SOLUTIONS / 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.
WHY YOUR CURRENT EVENT-DATA STACK CAN'T KEEP UP AT SCALE
Every way teams stand up custom event streams at hyperscale forks the data, hires a platform team, or prices itself out. Here's where each one lands.
Analytics warehouses tuned for batch. Real-time event query at petabyte-daily ingest pushes them off their performance curve, so teams reserve them for offline analytics and route operational queries elsewhere. The data ends up forked.
Real-time at PB scale is possible, but you become the platform team for it: NVMe sizing, S3 tiering, schema migrations, Kafka buffering, query-node provisioning at 3am. The bill is in engineer-hours rather than a vendor line item.
Wrong fit at large scale. Standard Indexing or licensing ceilings turn every new event source into a budget conversation, and neither platform was priced for petabyte-daily ingest in the first place.
Most hyperscale teams end up here by attrition. Kafka pipelines, cold storage in S3, custom query layers, and a tribe of engineers who own it. Works for a while. Tax on every new use case after that.
The default end state. Inference events go one place, embedding metadata another, product events a third, audit streams a fourth. Every team writes a bridge. Nothing connects.
HOW AXIOM SOLVES IT
ONE BACKEND
Axiom is built for the volumes hyperscale teams actually produce. EventDB is petabyte-scale, schema-on-read, with 95%+ compression on real workloads. Inference events, embedding metadata, product events, and audit streams land on one backend — no fork per team.
NO MIGRATION
Custom schemas land through the Events API and SDKs, beyond OTel-shaped data. Schema-less ingestion onboards new event types without a migration. Query compute is serverless and scales on demand, so the cost of asking the question only shows up when you ask. APL handles logs, events, and traces; MPL handles metrics. APL is faster than SQL for sequential log-style queries at scale.
FULLY MANAGED
The platform is fully managed: no clusters to size, no on-call rotation for the event-data infrastructure itself. Edge architecture handles data residency and write-side multi-region from a single Axiom organization. AI agents query the event store through the native MCP server using the same APL primitives engineers use.
CUSTOMER HIGHLIGHT
“We set up Axiom so we could troubleshoot our customers, and that evolved into, ‘What if they didn’t have to ask us about it?’”
BACKGROUND
AI compute on consumer GPUs.
PROBLEM
Per-run customer visibility, affordably.
WHY AXIOM
Managed event store, per-tenant APL.
IMPLEMENTATION
Per-customer datasets via Events API.
RESULTS
Customers self-serve their own runs.
PARTNERSHIP
Ongoing as network scales.
WHAT CHANGES FOR EACH ROLE
FOR PLATFORM AND INFRA ENGINEERS
Stop maintaining five places where event data lives. One event store, one set of credentials, custom schemas onboarded through the Events API without a migration.
FOR ENGINEERING LEADERS
Stop building another in-house event platform. Hyperscale event-data infrastructure as a managed service, with a usage-based bill that grows sub-linearly with volume.
FOR AI AND DATA TEAMS
Inference events, embedding metadata, eval runs, and product events on one backbone. AI agents query them through the native MCP server in APL and MPL.
WHAT YOU'LL USE
Events API and SDKs — arbitrary event schemas at high cardinality, beyond OTel-shaped data
EventDB — petabyte-scale, schema-on-read event store with 95%+ compression
Serverless query compute — scales on demand, no idle capacity to provision
Schema-less ingestion — new event types onboard without a migration
Edge architecture — data residency and write-side multi-region from one Axiom org
CLI and IaC tooling — codify event-data infrastructure alongside the rest of your stack
BYOB available as a supported capability
HOW THIS COMPARES
FAQ
SCHEMA AND CARDINALITY
No. Ingestion is schema-on-read. Send any JSON-shaped event through the Events API and query it in APL immediately. New fields appear as queryable columns without a migration.
None for events; the schema-less store handles wide schemas natively. MetricsDB handles high-cardinality time-series without the penalties traditional metrics systems impose.
PRICING AND MIGRATION
Sub-linear. Per-unit rates drop at higher volume tiers, and there's no overage SKU to trip into. See /pricing for current rates.
Yes. Run them in parallel as long as you need. Axiom shows up as a small usage-based line item on the workloads you route to it, then grows on usage merit.
DEPLOYMENT AND RESIDENCY
Yes, as a supported capability. For most teams the operational drag rarely outweighs the few percent of total cost it controls.
Edge architecture for data residency plus write-side multi-region from one Axiom organization. Not automatic cross-region failover.
QUERY AND INTEGRATIONS
Through the native MCP server, in APL. Same primitives engineers use. No glue code or per-vendor adapter.
OpenTelemetry, AWS, Cloudflare, Vercel, Netlify, Kubernetes, Cribl, and any system that can POST JSON to the Events API.
Always free to start. Usage-based scaling. Fully managed.