): Write arbitrary event schemas at any cardinality through the Events API and SDKs. Query in APL (logs, events, traces) and MPL (metrics) at real-time speed. Retain for as long as you need on EventDB. Fully managed, with edge architecture for data residency.
- [APM](https://axiom.co/use-cases/apm): Distributed tracing with zero sampling at ingest. Over a million spans rendered in a single trace view. Pivot from a span to surrounding logs in the same query language. OpenTelemetry-native. Fully managed.
- [AI Engineering](https://axiom.co/use-cases/ai-engineering): Trace multi-step agent workflows end to end. Track cost, tokens, and latency by model and provider. Query the same APL your engineers use for system observability. Native MCP so your agents can query the data, too.
## Compare Axiom
- [Elastic](https://axiom.co/compare-to/elastic): Elastic has built a broad platform for search, observability, and SIEM. Axiom is a modern machine data platform purpose-built for logs, events, traces, and metrics: schema-less ingest, APL, and usage-based pricing, fully managed, with no clusters to size or shards to balance.
- [ClickHouse](https://axiom.co/compare-to/clickhouse): ClickHouse is one of the fastest analytical databases ever built, and a superb foundation if you want to build your own observability platform. Axiom is that platform, already built: a fully managed event store, purpose-built for machine and event data, with APL and usage-based pricing. The real question isn’t the engine, it’s whether you want to operate one.
- [Datadog](https://axiom.co/compare-to/datadog): Datadog is the broadest full-stack observability suite on the market: APM, RUM, synthetics, security, and 900+ integrations in one place. Axiom is the modern machine data platform for teams whose logs and events have outgrown Datadog’s indexing economics: keep every byte, query all of it, and pay on one predictable dial.
- [Splunk](https://axiom.co/compare-to/splunk): Splunk pioneered the machine data platform and still runs some of the largest security operations on earth. Axiom rebuilt that platform for the volume modern, AI-driven systems generate: keep every log, event, trace, and metric at any scale, on usage-based pricing you can predict, with the power-user query experience Splunk made famous.
## Solutions
- [Enterprise](https://axiom.co/solutions/enterprise): Keep every byte at any scale, with usage-based pricing your procurement team can verify. Adopt incrementally — on your timeline, not your current provider's renewal cycle.
- [Startups](https://axiom.co/solutions/startups): For Platform and SRE engineers at hypergrowth software and AI-native teams running multi-TB/day. Always Free to start, usage-based scaling with automatic volume discounts, fully managed so you're not running another cluster.
## Pricing & company
- [Pricing](https://axiom.co/pricing): Usage-based pricing with automatic volume discounts as you scale. Always Free allowances on every plan. Enterprise features unlock in-console, not on a sales call.
- [vercel](https://axiom.co/vercel): Request logging, function performance, web vitals, and product insights for the serverless era.
- [support](https://axiom.co/support): Start in the docs, ask the community, or open a ticket. Enterprise plans add a private channel and an SLA. However you reach us, you talk to engineers who know the platform.
- [security](https://axiom.co/security): Axiom is built for teams that keep everything, so security is not a tier you buy later. Encryption, access control, audit, and compliance are part of the platform — and your data stays in your control.
- [company](https://axiom.co/company): All your event data. All the time. All in one place. For every possible need or desire.
- [contact](https://axiom.co/contact): Sales, support, security, or press — reach the right team below, or send a note and we will route it.
- [demo](https://axiom.co/demo): A 30-minute walkthrough with an engineer. Bring a real workload and we will show you what it looks like at full fidelity, in APL.
## Docs
- [Documentation](https://axiom.co/docs): Product documentation — getting started, ingest, query (APL), dashboards, monitors, integrations, API reference.
- [Agent-created organizations](https://axiom.co/docs/console/intelligence/agent-created-orgs.md): This page explains how AI agents can provision a temporary Axiom organization with a single API request, and how a human can later claim the organization and keep everything the agent built.
## Customer stories
- [Unkey](https://axiom.co/customers/unkey): The CEO made dashboards for business health stats that VCs want.
- [Cal.com](https://axiom.co/customers/cal-com): Cal.com’s CEO sees his engineers dive into logs far more now that they’ve switched to Axiom.
- [Audius](https://axiom.co/customers/audius): Blockchain-based music marketplace Audius uses Axiom to track terabytes of telemetry and history data across their distributed network.
- [Hypermode](https://axiom.co/customers/hypermode): As a new startup, Hypermode had neither the time nor money to provide fully-featured logging to their engineers and customers .. until they found Axiom.
- [Salad](https://axiom.co/customers/salad): The world’s largest distributed cloud uses Axiom to let its customers see and analyze their own logs.
