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Technical comparison

Selector vs. SolarWinds

Observability and Event Intelligence Use Cases

October 2026

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How each platform builds operational context

Selector collects raw telemetry and operational context together from every vendor, normalizes and enriches it before any model runs, and establishes causation with purpose-built ML before agents act. SolarWinds delivers observability through a portfolio of products on the SolarWinds Platform, including Network Performance Monitor, NetFlow Traffic Analyzer, Network Configuration Manager, and Server and Application Monitor, available self-hosted or as SaaS. Incident Response, the AI Agent, SW1, and the new Avior agentic system add correlation and action on top.

Foundry agents

Rosetta, Loom, TicketMaster, Artist, Herald, Sculptor; MCP and A2A.

Collaboration

Natural language, dashboards, ChatOps, ITSM workflows, query-backed APIs.

Knowledge

Anomaly detection, correlation, causation, forecasting, knowledge graph.

Data Hypervisor

ML schema inference, enrichment, ELT raw preservation, routing.

Collection

Horizontal engines near the source; push and pull; independent scaling.

Sources

Any vendor’s devices, cloud, CMDB, IPAM, ITSM, tools, and files.

Avior and SW1

Goal-oriented agentic system; governed AI identity; AI Agent in Observability SaaS.

Incident Response

Alert deduplication, grouping, suppression, enrichment, on-call, status communication.

Correlation views

PerfStack, Orion Maps, node dependencies, Observability SaaS entity views.

Domain products

NPM, NTA, NCM, IPAM, SAM, VMAN, DPA, Observability SaaS.

Collection

Polling engines, agents, and templates per product; OpenTelemetry in SaaS.

Sources

Network, server, application, database, cloud, and third-party alert integrations.

Where SolarWinds falls short architecturally:

A portfolio, not one data model: network, flow, configuration, application, and incident data live in separate products. Cross-domain correlation relies on PerfStack views, node dependencies, and the new Avior layer. Selector normalizes every source into one model before analysis.

Deterministic execution, probabilistic diagnosis: Avior grounds probabilistic models in a fixed framework of goals and approvals that governs how actions run. Selector applies deterministic topology and domain models to the diagnosis itself.

Template-driven collection, summarized history: pollers, templates, and custom pollers define what is collected, and detail data is summarized over time per retention settings. Selector learns structure from data and preserves raw records for replay.

Use case 1: Observability

Multivendor data collection

SolarWinds has deep network monitoring heritage, with strong flow, configuration, topology, and path analysis. Selector adds streaming telemetry, BMP routing analytics, and context sources with no API, collected into one pipeline rather than separate products.
Data type
SolarWinds
Metrics
SNMP, WMI, API, and agent polling across NPM, SAM, and VMAN; OpenTelemetry in Observability SaaS. Native gNMI streaming not described.
SNMP, gNMI and model-driven streaming telemetry, Prometheus-compatible sources, REST APIs.
Logs
Log management in Observability SaaS; Security Event Manager; Kiwi Syslog Server.
Syslog from any device, infrastructure, or application, structured by ML; Splunk, Logstash, TACACS.
Flow
NetFlow Traffic Analyzer: NetFlow, sFlow, J-Flow, IPFIX, and NBAR2 application visibility.
NetFlow, sFlow, IPFIX from devices or collectors, enriched with application, owner, and criticality.
Routing
BGP, OSPF, and EIGRP neighbor and route-table monitoring through SNMP in NPM. BMP ingestion not described.
BGP, BMP/OBMP, and IGP state ingested from customer devices.
Configuration
Network Configuration Manager: backup, change detection, compliance, and rollback; Avior restores last-known-good configurations.
Running and intended config via NETCONF and CLI; drift correlated with impact.
Topology
Orion Maps and Network Topology Mapper with CDP/LLDP discovery; NetPath hop-by-hop path analysis.
From SNMP, CDP/LLDP, configuration, and telemetry, across all vendors; digital twin.
Context
IP Address Manager; Service Desk CMDB and asset management; custom properties.
ServiceNow CMDB, NetBox, Nautobot, Infoblox; owner, site, circuit, criticality.
Hybrid & multicloud
AWS, Azure, and Kubernetes monitoring in Observability SaaS and Self-Hosted.
AWS, Azure, Google Cloud APIs, flow logs, transit gateways, VPCs, correlated with on-prem.
Digital experience
Synthetic, real-user, and website monitoring in Observability SaaS.
ICMP and UDP probes, ThousandEyes, traceroute, custom tests.
Files / streams
Not described in SolarWinds materials.
Kafka, CSV, Excel, Google Sheets.

