vs.

Monitoring built for merchants

Q: We already have Datadog for monitoring.

Datadog is strong for backend systems, but it does not prioritize issues by business impact. Noibu focuses on ecommerce conversions and surfaces friction in the context of your revenue funnel so teams act fast on what matters.

Q: Our dev team relies on Datadog’s logs.

Logs are valuable, but non-technical teams struggle to interpret them. Noibu bridges technical depth with business context—developers get stack traces and payloads, while ecommerce and product teams get clear next steps and revenue impact.

Q: Datadog captures errors across the full stack — isn’t that enough?

Not if you need to know what those errors mean for checkout and revenue. Noibu pinpoints where errors, performance issues, and friction occur in the journey, linking them to checkout failures, cart abandonment, and lost revenue.

One solution, designed for action, built for ecommerce.

Claim your free site scan See the difference

The observability power merchants need — with the ecommerce insight Datadog was never built for. Capture every session, uncover every blocker, and act fast with ecommerce-first insights.

Trusted by the world’s biggest brands

Complete ecommerce monitoring for merchants

Cut through the noise to find blockers that matter. Noibu captures every session, front-end error, and performance slowdown, tying them directly to customer experience and revenue impact for faster, data-backed resolution.

How Datadog misses:

Focused on backend infrastructure, not real user experience.
Limited front-end visibility; user friction and cart issues go unseen.
Alerts trigger on system metrics, not conversion-impacting events.

Actionable insights dev & product teams can use

Noibu translates technical data into clear next steps. AI-generated context, stack traces, and HTTP payloads make every issue reproducible and diagnosable — linked to session replays and Jira for rapid fixes.

How Datadog misses:

Raw logs and traces with no ecommerce context or prioritization.
Requires manual correlation between customer reports and technical data.
Non-technical teams depend on developers to interpret backend errors.

Purpose-built for ecommerce growth

Noibu is designed for ecommerce funnels from PDP to checkout. Detect and prioritize issues that impact revenue, uncover checkout failures and cart abandonment triggers, and see exactly how friction affects conversions.

How Datadog misses:

Built for infrastructure monitoring, not ecommerce performance.
No visibility into checkout or cart abandonment errors.
Can't quantify lost revenue or prioritize by customer impact.

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Feature-by-feature comparison

See how Noibu brings full-funnel visibility to ecommerce — while Datadog’s complex dashboards and backend metrics miss the moments that matter most.

Datadog (general infrastructure monitoring)

Noibu (purpose-built for ecommerce growth)

Error detection & session replay

⚠️

Captures wide range of metrics & logs, but limited user-centric visibility into errors and sessions. Requires layering in multiple tools for true front-end UX and replay.

Captures 100% of front-end errors and session replays, including user friction, technical details, and custom data fields—plus server-side log details for certain ecommerce platforms.

Issue detail investigation

⚠️

Steep learning curve focused on logs & infra traces, without simplified explanations for business teams. Technical teams must interpret errors for product owners and business users.

AI-generated explanation of error code and impact for high-context investigation, reproduction, and resolution.

Prioritization & proactive alerting

❌

Prioritization, reporting, and alerting based on frequency or technical thresholds—CPU, latency, etc. Fails to consider impact to customers or conversions.

Prioritization & alerting based on impact to customers, conversions, and revenue mapped to funnel stages—supported by AI and human verification.

Issue reproduction & resolution

❌

Can’t easily link customer complaints or support tickets to user sessions with details needed to reproduce an error. Developers need additional time to gain context, triage, and investigate customer-reported incidents.

Link customer-reported issues to session replays, reproduction steps, and full root-cause technical details (e.g. browser & device details, stack traces, top URLs, page lifecycle events, core web vitals, custom data).

Performance monitoring

⚠️

Technical, raw application performance data with heavy focus on infrastructure. Difficult to translate to industry-standard metrics + actionable next steps.

Captures real user traffic to report site performance with actionable insight, revenue impact, ecommerce benchmarks, and technical details.

Ecommerce focus

⚠️

Broad DevOps observability across various industries. Requires heavy customization to serve ecommerce use cases.

Built for ecommerce with funnel-oriented workflows, ecomm partnerships & integrations, and industry expertise among customer success, support, product, and technical teams.

Error detection & session replay

⚠️

Captures wide range of metrics & logs, but limited user-centric visibility into errors and sessions. Requires layering in multiple tools for true front-end UX and replay.

Captures 100% of front-end errors and session replays, including user friction, technical details, and custom data fields—plus server-side log details for certain ecommerce platforms.

Issue detail investigation

⚠️

Steep learning curve focused on logs & infra traces, without simplified explanations for business teams. Technical teams must interpret errors for product owners and business users.

AI-generated explanation of error code and impact for high-context investigation, reproduction, and resolution.

Prioritization & proactive alerting

❌

Prioritization, reporting, and alerting based on frequency or technical thresholds—CPU, latency, etc. Fails to consider impact to customers or conversions.

Prioritization & alerting based on impact to customers, conversions, and revenue mapped to funnel stages—supported by AI and human verification.

Issue reproduction & resolution

❌

Performance monitoring

⚠️

Technical, raw application performance data with heavy focus on infrastructure. Difficult to translate to industry-standard metrics + actionable next steps.

Captures real user traffic to report site performance with actionable insight, revenue impact, ecommerce benchmarks, and technical details.

Ecommerce focus

⚠️

Broad DevOps observability across various industries. Requires heavy customization to serve ecommerce use cases.

Built for ecommerce with funnel-oriented workflows, ecomm partnerships & integrations, and industry expertise among customer success, support, product, and technical teams.

Switching from Datadog: What teams ask

“We already have Datadog for monitoring.”

Datadog is great for backend systems — but can it prioritize issues by business impact?

Noibu focuses on the issues that actually hurt ecommerce conversions, not just the most frequent ones. We surface friction in the context of your revenue funnel so teams act fast on what matters.

“Our dev team relies on Datadog’s logs.”

Can non-technical teams interpret them and take action?

Noibu bridges technical depth with business context. Devs get stack traces and payloads; ecommerce & product teams get clear next steps and revenue impact.

“Datadog captures errors across the full stack — isn’t that enough?”

Not if you want to know what those errors mean for your checkout funnel and revenue.

Noibu identifies exactly where errors, performance issues, and friction points occur in the customer journey, linking them to checkout failures, cart abandonment, and lost revenue, not just raw code issues.

Why ecommerce leaders switch to Noibu

Backend monitoring only goes so far. Noibu gives teams full-funnel visibility, revenue impact insights, and actionable fixes — right out of the box.

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