ACTIVE MONITORING
Synthetic testing that goes outside in, inside out, and everywhere between.
Legacy tools test from the cloud down. Parlon’s active monitoring runs synthetic tests from outside your network, inside your network, and between every location you operate, unified with continuous telemetry in a single native system.
< 15 min
Time to first synthetic check, any deployment mode
~70%
Reduction in alert noise via Alert Auto-Tune™
Multiple
Check types, including MCP for AI/LLM workflows
Day 1
Air-gapped and on-premises deployment. In production today.
THE COVERAGE GAP
It’s 2:47am. A branch office loses 30% packet loss on the path to your data center. Your cloud-only monitoring tool shows green. Your users won’t know until 9am.
Legacy synthetic tools test from cloud regions down to your endpoints. That’s outside-in coverage, and it matters. But it misses what’s happening on the paths inside your network and between your locations. Parlon runs both: the same outside-in tests your team already understands, plus synthetic tests from behind the firewall and probe-to-probe between every site. That’s what active monitoring means: full-spectrum coverage.
LEGACY SYNTHETIC MONITORING
Outside-in only
- Probes deployed in cloud regions, not behind your firewall
- Tests external endpoint availability; misses internal path degradation
- No probe-to-probe monitoring between sites or locations
- Separate modules for synthetics and telemetry: two panes of glass
- Alert volume with no ML learning: teams stop trusting the tooling
PARLON ACTIVE MONITORING
Outside-in and inside-out
- Deploy behind the firewall, at branch sites, or in air-gapped environments
- Continuous probe-to-probe monitoring between every location
- Synthetics and telemetry in a single native system: one pane of glass
- Alert Auto-Tune™ learns your environment and cuts noise by ~70%
- 10+ check types including MCP for AI and LLM workflows
WHY IT'S DIFFERENT
Not a cloud-only synthetic tool with an enterprise sticker.
Legacy synthetic monitoring was built when your entire network was public. Outside-in testing from cloud regions was sufficient because that’s where everything lived. Networks changed. The architecture didn’t. Probes still live exclusively in the cloud, telemetry still lives in a separate product, and correlation still happens after the fact.
Parlon runs the outside-in tests too, and adds what legacy tools structurally cannot: synthetic tests from inside your network, probe-to-probe visibility between every location, and a native unification of synthetic testing with continuous telemetry. One data model. No context switching. Not features you can bolt onto a 20-year-old codebase.
| Capability | Legacy | Parlon |
|---|---|---|
| Probes behind the firewall | ✕ | ✓ Docker or K8s, no inbound ports |
| Probe-to-probe monitoring | ✕ | ✓ RTT, jitter, packet loss, PBD |
| Air-gapped deployment | ✕ | ✓ SSH-pull, in production today |
| Synthetics + telemetry, native | ✕ | ✓ One platform, one data model |
| ML-based alert noise reduction | ✕ | ✓ ~70% noise cut, confirmed |
| MCP / LLM workflow testing | ✕ | ✓ Native, not a plugin |
CHECK TYPES
Every check type your stack needs. Including the one your current tool skipped.
Ten check types cover the full surface area of modern infrastructure, from the TCP port to the AI inference endpoint. Each type runs on every probe model: global, local, edge, and air-gapped.
If you’re running AI workloads, the MCP check type validates Model Context Protocol endpoints end-to-end: context integrity, response timing, and silent failure detection. No other platform has this natively.
HTTP/S
Availability, latency, status codes, TTFB
ICMP
Ping, reachability, RTT
TCP
Port connectivity and connection timing
DNS
Resolution accuracy and failure detection
SSL/TLS
Certificate validity, expiry, handshake
WebSocket
Persistent connection health and round-trip
Browser
Full Playwright end-to-end user journeys
Database
Query latency and connectivity
Redis
Cache reachability and command timing
MCP
Model Context Protocol for AI/LLM workflows
Round-Trip Time
Continuous latency measurement between every probe pair. Baseline drift is detected before it crosses a threshold you'd notice.
Latency Variance
Critical for real-time applications, voice, and video. Jitter trends surface routing instability before it impacts users.
Per-Hop Loss Detection
Loss is pinpointed at each network hop, not just "somewhere on the path." You know if it's your network, your ISP, or your cloud provider.
