5 Datadog alternatives for teams who want more than just backend metrics

If you’re here, you’re probably weighing up Datadog alternatives. Maybe it’s the cost of Datadog. Maybe it's the complexity. Or maybe you’re just ready to go beyond just system alerts and understand how technical issues impact your actual users and business outcomes.

This article covers and compares 5 strong Application Performance Monitoring (APM) platforms built to detect, trace, and troubleshoot performance problems.

However, most of these platforms stop at diagnosing system health. They tell you what broke, but not how that breakage frustrated users, where it disrupted journeys, or what it cost you in revenue.

That’s where Contentsquare comes in.

Contentsquare doesn’t fully replace Datadog (or its alternatives). It complements them by connecting backend issues to real user behavior, friction, and business impact so you can prioritize the right fixes to protect conversions and revenue.

Keep reading to find out

• Where Datadog falls short

• 5 strong Datadog competitors to consider

• Why combining observability with experience analytics is the smarter way to fix what matters most

Key takeaways

• Datadog is great for backend monitoring, but it’s built for engineers and DevOps. It can’t connect technical issues to user frustration, quantify the revenue impact of errors, support long-term data analysis, or provide a scalable, self-serve experience for cross-functional teams

• Alternative APM platforms like Dynatrace, New Relic, and Splunk bring strong system monitoring and troubleshooting capabilities. However, they still lack the behavioral insights, mobile journey analysis, and cross-team accessibility needed to fully understand and improve the digital experience

• Contentsquare brings strong Digital Experience Monitoring (DEM) capabilities including real user monitoring, error detection, session replay, and AI frustration tracking. But critically, it also integrates with Datadog (and alternatives) to connect backend errors to user friction, prioritize fixes by business impact, and enable faster, smarter decision-making across teams

Limitations of Datadog: why you’re looking at competitors

Datadog gets a lot right on the technical side. But if your teams care about users, revenue, or the full digital experience—not just uptime—here are the main gaps you’ll want to know about.

1. Technical signals without full experience context

Datadog tells you when something breaks and where that breakage happened. It also offers limited session replays and user journey views, but it’s missing complete behavioral insights like frustration signals, hesitation patterns, and user drop-offs 

Without clear experience data, there’s little visibility into behavior signals like frustration or hesitation and no built-in way to quantify how backend issues affect conversions, loyalty, or long-term customer value. 

2. Cost, setup complexity, and limited retention

Datadog’s pricing model charges separately for each product and feature, which can lead to unexpected costs as usage scales. Initial setup and dashboard customization often require advanced configuration and technical knowledge. Standard data retention is limited to short periods (ex: APM errors are only stored for 15 days), making long-term analysis and compliance more difficult without paying extra.

3. Built for DevOps, not cross-functional teams

Datadog is built for engineers and SREs. That’s great for system-level monitoring—but less so for product managers, UX designers, and marketers who need fast, self-serve answers about the customer experience.

When error detection rests solely with technical teams, 2 things happen:

👉 1. Non-technical teams get stuck waiting for support

👉 2. Issues they could fix (like broken UI elements or confusing page flows) go unresolved

Without shared visibility, small problems slip through, big ones take longer to fix, and teams waste time chasing what they can’t see.

4. Prioritization that’s technical, not business-driven

Datadog flags what’s broken based on system performance or security severity, not revenue risk, user frustration, or drop-off potential.

Without clear business impact scoring, teams often fix what’s loudest—not what matters most to growth.

5 best Datadog alternatives to monitor and fix what matters

Most of the tools below are true Datadog competitors: they monitor systems, track performance, and alert you when something breaks.

Our top pick, Contentsquare (yes, that’s us 👋), doesn’t replace Datadog—it builds on it. We connect technical errors to real user behavior, disrupted journeys, and lost revenue so you can prioritize fixes that have the biggest business impact.

Because we know that while spotting errors is critical, prioritizing the ones that cost you customers is what drives growth.

1. Contentsquare

What it is

Contentsquare is an all-in-one Experience Intelligence platform that combines digital experience monitoring (DEM), behavioral analytics, and revenue impact quantification.

Who it’s best for

Product, UX, marketing, engineering, and digital teams who need to understand not just what broke, but how that breakage affected real users, journeys, and conversions—without heavy tagging or manual analysis.

