THE PLATFORM
One platform.
Built from a different foundation.
Most infrastructure observability platforms ingest data and try to make sense of it later. Parlon normalizes at ingestion, actively validates real-world behavior, and learns what normal looks like, so your team acts on signals they actually trust.
~70%
Reduction in alert noise with Alert Auto-Tune™
30-50%
Reduction in MTTR across customer deployments
< 4 hrs
Time to first value; synthetics live same day
3–8×
Lower 3-year TCO vs. legacy monitoring platforms
CORE CAPABILITIES
Three capabilities. One architecture.
Parlon's infrastructure observability platform is built around three capabilities that work together, not three modules bolted onto a legacy data model.
Normalization Engine
Incoming telemetry from every vendor, protocol, and environment is mapped into a unified schema at ingestion. No custom parsers. No brittle correlation scripts. Clean, consistent, query-ready data from day one across infrastructure, applications, and AI workflows.
Active Synthetic Validation
Parlon doesn't wait for failures. LLM-aware synthetic tests continuously validate real paths, workflows, and endpoints, surfacing latency, failures, and routing instability before users feel the impact. Active first, passive telemetry as context.
Alert Auto-Tune™
According to EMA's 2026 Network Management Megatrends research, 67% of alerts generated by current monitoring tools are noise. Alert AutoTune™ was built to fix that.
CAPABILITY 01
Normalization Engine
Normalization is the foundation of effective observability. Everything else depends on it.
Modern monitoring stacks ingest massive volumes of telemetry, but each vendor, protocol, and environment describes the same things differently. The result is custom parsers, inconsistent metrics, and brittle dashboards that can’t compare cleanly across systems.
Parlon normalizes data as it enters the platform, not after the fact. Incoming metrics and events are mapped into a unified schema in real time, preserving vendor-specific detail while producing consistent, trustworthy signals at enterprise scale.
- Apples-to-apples visibility across vendors, environments, and cloud providers, without losing depth or context
- Higher-signal alerting with fewer false positives, normalized data produces cleaner inputs for Alert Auto-Tune™
- Built for scale and high cardinality without trading off detail vs. cost
- Trustworthy downstream use across SIEM, compliance, AI-powered analytics, and streaming exportList Item
Feature guide
Normalization — unified data across heterogeneous environments
How Parlon maps telemetry from 200+ vendors into a single schema at ingestion — and why that changes everything downstream.
Download PDF
Network Path Divergence Detection
Detects real-time path divergence even when availability appears healthy. Highlights routing instability caused by load balancing or upstream changes. Surfaces hidden latency risks that aggregate metrics miss entirely.
Traceroute & Latency Waterfall
Visualizes cumulative latency at each network hop. Pinpoints where latency is introduced — local network, backbone, or downstream provider. Accelerates root cause analysis for global and latency-sensitive applications.
HTTP Performance Breakdown
Breaks total request latency into DNS, TCP, TLS, server (TTFB), and download phases. Separates network, server, and client-side contributors. Enables faster isolation of whether slowness originates in infrastructure or application layers.
LLM Workflow Validation (MCP)
Actively validates Model Context Protocol endpoints, testing context integrity, input/output reliability, and response timing across AI inference paths. Detects model drift, latency spikes, and silent failures that no passive APM tool sees.
CAPABILITY 02
Active Synthetic Validation
Legacy tools wait for something to break. Parlon continuously validates real paths, workflows, and AI interactions — surfacing routing instability, latent latency, and drift that metrics-only platforms never see.
Critically, Parlon’s synthetics are LLM-aware. They test workflows, not just endpoints, validating the context, inputs, and responses flowing to and from AI systems, including Model Context Protocol (MCP) services. This is the visibility gap every other platform leaves open.
AI workloads introduce failure modes that legacy tools were never designed to see. Only active, behavior-first observability can catch them.
Solution guide
Active Monitoring — inside-out and outside-in synthetic testing
10+ check types across HTTP/S, DNS, browser, database, and MCP — deployed from global probes or from inside your firewall, in one platform.
