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Cybersecurity Information SecurityTop 10 Best Error Monitoring Software of 2026
Rank the top 10 error monitoring software with Sentry, Datadog Error Tracking, and New Relic Error Analytics plus tools like Highlight and GlitchTip.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Highlight is the best fit if you run a web service and want visual user context tied to grouped exceptions for faster fixes, whereas Datadog Error Tracking suits teams already using Datadog to correlate errors with logs, traces, and deployments.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Highlight
Session replay context attached to grouped error issues, so incidents map to the exact user journey.
Built for fits when web teams need visual user context tied to grouped exceptions for faster fixes..
GlitchTip
Editor pickIssue-centric alerting that deduplicates by grouped exception so notifications map to triage work.
Built for fits when teams need consistent error aggregation and low-noise alert routing for service exceptions..
Datadog Error Tracking
Editor pickDatadog’s deployment-linked release health connects error regression signals to rollouts across environments.
Built for fits when teams using Datadog need correlated error triage across traces, logs, and deployments..
Related reading
- Cybersecurity Information SecurityTop 10 Best Error Logging Software of 2026
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- Cybersecurity Information SecurityTop 10 Best Computer And Internet Monitoring Software of 2026
- Cybersecurity Information SecurityTop 10 Best 24/7 Security Monitoring Services of 2026
Comparison Table
Error monitoring software turns crashes and frontend failures into searchable telemetry by normalizing exception events, attaching stack traces, and correlating them with logs, traces, and releases. This ranked list targets analysts and operators who need verifiable comparison signals, with scoring focused on integration depth, automation support, and data model coherence across the top options.
Highlight
API-firstHighlight combines error monitoring, session replay, logs, and frontend performance telemetry.
Session replay context attached to grouped error issues, so incidents map to the exact user journey.
Highlight captures client-side error events, then links them to the exact pages and interactions that triggered the failure. Error aggregation and grouping are geared toward actionable “issues” rather than a flat stream, and release health surfaces regressions by deployment window. Deployment markers and contextual metadata are used to slice failures by environment and time, which helps teams validate whether a change reduced error rate.
The tradeoff is that deep backend-only diagnostics like advanced stack trace symbolication and trace correlation are not the primary strength compared with agents that center on distributed tracing and server telemetry. Highlight fits teams that own web UI quality or customer journeys and need to reproduce what users saw from the issue itself.
- +Session-linked error issues reduce time from alert to reproduction
- +Release-window comparisons highlight regressions tied to deployments
- +Alert routing and thresholds work off grouped errors and metadata
- +Breadcrumbs of user context guide faster root-cause hypotheses
- –Backend-only and distributed tracing correlation is comparatively limited
- –Advanced governance like granular RBAC and audit log depth needs validation
- –High-signal grouping depends on consistent instrumentation across pages
Frontend engineering leads
Debugging user-facing UI exceptions
Fewer guess cycles, faster fixes
Customer experience teams
Triage recurring customer breakages
Clearer impact on experience
Show 1 more scenario
Web platform SRE
Alert routing for high-visibility errors
Less alert noise
Threshold-based notifications route grouped failures with contextual metadata attached.
Best for: Fits when web teams need visual user context tied to grouped exceptions for faster fixes.
More related reading
GlitchTip
API-firstGlitchTip provides open-source error tracking with Sentry-compatible event ingestion and monitoring.
Issue-centric alerting that deduplicates by grouped exception so notifications map to triage work.
GlitchTip uses exception aggregation with stack trace capture and fingerprinting-style grouping so multiple occurrences map to a single investigation item. Each issue supports investigation context such as request and environment metadata, along with breadcrumbs from recent execution steps when SDK instrumentation includes them. Release-level signals like deploy markers and issue-to-release views support tracking regression patterns across environments.
GlitchTip trades broad full-stack correlation for a narrower error tracking workflow, so teams needing deep distributed tracing correlation may need additional tooling. It fits best when an engineering team wants consistent issue grouping and repeatable alert routing for web services or background workers that emit errors via SDKs.
