From early-stage startups to platform teams — one OpenTelemetry-native platform for logs, metrics, traces, and AI workloads. Every capability below ships in the product today.
A platform team running separate tools for logs, metrics, and traces points its existing OpenTelemetry Collector and Prometheus remote-write at aiAxonIQ. Instrumentation stays untouched — only the exporter endpoint changes. Logs, metrics, and traces land in one place, correlated by the same resource attributes.
Latency spikes in a payment flow get traced span-by-span across services, with one click from the slow trace to the exact logs it produced. Root cause stops being a guessing game.
Every LLM call is tracked for token cost, latency, and model breakdown via OpenLLMetry-compatible instrumentation — so an expensive prompt shows up in a dashboard, not on next month's invoice.
SLOs with error budgets and multi-window burn-rate alerts page the on-call only when the budget is actually at risk — not for every transient blip. AI-assisted authoring helps write the SLO definitions.
An existing Prometheus setup is pointed at aiAxonIQ via remote-write — no agent changes, no re-instrumentation. Dashboards and alert rules keep working against the same metric names.
A small team instruments its first three services on the free tier — no credit card — and upgrades to Growth at $49/month when it scales. Pricing you can predict, not a usage bill you dread.
A team that needs telemetry to stay inside its own network deploys the full platform with Docker Compose or Kubernetes. Open standards mean the same instrumentation works either way.
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