SolarWinds gaps

No native gNMI or model-driven streaming telemetry described.

BMP ingestion not described; routing visibility comes from SNMP neighbor and route-table polling.

Each product collects through its own pollers and templates, so data lands in separate stores.

Selector value

Streaming telemetry, flow, routing, configuration, and context from any vendor in one pipeline.

Routing changes tied to the services they affect, from BMP and IGP state on the customer’s own devices.

Ingestion from files and streams for data with no API.

Use case 1: Observability

Data normalization and enrichment

Selector normalizes and enriches every signal from every source before any model runs. SolarWinds structures data per product through templates and pollers, and enriches alerts as they reach Incident Response.

Capability
SolarWinds
Data Hypervisor
Schema inference
Application templates, Universal Device Pollers, and configuration templates define collection and parsing. ML schema inference not described.
ML infers structure from free-form logs, syslog, traps, and CLI output; no parsing templates.
Entity resolution
Nodes, interfaces, and applications modeled per product on the SolarWinds Platform; entity views in Observability SaaS.
Named-entity and clustering models group events across vendor and format variations.
Enrichment
Custom properties, node dependencies, and Incident Response enrichment with services, change history, and dependencies.
Topology, dependency, owner, site, maintenance state, and criticality added before analysis.
Data fidelity
Polled detail data summarized into hourly and daily data per retention settings. Raw-data replay of network state not described.
ELT: raw data lands with original timestamps; transformation follows.
Noise suppression
Alert conditions, baseline thresholds, dependency-based suppression, and Incident Response deduplication and suppression rules.
Duplicate, flapping, and irrelevant signals removed before the intelligence layer.
Business logic
Alert rules, maintenance mode, and suppression rules configured per product.
Maintenance windows, provider notices, suppression, and criticality in YAML via DevOps workflows.
Customization
Custom pollers, SAM templates, NCM scripts, and SWQL queries.
S2ML tunes enrichment, labeling, noise rules, prioritization, and LLM output.
Data access
SolarWinds Information Service API and SWQL; product dashboards and PerfStack.
Every view is a query and can be exposed as an API endpoint; MCP server.

SolarWinds gaps

Structure depends on templates and custom pollers that need maintenance as vendors and formats change.

Detail data is summarized over time, limiting precise reconstruction of past incidents.

Enrichment and suppression are configured separately in each product.

Selector value

Learned normalization: no parsing templates or custom pollers to maintain.

Raw records retained with original timestamps for replay and re-analysis.

One enrichment layer applies topology, ownership, maintenance, and criticality to every signal.

Use case 1: Observability

AI-powered operational analytics

Selector models every time series and log stream individually and correlates across domains automatically. SolarWinds detects issues primarily through thresholds and baselines in each product, with PerfStack for manual cross-domain analysis.
Capability
SolarWinds
Anomaly detection
Static and baseline-calculated thresholds in self-hosted products; anomaly-based alerting in Observability SaaS.
Self-supervised, statistical, and regression models. A model per time series, retrained every 30 minutes, with seasonality.
Log analysis
Log search, filtering, and alerting in Observability SaaS; event correlation rules in Security Event Manager.
Small language models. Log semantics interpreted, clustered across vendors, and turned into structured events.
Prediction
Capacity forecasting in NPM and VMAN. Optical failure prediction not described.
Forecasting and predictive models. Capacity forecasting and optical transceiver failure prediction.
Cross-domain analysis
PerfStack lets engineers overlay metrics from multiple products on one timeline for manual correlation.
One model across domains. Correlation runs automatically across network, cloud, and application signals.
Natural language
SolarWinds AI Agent in Observability SaaS and SW1 across SaaS and self-hosted environments.
Rosetta. Plain-language questions answered with every claim linked to source data.

SolarWinds gaps

Detection centers on static and baseline thresholds per product; per-series learned models for every metric are not described.

Cross-domain correlation in PerfStack is engineer-driven.

Optical failure prediction not described.

Selector value

Per-series models retrained every 30 minutes detect anomalies no threshold would catch.