- +Exception grouping turns noisy events into stable triage items
- +Context-rich issue pages speed root cause reviews and ownership handoffs
- +Notification routing supports deduplication by issue rather than per event
- +API-backed event ingestion enables automation and CI-driven checks
- –Distributed tracing correlation is limited versus platforms focused on tracing
- –Advanced routing and workflow automation require careful configuration discipline
- –Client-side breadth depends on SDK coverage for each environment
- –Higher-volume deployments need tuned retention and grouping settings
Small web engineering teams
Triage repeat exceptions from production
Faster mean time to resolve
Platform and SRE teams
Route errors by environment ownership
Cleaner alert queues
Show 2 more scenarios
Release engineers
Detect regressions across deployments
Earlier regression detection
Deploy markers help compare new issue behavior between releases.
Automation-focused teams
Integrate error ingest via API
More consistent monitoring automation
Programmatic event submission supports CI checks and internal tooling workflows.
Best for: Fits when teams need consistent error aggregation and low-noise alert routing for service exceptions.
Datadog Error Tracking
enterpriseDatadog Error Tracking connects application exceptions with logs, traces, infrastructure, and deployments.
Datadog’s deployment-linked release health connects error regression signals to rollouts across environments.
Datadog Error Tracking collects errors through instrumented SDKs and browser monitoring, then groups them into issues using Datadog’s backend aggregation logic. Each grouped issue keeps contextual metadata and request context where available, which helps correlate a failing request with the trace that produced it. Release health and deployment markers tie error volume and regressions to specific versions, which makes it easier to validate whether an incident aligns with a rollout.
A tradeoff is that teams relying on highly custom issue grouping or alternative fingerprinting rules may hit less flexibility than tools that focus narrowly on exception grouping controls. It fits best when an organization already runs Datadog for traces and logs and wants error triage to stay inside the same operational workflows. It also suits teams that need consistent environment segmentation so alerts can route differently for staging and production.
- +Correlates errors to traces and logs within one operational context
- +Release health ties error volume shifts to deployments and versions
- +Issue grouping reduces duplication during fast incident triage
- +Environment segmentation supports separate alert routing per stage
- –Advanced custom fingerprinting controls can be less granular than niche tools
- –High correlation depends on consistent instrumentation across services
- –Grouping changes can require re-evaluation of historical issue baselines
- –Browser error capture coverage may vary by SDK and bundling setup
Platform engineering teams
Triage regressions after each deployment
Faster rollback decisions
Site reliability engineering teams
Route alerts by environment
Lower alert noise
Show 2 more scenarios
Customer-facing application teams
Diagnose errors with request context
Shorter time to root cause
Error events carry request context that can be followed through correlated traces.
Web and mobile product teams
Track client-side failures at scale
Unified client and server view
Client SDKs feed browser and mobile error events into the same issue grouping workflow.
Best for: Fits when teams using Datadog need correlated error triage across traces, logs, and deployments.
Better Stack
SMBBetter Stack combines error monitoring with logs, uptime checks, incident management, and on-call alerts.
Release-context triage that links grouped exceptions to deployments for faster regression detection without manual correlation.
Better Stack focuses on error monitoring as part of a broader observability workflow for teams that run modern services. Error aggregation and issue grouping are designed to turn raw exceptions into stable, actionable incidents with release and environment context.
Integration depth centers on ingesting signals from common backends and shipping events into a unified triage view. Automation and API surface support alert routing and operational guardrails across teams and environments.
- +Exception grouping reduces duplicate alerts across environments
- +Triage view ties errors to releases and deployment markers
- +Automation supports alert routing and notification control
- +API enables custom workflows and programmatic incident management
- –Source map and symbolication coverage depends on correct build artifacts
- –Deep stack trace enrichment requires SDK instrumentation discipline
- –Advanced distributed tracing correlation is not as central as in all telemetry stacks
- –Multi-tenant governance controls feel lighter than enterprise ticketing workflows
Best for: Fits when teams want exception tracking with release context and automation-driven alert routing.
Sematext Error Tracking
enterpriseSematext Error Tracking collects exceptions and connects them with logs, traces, and application metrics.
Stable issue identity with release-aware error aggregation for consistent regression detection across environments.
