Stop Paying for Noise
The Challenge
IT operations teams rely on analytics tools, but they were never designed to ingest everything your infrastructure generates. If your collection infrastructure is built around a single vendor's tooling, switching platforms becomes a costly, high-risk project. As environments grow more complex, unchecked data volumes inflate costs, slow down queries, and bury the operational signals your teams need to act on.
Ingest costs that scale with complexity, not value
Every new service, container, or cloud workload adds means more logs, metrics, traces, and events, and most of it is repetitive, low-priority noise. Analytics platforms charge by ingest volume, so teams pay premium prices to store data that is never queried and adds no operational value.
No visibility into what's flowing or what it costs
Teams routinely discover, weeks later, that a critical server stopped sending data, a new source appeared unexpectedly, or a bottleneck formed upstream. The "collect everything, figure it out later" approach makes pipeline problems invisible until they become incidents.
Poor data quality degrades dashboards and alerts
Missing timestamps, inconsistent field names, malformed messages, and schema mismatches don't just slow down investigations. They break dashboards, generate false alerts, and undermine SLO tracking. Discarding bad data isn't the answer; it creates blind spots that quietly erode your operational visibility.
Operational context lost before data reaches your tools
Labels available at collection time are rarely attached to the data making root cause analysis almost impossible. By the time telemetry reaches your analytics platform, the context that makes it actionable for IT operations is already gone, leaving engineers to manually reconstruct it for every incident.
The Solution
Axoflow decouples your collection and routing layer from any single analytics destination, so your telemetry pipeline is yours to control. IT Operations teams get a structured, policy-driven approach to controlling what enters their analytics platforms, so tools receive the right data, in the right format, with the operational context already attached.
Automated Source Discovery
Axoflow automatically detects and classifies every telemetry source across your infrastructure, from Kubernetes pods and cloud services to legacy on-prem systems. You get a live inventory showing what's flowing, where it's going, and what it's costing your analytics platforms, with no manual configuration required per source.
Policy-Driven Filtering
Define what matters using organization-wide policies, not one-off scripts scattered across collectors. Axoflow filters repetitive and redundant messages, drops irrelevant events, and applies sampling to high-frequency sources, significantly reducing the volume sent to your analytics tools without removing coverage.
In-Flight Normalization
Inconsistent field names, mismatched schemas, and format variations are resolved before data reaches your analytics platform. Axoflow parses and normalizes telemetry in transit, including log classification for hundreds of commercial products, so dashboards, alerts, and correlation rules work reliably regardless of the source, lowering MTTR in root cause analysis.
Enrichment at Collection
Add Kubernetes labels, service ownership data, environment tags, cost center assignments, and trust scores as data is collected before it is sent to your analytics tools. Enriched data arrives ready for immediate operational use, reducing mean time to investigation and eliminating the manual lookup work that slows MTTR.
Integrations
Axoflow Platform currently has hundreds of application adapters for a range of data sources and destinations and we are working on adding new ones every day.
These zero-maintenance connectors enable Axoflow Platform to parse incoming data sources automatically and then transform them to the destination schema. Check out our documentation for the full list.
FAQs
If we filter data before it reaches our analytics tools, will we miss incidents?
No. When filtering is policy-driven rather than applied as blanket drops, all actionable signals are preserved. Axoflow eliminates redundancy and noise, not relevance. Your team defines what low-value looks like; the platform enforces it consistently across every collector.
How is this different from tuning our existing collector configurations?
Collector tuning is manual, invisible, and breaks down at scale. Axoflow provides automated source discovery, a centralized policy layer, live pipeline metrics, and continuous enforcement, none of which are possible with hand-edited configs distributed across hundreds of nodes.
Will this work alongside our existing analytics platforms?
Yes. Axoflow integrates with Splunk, Datadog, Dynatrace, and other major analytics platforms without requiring you to replace them. It acts as an intelligent pipeline layer that prepares and controls the data flowing into your existing tools.
Does it work with OpenTelemetry?
Yes. Axoflow is built on open standards including OpenTelemetry and syslog-ng, and integrates natively with OTel collectors and SDKs. It complements your existing OpenTelemetry investment rather than replacing it.
How does Axoflow pricing work?
Axoflow uses a transparent, linear pricing model instead of complex consumption-based licensing. When you reduce data volume with Axoflow, you see real, predictable savings—often up to 50%—rather than confusing tier jumps or hidden fees.
Can we keep full-fidelity data while still reducing what we send to analytics tools?
Yes. Axoflow routes reduced, high-value telemetry to your analytics platforms while simultaneously archiving full-fidelity data in cost-efficient object storage via AxoLake. You never lose data — you stop paying analytics platform prices to store data that belongs in a long-term archive.
Is cost reduction the primary benefit for IT operations teams?
Cost reduction is an outcome, not the goal. The goal is that your operations engineers have fast, reliable access to accurate, enriched telemetry when they need it — during an incident, an SLO review, or a capacity planning exercise. Eliminating waste from your analytics pipelines is what makes that possible.