- [Hapn](https://axiom.co/customers/hapn): CloudWatch cost too much. A cheaper competitor spiked their bill. But Axiom keeps 100% of a year’s event data live and quite affordable.
- [Analogue](https://axiom.co/customers/analogue): Analogue makes consoles for vintage video games. Axiom lets them oversee all their infrastructure in one place.
- [Ruggable](https://axiom.co/customers/ruggable): Axiom makes Ruggable’s international e-commerce observable and optimizable, often without the need for a specialist’s time.
- [Plex](https://axiom.co/customers/plex): Plex is a streaming media platform that augments personal media collections with thousands of free, ad-supported movies and TV shows and 60 million high-definition music tracks from Tidal.
- [Speedscale](https://axiom.co/customers/speedscale): Speedscale is a Y-Combinator backed startup that helps Kubernetes engineering teams build more resilient and performant containerized apps.
- [Prisma](https://axiom.co/customers/prisma): Prisma is a real-time analytics platform that helps companies understand their customers and products. Prisma uses Axiom to scale their observability and data collection.
- [Parachute Home](https://axiom.co/customers/parachute): Parachute uses Axiom to analyze 100% of the events from their online stores, spotting problems and inspiring innovation.
## Blog (latest)
- [All posts](https://axiom.co/blog)
- [How many bitmaps does it take to beat a gorilla?](https://axiom.co/blog/how-many-bitmaps-does-it-take-to-beat-a-gorilla): Why we built our own caching and storage format.
- [Introducing Correlations: from symptom to system state](https://axiom.co/blog/correlations): Connect logs, traces, and metrics so investigations can move from the first clue to the surrounding system state without manual query stitching.
- [Measure less, learn more: Open-source documentation observability from Axiom](https://axiom.co/blog/do11y): Most analytics tools tell you how docs pages are consumed. They rarely tell you whether they're effective. Do11y is our open-source attempt to close that gap by treating documentation as instrumented software.
- [The humble placeholder: A small documentation problem worth fixing](https://axiom.co/blog/placeholder-configurator): Axiom is simple to get started with. But there is one small, reliable stumbling point for new users: placeholder replacement in code examples. We built a small tool to fix it.
- [Dynamic subrings: Consistent hashing without the tradeoff](https://axiom.co/blog/dynamic-subrings): How dynamic subrings let MetricsDB balance writes, reads, and resilience without compromise.
- [The Sortable interface: Teaching every column type to sort itself](https://axiom.co/blog/sortable-interface): Replacing a one-size-fits-all sort with column-aware algorithms delivered speedups ranging from 2x to 26x, without changing a single query.
- [Data residency without compromise: introducing Axiom's new edge architecture](https://axiom.co/blog/axiom-edge): Axiom now runs on an edge-based architecture. Data is ingested and queried locally at your chosen edge deployment, while a single global control plane handles auth, billing, and routing. One login, one bill, and your data where it needs to be.
- [From cost center to strategic asset: How Monks transformed observability with Axiom](https://axiom.co/blog/monks-three-tree-tech): Global digital services company Monks cut observability costs by 40%, eliminated security blind spots, and unlocked AI readiness, guided by strategic technology advisor Three Tree Tech.
- [Metrics are generally available](https://axiom.co/blog/metrics-mpl): Metrics are now generally available. Hyper-cardinality, unified with logs and traces, and fully queryable by AI agents through MCP and a dedicated metrics skill.
- [Find what's failing and why: review and issues for AI capabilities](https://axiom.co/blog/ai-engineering-review-workflow): Review AI traces with your team. Unstructured feedback becomes categorized issues you can investigate and resolve.
## Changelog (latest)
- [All entries](https://axiom.co/changelog)
- [APL and MPL in the Grafana data source](https://axiom.co/changelog/apl-and-mpl-in-the-grafana-data-source): Query Axiom from Grafana in both APL and MPL, four new weighted aggregations in APL, and monitor management from your AI agent.
- [Correlations](https://axiom.co/changelog/correlations): Connect logs, traces, and metrics datasets so investigations can move from the first clue to the surrounding system state without manual query stitching.
- [Metrics are now generally available](https://axiom.co/changelog/metrics-mpl)
- [Dashboards API](https://axiom.co/changelog/dashboards-api)
- [Write Evaluations skill for AI agents](https://axiom.co/changelog/write-evals-skill)
- [Online evaluations for AI engineering](https://axiom.co/changelog/online-evaluations)
- [Query metrics skill](https://axiom.co/changelog/query-metrics-skill)
- [Field sorting by frequency, improved APL autocompletions, and better API token management](https://axiom.co/changelog/field-sorting)
- [User feedback for AI engineering, metrics dashboard generation, and export enhancements](https://axiom.co/changelog/user-feedback)
- [Canvas and SRE skill](https://axiom.co/changelog/canvas)