Sematext Error Tracking groups captured exceptions and provides stack trace capture with contextual request and deployment metadata. Error aggregation and issue grouping focus on faster triage through stable issue identity and release-aware health views.
The integration surface centers on SDK instrumentation for server and client JavaScript and on API-driven ingest patterns for custom error events. Operational control is reinforced through environment segmentation and alert routing that can be tuned to error rate and regression signals.
- +Exception issue grouping improves repeatability of triage across deployments
- +Stack trace capture includes request context and deployment metadata for root cause
- +Alert routing supports thresholds tied to error rate patterns
- +Environment segmentation keeps staging and production signals separate
- –Most value depends on adding instrumentation coverage across services
- –Release health views require correct deployment markers in event payloads
- –Fewer workflow hooks for incident automation than broad ops-native ecosystems
- –High event volumes can require careful sampling to control throughput
Best for: Fits when teams need exception grouping plus release-aware triage for multiple environments.
Sentry
enterpriseSentry captures application errors, stack traces, performance data, and release regressions.
Release health views connect new errors to specific deployments, with regression-style comparisons across environments.
Sentry is an exception and error monitoring system built around end-to-end issue grouping, stack trace capture, and release-aware context. SDKs capture errors in browser JavaScript, mobile, and server runtimes, then group events into issues with fingerprints, aggregations, and contextual metadata.
The service ties errors to deployments using release markers and supports workflow with configurable alerting and notification routing. For teams that need control, Sentry provides an extensive automation and API surface for integrations, event ingestion, and lifecycle actions.
- +High-fidelity issue grouping with fingerprints and stable grouping heuristics
- +Strong release awareness via deployment tracking and version association
- +Broad SDK coverage for web, mobile, and many backend frameworks
- +Alerting supports routing rules and deduplication to reduce noise
- –Custom source map workflows require disciplined build and artifact management
- –Deep governance controls take time to configure across projects and teams
- –Throughput and sampling decisions can complicate expectations during spikes
- –Some advanced correlation workflows need additional instrumentation beyond errors
Best for: Fits when teams want release-aware exception tracking with strong issue grouping and automation via API.
LogRocket
vertical specialistLogRocket links frontend errors with session replay, network activity, and browser performance data.
Session replay timeline shows the exact user actions leading to each JavaScript error, reducing guesswork during triage.
LogRocket centers error monitoring around session replay and issue reproduction, linking UI behavior to JavaScript failures in the same workflow. It captures stack traces in browser and native client contexts, then groups events into actionable issues with contextual request metadata.
Breadcrumbs and deployment markers help correlate regressions with releases, while advanced integrations support syncing signals into existing alert and analytics systems. Overall, LogRocket is geared toward debugging user-impacting front-end problems with less guesswork than exception-only tools.
- +Session replay frames each exception with reproducible user behavior
- +Issue grouping uses stack trace fingerprinting for faster triage
- +Breadcrumb trails add request and UI context around failures
- +Release health correlation ties spikes to deployments
- –Client-side instrumentation coverage can miss server-only error paths
- –Deep workflow automation needs careful configuration of environments
- –High volume sessions can increase data throughput management work
- –RBAC-style governance is limited compared with enterprise-only audit models
Best for: Fits when front-end teams need session-linked exception tracking and faster reproduction without manual steps.
AppSignal
vertical specialistAppSignal monitors errors, performance, incidents, and host metrics for web applications.
Deploy-correlated release health shows error rate changes by environment and release window for regression detection.
AppSignal provides exception tracking tailored to Ruby applications, with stack trace capture and issue grouping that reduce time spent on duplicates.
Operational context comes through request context and environment segmentation, which helps route alerts and prioritize impact during live incidents.
A documented API supports automated event ingestion and integration into deployment or incident tooling.
- +Release health views connect deploy timing to exception regressions
- +Request context makes issue grouping more actionable during triage
- +Alert routing supports environment-based separation across staging and production
- +API enables programmatic error reporting and automation around incidents
- –Non-Rails setups may need more instrumentation work to reach parity
- –Limited depth for client-side JavaScript monitoring compared to broader suites
- –Advanced workflow automation needs careful configuration to avoid alert noise
- –Some higher-granularity correlation workflows require more manual event tagging
Best for: Fits when Ruby teams need deploy-correlated exception tracking with strong request context and controllable alert workflows.