Download PDFCAPABILITY 03
Alert Auto-Tune™
Normalization is the foundation of effective observability. Everything else depends on it.
Modern observability tools overwhelm teams with alerts. Static thresholds generate floods of repetitive notifications tied to the same conditions, often without context or clear severity. The result: teams stop trusting their own tooling.
Alert Auto-Tune transforms alerting from a noisy byproduct into an intelligent signal layer. Instead of firing on static rules, it learns system behavior over time, ensuring teams see fewer alerts, with far greater confidence and clarity.
Operational context added to every alert
ENVIRONMENT
BUSINESS IMPORTANCE
NETWORK ZONE
TENANT / CUSTOMER
Feature guide
Alert Auto Tune — reducing noise and surfacing what matters
How Alert Auto-Tune learns your environment, suppresses redundant events, and cuts alert volume by ~70%.
Download PDFLearns baselines automatically
Analyzes historical alerts and normalized telemetry to understand what "normal" looks like for your specific environment — adapting continuously as infrastructure evolves.
Recommends smarter thresholds
AI-recommended policy adjustments that suppress low-impact noise while preserving real operational risk — no manual tuning required.
Correlates and de-duplicates
Consolidates related alerts into a single escalating stream, eliminating repetitive notifications from the same root cause.
Adds operational context
Enriches every alert with environment, business importance, network zone, and tenant metadata, so teams know immediately what matters and why.
Continuously improves
Re-tunes alerting as fixes are applied, helping teams uncover the next meaningful issues rather than re-alerting on resolved conditions.
~70%
Reduction in alert noise
Higher
Team trust in alerts
TECHNICAL ARCHITECTURE
Built for engineers who care how it works.
Parlon is a modular Built for modern infrastructure observability platform with well-defined layers. Each layer is independently scalable. The full technical guide is available for download.
Core Architectural Principles
Active-first observability
Synthetic validation augments passive telemetry, not the other way around. Active testing runs continuously, not on-demand.
Normalized data fabric
Telemetry is structured and contextualized at ingestion. Every downstream use (alerting, AI, export) runs on the same trusted data model.
Streaming by default
Data is available in real time for alerting, ML pipelines, and export. No batch delays. No stale dashboards.
Open by design
REST API, streaming export, vendor-agnostic collection. Designed for tool consolidation, not lock-in. Your data goes where you need it.
Streaming by default. Data is available in real time for alerting, AI and ML pipelines, and export — not batch-processed after the fact. Built on Apache Kafka for event streaming, VictoriaMetrics for time-series, Redis 7 for cache and queue, and PostgreSQL 16 for configuration and state.
DEPLOYMENT OPTIONS
Deploy the way your security team requires.
Parlon runs wherever your environment demands, without compromising on capability. The Parlon infrastructure observability platform is fully feature-equivalent across all deployment modes.
SaaS
Hosted on Parlon's cloud infrastructure (AWS). Fastest path to value. No infrastructure to manage. Automatic updates.
MOST COMMON
On-Premise
Fully installed within your own infrastructure. Complete data residency control. Suitable for regulated environments without strict air-gap requirements.
HIGH COMPLIANCE
Air-Gapped
Fully isolated deployment with no external connectivity. Meets the strictest data residency and security requirements. HIPAA, FedRAMP-aligned environments supported.
MAXIMUM SECURITY
UNIQUE ADVANTAGE
MSP / Multi-Tenant
Purpose-built for managed service providers. Role-based access, tenant isolation, white-labeling support. Manage multiple clients from a single pane of glass.
MSP-READY
All deployment modes are feature-equivalent. The platform you evaluate in SaaS is the same platform you deploy on-premise.
CUSTOMER RESULTS
In production. Proven.
One of our earliest customers. Healthcare. Regulated. Air-gapped. 1,000+ locations. They needed it to work from day one, and it did.