Use Contentsquare Error Analysis to track error trends and dive into related replays, quantify impact, and view impacted customer journeys

What it does

• Combines Experience Analytics (Zone-Based Heatmaps, Journey Analysis) with Product Analytics, and Voice of Customer to provide a complete view of user behavior, journey friction, and experience performance across web and app

• Complete Digital Experience Monitoring (DEM), combining technical Error and Speed Analysis (JavaScript errors, API failures, slow loads) with Frustration Score (rage clicks, hesitations) and Session Replay to detect and resolve issues fast

• Impact Quantification to calculate the revenue and conversion impact of any issue or behavior

• Includes mobile app analytics to capture taps, scrolls, hesitations, and technical issues across iOS and Android, helping improve performance and conversions across the full mobile journey

• Enables every team to act through visual dashboards, smart alerts, and AI-driven prioritization

• Integrates real-time error and friction alerts into Slack, Teams, and Jira

• Seamlessly integrates with Datadog, New Relic, Splunk, and others, extending your APM stack

Contentsquare’s Frustration Score highlights where users struggled most—so you can fix what matters fast

Why it’s better than Datadog

Datadog detects when something breaks. Contentsquare shows you what and why that issue costs—in lost conversions, user frustration, and revenue.

Instead of guessing which issues matter, teams prioritize by business impact and act faster, across every function.

And because Contentsquare integrates with your APM stack, it brings experience clarity into your existing workflows so engineering, product, and digital teams can act faster with shared context and clear priorities. 

Keep reading and we’ll show you exactly how this looks in practice below.

2. Dynatrace

What it is 

Dynatrace is an application performance monitoring (APM) platform that uses AI to detect, diagnose, and automate the resolution of complex technical issues across applications, infrastructure, and cloud environments.

Who it’s best for

DevOps, SRE, and IT teams at large enterprises that need deep technical visibility across multi-cloud environments, with strong automation and AI for complex troubleshooting.

What it does

• Monitors and traces application performance across distributed systems

• Detects anomalies and automates root cause analysis with Davis AI

• Tracks infrastructure health, network activity, and session performance metrics

• Surfaces backend technical errors like slowdowns, API failures, and service outages

Why it’s not enough

Like Datadog, Dynatrace excels at system-level monitoring. But when used alone, it leaves a critical gap: it doesn’t connect technical issues to real user experience or business outcomes.

Dynatrace can show when a transaction is slow or an API is failing. It doesn’t show whether that caused frustration, drove users to abandon a purchase, or led to lost revenue.

3. New Relic

What it is

New Relic is an all-in-one observability platform that combines APM, infrastructure monitoring, real user monitoring (RUM), logs, and AI-powered insights into a single connected platform.

Who it’s best for

DevOps, SRE, and engineering teams who want a flexible observability solution that integrates easily across cloud environments and developer tools.

What it does

• Monitors applications, infrastructure, networks, and user sessions with unified telemetry

• Offers APM, browser monitoring, mobile RUM, session replay, synthetics, and AI-driven root cause analysis

• Consolidates logs, events, metrics, and traces into one dashboard for faster troubleshooting

• Integrates with +780 platforms, cloud providers, and open-source tools, including Contentsquare

Why it’s not enough

New Relic gives a deep technical view of system health and user session performance. But like other APMs, it’s focused on engineers, making it hard for other digital teams to access or act on the data. Without broader visibility, opportunities to improve user experience or drive business impact often get missed.

However, you can overcome these limitations by integrating New Relic with Contentsquare, as one of our customers, showed us. 

🎥 See it in action: during a major ecommerce replatform, leading UK sofa retailer DFS combined Contentsquare Experience Monitoring with New Relic to prioritize fixes based on customer impact and conversion risk.

By identifying a critical checkout API error immediately post-launch, the team accelerated mean time to resolution and protected the customer journey, helping deliver a -20% reduction in load time and a -9% reduction in bounce rate.

Read (and watch) the full story

4. Splunk AppDynamics

What it is

AppDynamics is now part of the Splunk Observability platform, combining infrastructure monitoring, application performance management (APM), and business transaction tracing into a single solution.