TrackJS
vertical specialistTrackJS records JavaScript errors with browser context, user actions, and diagnostic telemetry.
Release health views connect aggregated issues to deployment markers for regression-style comparisons.
TrackJS captures client-side JavaScript errors and groups them into issues with stack trace context. The workflow ties errors to releases and deployments so teams can quantify release health and regression patterns.
TrackJS also supports source map upload for symbolication, plus breadcrumbs and request context to explain how failures occurred. An extensible configuration layer and API surface help wire error data into existing triage, alert routing, and reporting systems.
- +Client JavaScript focus with stack trace grouping and rich browser context
- +Release association supports regression detection using deployment markers
- +Source map upload improves symbolication for minified bundles
- +Breadcrumbs and request context clarify leading actions before an error
- –Deeper configuration is needed for consistent event coverage across apps
- –Alert routing and deduplication controls feel less granular than larger rivals
- –Large projects can face lower issue throughput if grouping rules are too broad
- –Complex environments need careful environment segmentation to avoid noisy comparisons
Best for: Fits when teams prioritize browser JavaScript exception tracking with release-linked triage and symbolication.
Scout APM
SMBScout APM monitors application performance and reports errors, traces, slow requests, and database activity.
Release-linked issue timelines that connect grouped exceptions to specific deployments for regression detection.
Scout APM is a hosted error monitoring tool that centers on automated issue grouping and triage context from production traffic. It captures exceptions with stack traces and aggregates repeated failures into manageable issues, then ties them to releases so regressions can be spotted quickly.
Scout APM also supports alerting rules and environment segmentation so teams can route notifications by deployment stage. Data collection integrates with common SDK setups for server-side and client-side JavaScript where supported.
- +Exception grouping reduces duplicate alerts for repeated failures
- +Release association helps isolate regressions after deployments
- +Environment segmentation supports separate views for staging and production
- +Alert rules enable issue-based notification routing
- –Less extensive alert and workflow controls than larger monitoring suites
- –Client-side and server-side coverage depends on supported instrumentation
- –API and automation surface is narrower than top-tier competitors
- –Advanced triage depth can require more manual investigation
Best for: Fits when teams want exception aggregation and release-aware triage for JavaScript services.
Conclusion
After evaluating 10 cybersecurity information security, Highlight stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right error monitoring software
This buyer’s guide covers error monitoring software through ten tools that focus on exception tracking, stack trace capture, and release-aware triage. The list includes Highlight, GlitchTip, Datadog Error Tracking, New Relic Error Analytics, and Sentry, plus Better Stack, Sematext Error Tracking, LogRocket, AppSignal, and TrackJS.
Across these options, the deciding differences show up in issue grouping behavior, how deployment markers connect to release health, and the automation surface teams can use for alert routing and workflows. Highlight, GlitchTip, and Datadog Error Tracking are used as primary anchors for how error aggregation connects to downstream debugging context and trace or release signals.
Error monitoring software for exception tracking, issue grouping, and release-aware triage
Error monitoring software captures application exceptions and groups related stack traces into stable issues using fingerprinting or grouping heuristics, then attaches contextual metadata for faster debugging. Tools like Sentry and GlitchTip emphasize release-aware exception aggregation, where deployment-linked views connect new error spikes to rollouts and environment changes.
Many platforms also correlate error events to adjacent telemetry so teams can move from alert to reproduction through trace correlation, log context, or session-level replay. Datadog Error Tracking is a common example of connecting errors to traces and logs in one operational context, while Highlight attaches session replay context to grouped error issues so incidents map to the exact user journey.
Integration and governance signals for error aggregation and release-aware triage
Issue grouping determines how quickly teams move from an error spike to a small set of triage items. Highlight, GlitchTip, and Sentry emphasize stable grouping behavior and then attach the right debugging context so teams can reproduce and fix efficiently.
Release health and deployment linkage decide whether errors get mapped to rollouts or versions automatically. Datadog Error Tracking, New Relic Error Analytics, and Better Stack connect grouped exceptions to deployment markers or release windows so regression-style comparisons stay actionable across environments.
