Visibility shrinks as you grow.
The more data your systems produce, the more you sample, drop, or aggregate. The system gets less useful exactly as your workloads get more complex and your incidents get harder to diagnose.
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.
Your old stack is broken by design
What worked at terabytes doesn't 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. The team managing the stack keeps growing alongside the data.
Cost models break at scale.
Bundled SKUs, indexing tiers that gate visibility, annual renegotiation theater. Finance can't forecast next quarter. Engineers self-censor instrumentation to keep the bill survivable.
The operational surface keeps growing.
Self-managed clusters mean schema migrations, capacity planning, and someone on call for the observability stack itself. The team you hired to ship product spends its time keeping the pipeline alive.
Keep every byte
No data left behind.
Petabyte-scale schema-less ingest on a fully managed event store with 95%+ compression. Keep every log, every dimension, for as long as you want, without standing up a self-managed cluster to do it.
Stream-oriented, schema-on-read architecture. Virtual fields for query-time transformation. No indexes to manage, no ingest-time schema decisions to regret six months later.
PRICING THAT SCALES
Predict every bill.
Pay only for what you use. Automatic volume discounts in-console. Permanent free tier. Self-serve enterprise add-ons. Per-unit rates drop at higher tiers, so total cost grows sub-linearly with usage.
One usage-based dial across logs, traces, metrics, and events. No SKU stair-steps. No overage tier. No 30-SKU pricing matrix to decode.
“Great product, I see you as the clear market leaders. Not old and bloated like some others. Just enough power while remaining simple.”
— Claras.ai
Queries built for engineers
Modern backend. Classic UX.
APL (piped, sequential, log-friendly) on a purpose-built event store. MPL (same style, built for metrics) on a purpose-built metrics engine. The query experience power users loved about Splunk, on infrastructure modern systems can actually afford to feed. Fully managed.
Sequential processing built into both languages. AI agents use the same APL and MPL primitives because the languages were designed for it. Schema-less ingestion plus virtual fields preserve data-model flexibility without giving up query power.
Land beside Splunk
No rip-and-replace. No renegotiation.
Full Splunk migrations are 18-month projects nobody approves. Axiom shows up beside Splunk via the Splunk App on orphan workloads first (dev, staging, marketing event data), without procurement renegotiating the existing contract. Engineering proves the model, then you expand at the renewal seam.
4Data, our EMEA services partner, supports migrations end-to-end where you want it.
AI-queryable by default
Built for engineers and their agents.
AI agents are showing up in the on-call rotation, the eval pipeline, the analytics workflow. They need to query observability data the way engineers do, without custom glue code, without per-vendor adapters, without re-implementing the query layer for every tool.
Native MCP server. SRE skill teaches agents the patterns that move from hypothesis to proof. Metrics skill exposes high- cardinality metrics to agent reasoning in MPL. Every byte queryable in APL and MPL, the same primitives your engineers use.
Playground
Run a query. No account required.
No account required. Public dataset. See what schema-less plus pipes feels like.
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See how Axiom compares to leading observability platforms across performance, pricing, scalability, and ease of operation.
Keep every log. Predict every bill.
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