APL (Axiom Processing Language)
The piped, sequential query language. Designed for power users composing analytics pipelines rather than clicking faceted filters.
A MODERN, MANAGED MACHINE DATA PLATFORM
What the Splunk era couldn't build
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.
Companies powered by Axiom
Power users, picking it up in an afternoon.
Dashboards in days, not weeks
Built custom dashboards on production data in days. Pipeline composition felt like SPL they'd graduated to.
A year of Lambda logs, queryable
Queries a year of AWS Lambda logs — 4 billion messages — in APL, with no sampling and no rehydration step.
Vercel-edge visibility in days
Stood up edge-function visibility across the Vercel fleet in days, querying every request in APL.
ARCHITECTURE
How it works under the hood.
Compute and storage, fully disaggregated. Events land schema-less at the edge, compress into columnar object storage, and query-time workers read only the columns they need — so you pay for compute only while a query runs.
A single backend service can produce multiple terabytes per day. AI workloads explode cardinality with prompt IDs, model versions, embedding vectors. The category Splunk pioneered still describes the data, it just needs a platform built for the volumes it's now reaching.
TECHNICAL DEEP DIVES
The pieces, in detail.
Splunk App
Drop-in onboarding for teams moving off Splunk without renegotiating their main contract.
MCP server + SRE skill + Metrics skill
Native MCP server. Your AI agents query Axiom in APL, the same way your engineers do.
Events API + SDKs
Write arbitrary high-cardinality event schemas at any scale, beyond OTel.
Edge deployment
Storage distributed across edge regions for data residency and write-side multi-region resilience.
Schema-less ingestion + Virtual fields
Shape of data decided at query time, not at the pipeline. Virtual fields reshape on read.
“Really appreciate the way Axiom integrates logs, metrics, and traces into a single unified dataset. UI is responsive and modern.”
— Hydroxygen Labs
FAQ
What engineers ask first.
ARCHITECTURE AND COMPLIANCE
They built when data volumes were 100x smaller. Indexing every byte (Splunk) or gating visibility through SKUs (Datadog) was the right call then; unwinding it now means rewriting the storage engine and the pricing model at the same time.
Edge architecture handles the write side: data residency and write-side multi-region from a single Axiom organization, so ingest stays durable through a regional event. It doesn't automatically fail over query reads; if a region is down, queries against that region pause until it recovers.
SOC 2 Type II, GDPR-compliant, HIPAA BAA on every plan (not a separate tier). Full sub-processor list and certifications in the Trust Center.
PRICING AND SCALE
No catch. One dial (bytes ingested) with automatic volume discounts. No SKU stair-steps, no overage tier to trip into; set spend alerts and hard caps in-console, or query your bill in APL the same way you'd query latency.
Ingest scales automatically on the same architecture, no migration; total cost grows sub-linearly as per-unit rates drop at higher tiers, and most teams at this scale pre-purchase compute credits for deeper discounts.
MIGRATION
Piped and sequential like SPL and KQL, schema-less unlike SQL. The closest in-class experience for Splunk muscle memory; try a side-by-side in the APL Playground, no account.
Land beside Splunk on orphan workloads via the Splunk App (in preview): dual-write, prove the model, expand at the renewal seam. We have multiple service partners in the US and EU who can assist as well.
Petabyte-scale machine data, on a bill you can predict.
Petabyte-scale ingest. Pricing you can predict.