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Technical comparison

Selector vs. Dynatrace

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. Dynatrace is an AI-powered observability platform built on OneAgent, the Grail data lakehouse, Smartscape topology, and Davis causal AI. Dynatrace Intelligence combines deterministic causal reasoning with agentic AI.

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.

Dynatrace Intelligence

Agentic AI operating within deterministic causal boundaries.

Davis AI

Causal, predictive, and generative AI; root cause analysis.

Smartscape

Real-time topology and dependency graph.

Grail

Data lakehouse for metrics, logs, traces, and events.

Collection

OneAgent, OpenTelemetry, extensions, and APIs.

Sources

Applications, hosts, Kubernetes, cloud; network devices through extensions.

Where Dynatrace falls short architecturally:

Causality strongest in the application stack: Smartscape is discovered by OneAgent across hosts, processes, and services; network devices are modeled through extensions. Selector’s causation is built for the network and infrastructure, alongside cloud and applications.

Network depth through extensions: SNMP and flow data arrive through extensions, and routing protocol analytics are not a core capability. Selector collects streaming telemetry, BMP, IGP, and configuration natively.

Agent-based discovery: Topology depends on OneAgent deployment, which does not run on network devices. Selector builds topology from device telemetry, CDP/LLDP, and configuration.

Use case 1: Observability

Multivendor data collection

Dynatrace leads in application and cloud observability. Selector adds network-native depth: streaming telemetry, routing protocols, and configuration state.
Data type
Metrics
OneAgent, OpenTelemetry, and extensions; SNMP through extensions. Native gNMI streaming not described.
SNMP, gNMI and model-driven streaming telemetry, Prometheus-compatible sources, REST APIs.
Logs
Log management in Grail with OpenPipeline.
Syslog from any device, infrastructure, or application, structured by ML; Splunk, Logstash, TACACS.
Flow
NetFlow through extensions.
NetFlow, sFlow, IPFIX from devices or collectors, enriched with application, owner, and criticality.
Routing
BMP and IGP ingestion not described.
BGP, BMP/OBMP, and IGP state ingested from customer devices.
Configuration
Network device configuration and drift not described.
Running and intended config via NETCONF and CLI; drift correlated with impact.
Topology
Smartscape for applications and hosts; network devices through extensions.
From SNMP, CDP/LLDP, configuration, and telemetry, across all vendors; digital twin.
Context
Tags, ownership, and ServiceNow CMDB integration.
ServiceNow CMDB, NetBox, Nautobot, Infoblox; owner, site, circuit, criticality.
Hybrid & multicloud
Strong: AWS, Azure, Google Cloud, Kubernetes.
AWS, Azure, Google Cloud APIs, flow logs, transit gateways, VPCs, correlated with on-prem.
Digital experience
Strong: synthetics and next-generation RUM.
ICMP and UDP probes, ThousandEyes, traceroute, custom tests.
Files / streams
Ingestion APIs and OpenPipeline.
Kafka, CSV, Excel, Google Sheets.

Dynatrace gaps

BMP and IGP ingestion not described.

Network device configuration and drift not described.

Network devices modeled through extensions rather than core topology.

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

Both platforms enrich data with topology before analysis. Dynatrace’s enrichment is centered on OneAgent-discovered application topology; Selector’s spans network devices, routing, and context sources.
Capability
Data Hypervisor
Schema inference
OpenPipeline processing and semantic dictionary. ML schema inference for free-form network logs not described.
ML infers structure from free-form logs, syslog, traps, and CLI output; no parsing templates.
Entity resolution
Smartscape entities from OneAgent discovery.
Named-entity and clustering models group events across vendor and format variations.
Enrichment
Tags, ownership, and Smartscape context.
Topology, dependency, owner, site, maintenance state, and criticality added before analysis.
Data fidelity
Grail retains raw data per retention settings. Network state replay not described.
ELT: raw data lands with original timestamps; transformation follows.
Noise suppression
Davis problem detection and event merging.
Duplicate, flapping, and irrelevant signals removed before the intelligence layer.
Business logic
Maintenance windows and alerting profiles.
Maintenance windows, provider notices, suppression, and criticality in YAML via DevOps workflows.
Customization
Extensions, DQL, and workflows.
S2ML tunes enrichment, labeling, noise rules, prioritization, and LLM output.
Data access
DQL, APIs, and MCP server.
Every view is a query and can be exposed as an API endpoint; MCP server.

Dynatrace gaps

OpenPipeline processing rules are authored per source.

Network device context is limited compared with application context.

Network state replay not described.

Selector value

Learned normalization: no parsing templates or rules 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

Both platforms apply AI to telemetry. Dynatrace’s strength is application and cloud analytics; Selector’s is network-native causation across every domain.
Capability
Anomaly detection
Davis adaptive baselines and anomaly detection.
Self-supervised, statistical, and regression models. A model per time series, retrained every 30 minutes, with seasonality.
Log analysis
Log analytics and pattern detection in Grail.
Small language models. Log semantics interpreted, clustered across vendors, and turned into structured events.
Prediction
Davis predictive AI and forecasting. Optical failure prediction not described.
Forecasting and predictive models. Capacity forecasting and optical transceiver failure prediction.
Cross-domain analysis
Strong across applications, hosts, and cloud.
One model across domains. Correlation runs automatically across network, cloud, and application signals.
Natural language
Davis CoPilot translates questions into DQL.
Rosetta. Plain-language questions answered with every claim linked to source data.