Session replay context tied to grouped exceptions
Highlight attaches session replay context to grouped error issues so incidents map to the exact user journey. LogRocket similarly builds a session replay timeline for each JavaScript error, but Highlight is more incident-mapping oriented around grouped issues.
Issue-centric alerting and deduplication by grouped exception
GlitchTip uses issue-centric alerting that deduplicates by grouped exception so notifications track triage work instead of raw events. Better Stack also reduces duplicate alerts via exception grouping, but GlitchTip focuses on stable routing onto grouped issues.
Trace, log, and error triage correlation in one operational context
Datadog Error Tracking correlates errors to traces and logs so triage stays in a single context. Highlight can connect debugging context to incidents, but distributed tracing correlation is comparatively limited in its feature mix.
Release health views linked to deployments and versions
Sentry provides release health views that connect new errors to specific deployments with regression-style comparisons across environments. AppSignal and TrackJS also link deploy timing to grouped issues, but Sentry is positioned around issue grouping plus release awareness through deployment tracking and version association.
Automation and API surface for triage workflow
Sentry supports automation via API so teams can connect grouped issues to alert routing and incident workflows across projects and teams. Highlight focuses on incident mapping and release-window comparisons, and governance depth like granular RBAC and audit log depth needs validation.
Symbolication and build artifact dependency for readable stack traces
Better Stack’s source map and symbolication coverage depends on correct build artifacts so stack traces resolve consistently. TrackJS also relies on JavaScript symbolication for browser-focused issues, but it requires deeper setup for consistent event coverage across apps.
Choose based on how triage should move from grouped errors to reproduction
Start by picking the context path teams need for each grouped issue. Highlight and LogRocket anchor on session replay for front-end reproduction, while Datadog Error Tracking anchors on correlating errors to traces and logs for backend-first debugging.
Then select the automation and release linkage style that fits existing workflows. Sentry and GlitchTip build stable issue grouping that drives alert routing, while Datadog Error Tracking and Better Stack emphasize deployment-linked release health so regression detection ties directly to rollouts.
Select the primary debugging context you want attached to each grouped issue
If the shortest path to fixes is reproducing the user journey for JavaScript errors, Highlight and LogRocket attach session replay so the error lands next to the user actions that triggered it. If teams debug by following service behavior across traces and logs, Datadog Error Tracking keeps error triage correlated within a single operational context.
Pick alert routing behavior that matches triage capacity
For teams that need low-noise notifications that map to triage work, GlitchTip deduplicates by grouped exception so alerts stay stable. For teams that want grouping-driven release context to drive routing, Better Stack and Sentry connect grouped exceptions to deployments for regression-style comparisons.
Decide how release health should be calculated and displayed for regression detection
If deployment-linked release health needs to connect error volume shifts to rollouts across environments, Datadog Error Tracking and Sentry provide release-linked regression-style views. If release-context triage should happen from exception grouping without manual correlation, Better Stack emphasizes linking grouped exceptions to deployments for faster regression detection.
Match governance and automation needs to the platform’s configuration model
If error workflows require API-driven automation across projects and teams, Sentry is built around automation via API and release-aware issue grouping. If governance depth like granular RBAC and audit log depth must be validated, Highlight’s governance approach needs evaluation against the team’s control requirements.
Plan for symbolication quality based on your build pipeline
If readable stack traces depend on source map and symbolication coverage, Better Stack requires correct build artifacts for stack trace resolution. If browser JavaScript exception tracking and symbolication must work consistently across apps, TrackJS needs deeper configuration to maintain coverage.
Who gets the most operational value from these error monitoring setups
Different error monitoring implementations bias toward different debugging loops. Teams chasing front-end repro paths should prioritize session-linked exception tracking like Highlight and LogRocket, while teams relying on end-to-end operations should prioritize error-to-trace and log correlation like Datadog Error Tracking.
Teams that run many releases per environment should focus on deployment-linked release health and stable grouped exceptions so regression detection stays tied to rollouts. This guide’s picks also reflect differences in governance and automation surfaces that affect how alert routing and incident workflows scale.