Automatic correlation across network, cloud, and application signals in one model.

Optical failure prediction and capacity forecasting before impact.

Use case 2: Event intelligence

Event correlation and root cause

SolarWinds combines rule-based alert grouping in Incident Response, dependency-based suppression in NPM, and Avior’s new alert merging. Selector correlates alerts together with the telemetry behind them and determines cause, not just grouping.
Capability
SolarWinds
Inputs
SolarWinds product alerts plus alerts from third-party monitoring, cloud, APM, and log tools through Incident Response integrations.
Alerts and the raw telemetry, logs, flow, and configuration behind them, correlated together.
Correlation method
Deduplication, grouping, and suppression rules in Incident Response; node dependencies in NPM; Avior merges related alerts into one incident.
Contextual embeddings and clustering by shared devices, interfaces, IP addresses, topology, and time.
Noise reduction
Deduplication and suppression rules; AI-generated summaries and automatic categorization.
Up to 99.9% of irrelevant noise removed before correlation.
Root cause
Parent-child node dependencies mark downstream nodes unreachable; AI Agent assists diagnosis. Causation model not described.
Deterministic causation from topology and domain logic: reboot, then link failure, then BGP flap.
Symptom vs. cause
Rule-based node dependencies suppress downstream network alerts. Cross-domain consequence labeling not described.
Downstream effects labeled as consequences, not separate faults, across every domain.
Change correlation
NCM change detection and Incident Response change history attached to alerts.
Configuration diffs and change events correlated directly with degradation.
Evidence and replay
PerfStack historical views on summarized data; Avior logs every action. State replay not described.
Every finding links to source data; Network DVR replays past network state.

Selector example

The Loom agent resolved 214 alerts across six tools to a single fiber cut on one long-haul span, labeled routing reconvergence and cloud failover as consequences, and flagged the secondary path at 78% utilization as the next risk.

What determinism means in each

SolarWinds describes Avior as grounding probabilistic models in deterministic execution: fixed goals and approvals govern how actions run. In Selector, the diagnosis itself is deterministic: the same data produces the same root cause.

SolarWinds gaps

Grouping is rule- and dependency-based; a cross-domain causation model is not described.

Correlation operates on alerts from separate products rather than one model of the underlying telemetry.

Network state replay is not described; history is summarized over time.

Selector value

Alert floods collapse into one incident with a named cause, consequences, and next risk.

Telemetry, logs, flow, and configuration correlated with alerts in one model.

Every finding is traceable and reproducible with Network DVR.

Use case 2: Event intelligence

Agents, ITSM, and governed action

Function
SolarWinds
Foundry
Investigation
AI Agent and SW1. Conversational diagnostics and a governed AI identity across the SolarWinds stack.
Rosetta. Interprets plain-language intent, dispatches agents across domains, returns one cited thread.
Correlation / RCA
Avior. Merges related alerts into a single incident; Incident Response groups and enriches alerts.
Loom. Connects time, topology, changes, and domains into one incident with a root cause.
ITSM
Native SolarWinds Service Desk; ITSM integrations through Incident Response.
TicketMaster. Bi-directional ServiceNow sync across the lifecycle, with per-customer field mapping and incident-aware deduplication.
Communications
Incident Response status pages, stakeholder communication, and AI-generated summaries.
Herald. Executive briefs, ops updates, and post-incident reports from the verified record.
Custom agents
Avior works from predefined goals; SW1 applies customer-defined guardrails. Building new customer agents from playbooks not described.
Sculptor. Turns a described playbook into a governed agent with goal, tools, triggers, and approval points.
Action
Avior restores configurations, snapshots running configs, and retires stale assets, with risk-tiered autonomy, approvals, and rollback; Incident Response runbooks.
Agents investigate, act, and verify within operator-set limits on high-confidence findings.
MCP
Avior reaches third-party systems through MCP. An MCP server exposing SolarWinds insights not described.
Production MCP server with OAuth2, granular RBAC over insights, recommendations, and actions.
Deployment
SaaS and self-hosted; Avior available through a guided onboarding program since October 2026.
SaaS, on-premises, customer cloud, or air-gapped.

SolarWinds gaps

Avior’s goals are predefined by SolarWinds; turning the customer’s own playbooks into new agents is not described.

Avior acts on alerts and inventory, not on a deterministic cross-domain root cause.