Path Bandwidth Discovery
Active bandwidth measurement between locations. Know your actual available capacity, not just what your contract says.
BUILT FOR THE AI ERA
Your AI workloads are running on your network. Your monitoring platform has no idea.
47.7% of enterprises already have AI training or inference workloads deployed. Legacy synthetic tools were built before LLMs existed. They test HTTP endpoints, not model behavior.
Parlon’s MCP check type actively validates Model Context Protocol endpoints end-to-end. Not the infrastructure underneath them. The actual AI workflow: context integrity, input reliability, response timing, and silent failure patterns that no legacy tool ever surfaces.
47.7%
of enterprises already have AI training or inference workloads on their networks. Another 36.6% will deploy within 12 months. Only 35% believe their current tools are ready to manage AI network performance. (EMA 2026)
MCP Endpoint Validation
Actively tests Model Context Protocol services, validating context, inputs, and outputs flowing to and from LLM-based systems. Detects latency spikes, context errors, and response drift before users encounter them.
LLM Workflow Testing
Tests entire AI workflows, not just the infrastructure layer. Identifies where in the chain a failure occurs: whether that’s your network, your model endpoint, or your context pipeline.
GPU and Inference Path Visibility
Parlon’s ML Analytics module correlates network behavior with GPU utilization and inference latency, surfacing whether network congestion is affecting model performance.
MCP for Agentic Access
31% of organizations prioritize MCP support for agentic access and integration (EMA 2026). Parlon supports MCP as both a synthetic check type and as an API interface for AI agents.
CUSTOMER RESULTS
“We found Parlon’s capabilities at parity with the legacy vendors, and the simplicity of deployment and the cost were a significant value in themselves.”
Network Infrastructure Lead, National Health System
Healthcare. Regulated. Air-gapped. 1,000+ clinic locations. Needed it to work from Day 1. And it did.
~50%
Reduction in total observability spend
1,000+
Clinic locations under management
2 → 1
Vendors consolidated into one platform
Days
Time to value in a fully air-gapped environment
ALERT AUTO-TUNE™
The noise problem is architectural.
So is the fix.
Static thresholds fire on anything that crosses a line. That’s not intelligence. It’s a spreadsheet. 67% of network observability alerts are noise (EMA 2026), and teams have adapted by ignoring them. Which means real problems get missed.
Alert Auto-Tune™ learns your environment’s normal, with baselines built from your actual telemetry, not industry defaults, applying ML-based anomaly detection, change-point analysis, and drift detection to produce alerts worth acting on.
1. Learns Your Baseline
Analyzes historical alerts and normalized telemetry to understand what normal looks like for your specific environment. Adapts as infrastructure changes.
2. Recommends Smarter Thresholds
AI-generated threshold recommendations suppress low-impact noise while preserving real operational risk. No manual tuning cycles required.
3. Correlates and De-duplicates
Related alerts from the same root cause are consolidated into a single escalating stream. One alert per incident, not one hundred.
4. Keeps Getting Better
Re-tunes continuously as your environment evolves and as fixes are applied, so the floor keeps dropping, not just week one.
Network Infrastructure Lead, National Health System
~70%
Reduction in alert noise. Confirmed with live customers in production.
~67%
Of network observability alerts are noise, according to EMA's 2026 Network Management Megatrends research.
30–50%
Reduction in mean time to resolution across Parlon customer deployments.
DEPLOY ANYWHERE
Deploy the way your security team requires, not the way your vendor allows.
All four deployment modes are feature-equivalent. No capability tiers. No “enterprise add-on” for on-premises. The platform you evaluate is the platform you deploy.
MOST COMMON
SaaS
Hosted on Parlon’s cloud infrastructure. Fastest path to value. No infrastructure to manage. Updates happen automatically.
HIGH COMPLIANCE
On-Premises
Fully installed in your own infrastructure via Docker or Kubernetes. Complete data residency control. HIPAA-ready.
FLEXIBLE
Hybrid
Cloud-managed control plane with on-premises data collection. SaaS simplicity, on-premises data locality. Common for regulated industries.
MAXIMUM SECURITY
Air-Gapped
Fully isolated via SSH-pull architecture. No external connectivity required. Regulated environments supported.
GET STARTED
See it in the context of your environment.
We’ll walk through the platform against your specific infrastructure and show you what the TCO looks like against what you’re running today.