Dynatrace gaps

Causal analysis is strongest across application and host topology.

Network-device causation depends on extension coverage.

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

Dynatrace is the closest competitor to Selector on causation: Davis determines root cause deterministically over Smartscape. The difference is scope: Selector’s causation covers network devices, routing, and optical layers natively.
Capability
Inputs
Davis problems from Dynatrace data and ingested third-party events.
Alerts and the raw telemetry, logs, flow, and configuration behind them, correlated together.
Correlation method
Smartscape-based causal grouping into problems.
Contextual embeddings and clustering by shared devices, interfaces, IP addresses, topology, and time.
Noise reduction
Davis merges related events into a single problem.
Up to 99.9% of irrelevant noise removed before correlation.
Root cause
Deterministic causal AI over Smartscape topology.
Deterministic causation from topology and domain logic: reboot, then link failure, then BGP flap.
Symptom vs. cause
Root cause and impact separated within Smartscape topology.
Downstream effects labeled as consequences, not separate faults, across every domain.
Change correlation
Deployment and configuration events linked to problems.
Configuration diffs and change events correlated directly with degradation.
Evidence and replay
Problem evidence and timelines. Network 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.

Where Davis reasons

Davis traverses Smartscape to find the root cause among services, processes, and hosts. When the cause is a fiber cut, BGP flap, or failing optic, it lies outside the topology OneAgent discovers.

Dynatrace gaps

Causation covers Smartscape topology; network routing and optical layers sit largely outside it.

Network symptom-versus-cause labeling depends on extension data.

Network state replay not described.

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
Foundry
Investigation
Dynatrace Intelligence. Agentic AI within causal boundaries; Davis CoPilot.
Rosetta. Interprets plain-language intent, dispatches agents across domains, returns one cited thread.
Correlation / RCA
Davis AI. Causal root cause over Smartscape.
Loom. Connects time, topology, changes, and domains into one incident with a root cause.
ITSM
ServiceNow integration.
TicketMaster. Bi-directional ServiceNow sync across the lifecycle, with per-customer field mapping and incident-aware deduplication.
Communications
Problem notifications and summaries.
Herald. Executive briefs, ops updates, and post-incident reports from the verified record.
Custom agents
Workflows and AutomationEngine. Governed agents built from described playbooks not described.
Sculptor. Turns a described playbook into a governed agent with goal, tools, triggers, and approval points.
Action
AutomationEngine workflows and remediation.
Agents investigate, act, and verify within operator-set limits on high-confidence findings.
MCP
MCP server for Dynatrace data.
Production MCP server with OAuth2, granular RBAC over insights, recommendations, and actions.
Deployment
SaaS and Managed.
SaaS, on-premises, customer cloud, or air-gapped.

Dynatrace gaps

Turning the customer’s own playbooks into governed agents is not described.

Network-native remediation depth not described.

Deployment limited to SaaS and Managed.

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 Dynatrace customer adds with Selector, whether Selector runs alongside Dynatrace or replaces it.

1. One model across the estate

Network, cloud, application, and context signals from every vendor correlated automatically in one model.

2. Causation that includes the network

Deterministic root cause across network devices, routing protocols, optics, and configuration, not only the application and host topology an agent discovers.

3. Detection beyond thresholds

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

4. Network data without extensions

gNMI streaming telemetry, BMP/OBMP and IGP analytics, and configuration state collected natively, plus optical failure prediction.

5. Full-fidelity history

Raw data preserved with original timestamps; Network DVR replays past network state so findings can be verified and re-run.

6. No templates to maintain

Learned normalization infers structure across vendors and formats, replacing parsing rules and custom collection scripts.

7. Your playbooks as agents

Sculptor turns the team’s own runbooks into governed agents with approved tools, triggers, approval points, and audit.

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 deploys alongside Dynatrace and ingests from existing tools and APIs, adding causal root cause and governed agents above them.

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. Dynatrace at a glance

Capability
Observability
Data collection
Strong app and cloud; network via extensions.
Any vendor, including gNMI, BMP/IGP, flow, config, and file sources, in one pipeline.
Normalization
OpenPipeline and Smartscape.
Learned schema inference; ELT raw preservation.
Detection
Davis adaptive baselines.
Per-series models retrained every 30 minutes.
Cross-domain analysis
Strong across app, host, cloud.
Automatic correlation in one model.
Event intelligence
Correlation
Davis problems.
Alerts plus the raw telemetry behind them.
Root cause
Deterministic causal AI over Smartscape.
Deterministic causation with symptom labeling.
Reproducibility
Problem evidence.
Network DVR replay; source-linked evidence.
Custom agents
Workflows.
Sculptor builds governed agents from playbooks.
MCP
MCP server.
Server for insights, recommendations, and governed actions.
Deployment
SaaS and Managed.
SaaS, on-premises, or air-gapped.

The bottom line

Dynatrace is a leading observability platform with deterministic causal AI over its Smartscape topology and strong application, cloud, and digital experience coverage.

Selector applies deterministic causation where network and infrastructure teams need it: across devices, routing, optical, and configuration from every vendor, with raw-data replay, flexible deployment, and governed agents built from your own playbooks.

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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