Avior is newly available through guided onboarding.

Selector value

Sculptor builds governed agents from the team’s own playbooks, acting only on verified findings.

MCP server shares insights and governed actions with other agent platforms.

Every Foundry agent is available now, in every deployment model.

Functionality gained with Selector

Capabilities a SolarWinds customer adds with Selector, whether Selector runs alongside SolarWinds or replaces it.

1. One model across the estate

Signals from NPM, NTA, NCM, SAM, cloud, and non-SolarWinds sources correlated automatically in one model, instead of across separate products and PerfStack views.

2. Deterministic diagnosis

Root cause comes from deterministic topology and domain models, so the same data yields the same answer, not only governed execution.

3. Detection beyond baselines

Per-series models retrained every 30 minutes find conditions no static or baseline threshold would catch.

4. Streaming and routing depth

gNMI streaming telemetry and BMP/OBMP routing analytics from the customer’s own devices, plus optical failure prediction.

5. Full-fidelity history

Raw data preserved with original timestamps; Network DVR replays past network state instead of summarized history.

6. No templates to maintain

Learned normalization infers structure across vendors and formats, replacing custom pollers and parsing templates.

7. Your playbooks as agents

Sculptor turns the team’s own runbooks into governed agents, rather than working only from predefined vendor goals.

8. Governed action through MCP

Selector’s MCP server exposes insights, recommendations, and actions to other agent platforms under OAuth2 and RBAC.

9. Custom outcomes with engineers

Forward deployed engineers build integrations, data models, views, and agents on the customer’s own data.

10. No rip-and-replace

Selector ingests from SolarWinds, so teams add causal root cause and governed agents above existing monitoring.

Selector patents driving innovation

The techniques that define the Selector platform are covered by Selector’s U.S. patent portfolio. Patent records are available on request.
Capability
What Selector’s patented approach delivers
Root cause analysis
Deterministic causation across every domain.
Reproducible investigations
Network DVR replays network state so an investigation can be re-run.
Log intelligence
Structured signal from any vendor’s syslog without parsing templates.
Conversational investigation
Learns each customer’s names for sites, services, and devices without manual labeling.
Custom outcomes on demand
Declarative query chains for drill-downs and customer-specific views.
Operational context
Maintenance-window-aware alerting and reporting.
Optical failure prediction
Flags failing optics before they cause an outage.
Capacity planning
Forecasts capacity limits before they affect services.
Scale
Efficient storage and querying of millions of data points per minute.

Selector vs. SolarWinds at a glance

Capability
SolarWinds
Observability
Data collection
Deep network monitoring across NPM, NTA, NCM, and SAM; SaaS and self-hosted.
Any vendor, including gNMI, BMP/IGP, flow, config, and file sources, in one pipeline.
Normalization
Template- and poller-based collection; summarized history.
Learned schema inference; ELT raw preservation.
Detection
Static and baseline thresholds; anomaly alerting in SaaS.
Per-series models retrained every 30 minutes.
Cross-domain analysis
PerfStack for engineer-driven correlation.
Automatic correlation in one model.
Event intelligence
Correlation
Rule-based grouping, node dependencies, and Avior alert merging.
Alerts plus the raw telemetry behind them.
Root cause
Dependency-based suppression; AI-assisted diagnosis.
Deterministic causation with symptom labeling.
Reproducibility
Summarized history; Avior action logs.
Network DVR replay; source-linked evidence.
Custom agents
Avior predefined goals; SW1 guardrails.
Sculptor builds governed agents from playbooks.
MCP
Avior uses MCP to reach third-party systems.
Server for insights, recommendations, and governed actions.
Deployment
SaaS and self-hosted.
SaaS, on-premises, or air-gapped.

The bottom line

SolarWinds is an established IT management and observability vendor with deep network monitoring, configuration management, and flow analysis, delivered self-hosted or as SaaS, and now adding agentic AI through SW1 and Avior.

Selector is built for a different starting point. It collects raw telemetry and context from every vendor into one model, preserves it at full fidelity, determines cause rather than grouping alerts, and puts governed agents on top, including agents built from your own playbooks. Because Selector ingests from SolarWinds, teams can add it without replacing existing monitoring.

30 min

Model retraining cycle

99.9%

Noise reduction

8x

Faster resolution per P1

See Selector on your own data

www.selector.ai | sales@selector.ai

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