SOLUTIONS / 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.
WHY YOUR CURRENT STACK CAN'T KEEP UP
Every incumbent observability stack hits a wall at modern data volumes — on cost, on coverage, or on the team it takes to run it.
30+ SKUs and Standard Indexing that gates visibility itself: logs you load show up in Live Tail but don't appear in Log Explorer unless you pay to index them. Traces drop fields at cardinality limits. Metrics priced per custom dimension. End-of-quarter bill shock is the recurring objection.
Index-at-ingest architecture made sense when query speed was the bottleneck. It doesn't when data volume is the bottleneck and licensing is priced against it. Splunk vacated the data-tool category for security; the core platform hasn't moved in years.
Cost control through DIY operations: capacity planning, query-node provisioning, schema decisions, on-call rotation for the observability stack itself. The bill arrives in engineer-hours.
Per-user pricing made onboarding the whole engineering org a budget conversation. The data-plus-user model still pulls in two directions during scaling.
Adequate for AWS-native at lower volumes. Query depth, dashboarding, and cross-account analytics fall behind at multi-TB/day, and pricing isn't built for ingest-heavy patterns.
HOW AXIOM SOLVES IT
ONE STORE, EVERY SIGNAL
Axiom unifies logs, events, traces, and metrics on a single event store. EventDB is petabyte-scale, schema-on-read, with 95%+ compression. MetricsDB handles high-cardinality time-series natively, without the cost penalties traditional metrics systems impose. Every byte loaded is queryable in APL on day one — no second SKU to unlock search.
TRACING AT SCALE
Distributed tracing keeps every span at scale, with over a million spans rendered in a single trace view in production. Logs, events, and traces query in APL; metrics query in MPL. Pivot from a span to the logs around it in one query, no join required.
ONE USAGE-BASED DIAL
Pricing is usage-based with in-console volume discounts that drop sub-linearly. The platform is fully managed and OpenTelemetry-native, including OTel Metrics now GA — so you can move every signal off your current vendor on the same usage-based dial.
CUSTOMER HIGHLIGHT
“We chose Axiom because it's the only product out there that allows me to affordably see all my logs for a year. And it's very good at that.”
BACKGROUND
Asset-tracking on AWS Lambda.
PROBLEM
Year retention priced out elsewhere.
WHY AXIOM
Managed event store, sub-linear pricing.
IMPLEMENTATION
Lambda logs via AWS integration.
RESULTS
Year retention, 4B messages queryable.
PARTNERSHIP
Long-standing, expanding with growth.
WHAT CHANGES FOR EACH ROLE
FOR ENGINEERS
Stop sampling and choosing between signals. Logs, traces, metrics, and events live on one event store, queryable in APL on day one. Pivot from a slow span to surrounding logs without a join.
FOR ENGINEERING LEADERS
Consolidate logs, traces, and metrics on one usage-based dial. End-of-quarter bill shock turns into a forecast. Forecast accuracy moves into single digits, not the 30% uncertainty of an annual renegotiation.
FOR PLATFORM TEAMS
Stop running the observability stack itself. Keep query power (APL), schema flexibility (schema-less ingestion, virtual fields), and portability (OTel-native) without the operational drag of capacity planning, query-node provisioning, and on-call for the platform underneath.
WHAT YOU'LL USE
EventDB — petabyte-scale, schema-on-read event store with 95%+ compression
Distributed tracing — zero sampling, over 1 million spans in a single trace view
Serverless query compute — scales on demand, no idle capacity to provision
Integrations — Vercel, Cloudflare, AWS, Kubernetes, Cribl, the Splunk App
HOW THIS COMPARES
FAQ
PLATFORM AND SCALE
Yes. EventDB holds logs, traces, and events; MetricsDB handles high-cardinality time-series. APL queries logs, events, and traces; MPL queries metrics. The Axiom Console renders them together.
No sampling decisions at ingest. Distributed tracing in Axiom renders over a million spans in a single trace view in production. The waterfall is complete during the incident.
PRICING AND MIGRATION
Sub-linear. Per-unit rates drop at higher tiers, and there's no overage SKU to trip into.
Yes. Most teams start with one workload, model the cost and performance, and expand at the renewal seam. The Splunk App accelerates orphan-workload landings.
INTEGRATIONS AND STANDARDS
Yes, OTel-native, including OTel Metrics now GA. Move logs, traces, and metrics off your current vendor on the same dial.
Vercel, Cloudflare, AWS, Netlify, Kubernetes, Cribl, the Splunk App, and any system that can POST JSON to the Events API.
DATA AND ACCESS
Edge architecture for data residency plus write-side multi-region from a single Axiom organization. Not automatic cross-region failover.
Through the native MCP server, in APL. Same primitives engineers use. SRE skill ships with the server.
Always free to start. Usage-based scaling. Fully managed.