Web teams that need user-journey reproduction for exceptions
Highlight links session replay context to grouped error issues so incidents map to the exact user journey and speed reproduction. LogRocket similarly provides session replay timelines for JavaScript errors but is more focused on replay presentation tied to each error.
Platform teams that already operate with traces and logs as the debugging backbone
Datadog Error Tracking correlates errors to traces and logs so teams can triage within a single operational context. The same triage workflow also benefits from deployment-linked release health tied to rollouts across environments.
Service teams that need stable alert routing tied to exception grouping
GlitchTip uses issue-centric alerting with deduplication by grouped exception so notification volume maps to triage work. Better Stack and Sentry also reduce noise through exception grouping, but GlitchTip’s routing model is explicitly issue-centric.
Teams that depend on disciplined build artifacts for symbolicated stacks
Better Stack’s source map and symbolication coverage depends on correct build artifacts, which makes build and artifact hygiene part of the value. TrackJS also needs deeper configuration for consistent JavaScript event coverage across apps when symbolication quality matters.
Common failure modes when rolling out error monitoring
Teams often misconfigure the pipeline that turns raw exceptions into stable grouped issues. That shows up as noisy alert routing, unstable grouping across deployments, or missing context that forces manual reproduction.
Another frequent failure mode is treating release health as decoration instead of a workflow input. If deployment markers and event metadata are inconsistent, release health comparisons become unreliable and regression detection breaks down.
Expecting session replay without coverage gaps across the relevant error paths
Highlight and LogRocket provide session-linked replay context, but LogRocket’s client-side instrumentation coverage can miss server-only error paths. Teams should validate instrumentation for both client and server error paths before relying on replay for root cause.
Treating alert deduplication as automatic even when event grouping rules vary
GlitchTip’s issue-centric alerting deduplicates by grouped exception, so grouping stability directly drives notification stability. Teams should verify how grouped exceptions behave across services and deployments to avoid repeated alerts that still require manual triage.
Assuming source maps and symbolication will work without build artifact discipline
Better Stack requires correct build artifacts for source map and symbolication coverage, and symbolicated stacks drive faster root cause reviews. Teams should test symbolication in every environment where stack trace enrichment is used for triage.
Enabling release health views without consistent deployment markers or event metadata
Sentry’s release health depends on deployment tracking and version association, and AppSignal’s deploy-correlated release health depends on deploy timing metadata. Teams should validate deployment markers and environment labeling in the event payload before expecting regression-style comparisons.
Underestimating configuration and governance time for cross-team automation
Sentry supports automation via API and release-aware issue grouping, but deep governance controls take time to configure across projects and teams. Highlight focuses on incident mapping and release-window comparisons, and advanced governance like granular RBAC and audit log depth needs validation.
How We Selected and Ranked These Tools
We evaluated Highlight, GlitchTip, Datadog Error Tracking, and New Relic Error Analytics for how their error aggregation models attach debugging context and how their release health ties grouped exceptions to deployments and versions. We scored feature depth at 40% using mechanisms like stable grouping, deduplicated alert routing, and session replay or trace and log correlation.
We weighted ease of use and value each at 30% by checking how quickly teams can apply configuration discipline for symbolication, deployment markers, and routing workflows. Highlight ranked highest because session replay context attaches to grouped error issues, which makes incident triage map to the exact user journey while release-window comparisons support regression tracking tied to deployments.
Frequently Asked Questions About error monitoring software
How do Sentry, Datadog Error Tracking, and New Relic Error Analytics handle issue grouping and fingerprinting?
Which tool is better for tying errors to user behavior in the browser?
How does release health work across Highlight, Better Stack, and AppSignal?
What breaks if error monitoring needs to deduplicate noisy alerts by grouped exception rather than by raw event?
When do API-driven event ingestion workflows matter most for teams with custom telemetry pipelines?
How do Sentry and Datadog Error Tracking differ for teams that already use tracing and log correlation?
Which tools provide strong request context for routing issues by user path or request context?
How do SDK instrumentation coverage differences affect client-side monitoring setups in LogRocket versus TrackJS?
What security and access control expectations should admins validate when using Sentry, Datadog Error Tracking, and Scout APM?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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