Who it’s best for

IT operations, DevOps, and SRE teams at enterprises that need deep technical visibility across cloud and hybrid environments, particularly those already invested in Splunk’s ecosystem.

What it does

• Monitors application, server, database, and network health across environments

• Traces backend business transactions triggered by user actions, like checkout flows or login requests

• Uses AI-assisted root cause analysis to accelerate technical troubleshooting

• Connects system performance with business service health to support incident response

Why it’s not enough

Splunk AppDynamics gives a strong technical view of system and service performance. It can trace when a backend transaction slows down or fails, but it stops short of showing the real-world impact on users or revenue.

It doesn’t quantify how performance issues drive frustration, cause drop-offs, or affect conversions. Without deeper experience analytics, teams risk solving technical problems without understanding which ones are actually hurting growth, engagement, or the bottom line.

5. Noibu

What it is

Noibu is is an entry level digital experience monitoring (DEM) platform focused on detecting, investigating, and resolving critical ecommerce site errors that impact conversions and revenue.

Who it’s best for

It is only suitable for Ecommerce websites, since you have to have a predefined average order value to calculate revenue impact.

What it does

• Continuously monitors ecommerce sites for loading time, JavaScript errors, API failures, and checkout disruptions

• Surfaces the most critical errors based on estimated financial impact and severity

• Provides full session replays to investigate issues quickly

• Prioritizes fixes with AI-powered recommendations and real-time alerting

• Integrates with leading ecommerce platforms like Shopify, Magento, Salesforce Commerce Cloud, and more

Why it’s not enough

Noibu can only detect and prioritize revenue-impacting errors for ecommerce sites that cross the threshold of 10% site-wide or 5% in add-to-cart/checkout stage. But it’s focused on technical troubleshooting, not full experience analytics. 

As a result, it distorts the priority list by inaccurate measurements and not contextualizing core-web-vitals based on your real impact, but only to some benchmark based off of 35mio pageviews from existing Noibu-Customers. Unspecific to your industry, unspecific to your brand. It also misses broader user signals like frustration, hesitation, or self-reported issues. It lacks tools like heatmaps and journey analysis to visualize how problems disrupt the experience and how to fix them.

As said earlier, Noibu is built specifically for ecommerce, making it virutally useless for teams in other industries or higher traffic numbers that might not have 10% conversion impact of just one error (source).

Why Contentsquare is the best complement to every Datadog alternative

Tools like Datadog, Dynatrace, and New Relic are built to monitor technical health. They alert you when something breaks: an API fails, a page slows down, or an error rate spikes.

But once the alert fires, you’re left with bigger questions:

• How did the issue affect real users?

• Where did it cause frustration or drop-offs?

• How much revenue or engagement did it actually cost?

That’s the gap between observability and digital experience monitoring—and it’s where Contentsquare comes in.

Here’s what you can do by combining Contentsquare with any Datadog alternative.

1. Surface technical issues and user frustration together

Datadog (or any APM) alerts you to backend failures, like slow APIs or server outages.

But users don’t just drop off because of technical issues. More likely, invisible UX friction—slow load times, broken elements, rage clicks, or confusing layouts—are causing users to leave without purchasing.

Contentsquare fills in that critical missing context.

• Frustration Score detects behavioral signals like rage clicks, repeated interactions, hesitations, and dead clicks, so you can see exactly where users are getting stuck and irritated​

• Error Analysis tracks frontend issues like JavaScript errors, failed clicks, API failures, and network slowdowns, and links them to affected sessions for easy investigation​

• Speed Analysis pinpoints slow-loading elements and quantifies how much they’re dragging down conversions or increasing bounce rates​

Use Speed Analysis to pinpoint slow-loading elements and get actionable fixes

• Session Replay shows you exactly what users saw and did before, during, and after an issue—giving you full context to troubleshoot faster, validate errors, and understand how problems impacted the experience

Use Session Replay to see the impact of any error on user experience and behaviour

2. Prioritize fixes by business impact, not just technical severity

Most APMs rank issues based on system metrics: CPU load, error frequency, latency spikes. Contentsquare’s Impact Quantification capability ranks issues by what matters most to your business: conversion loss, missed revenue, and user drop-off, so you can focus on the most impactful fixes first.

Use Impact Quantification to see the business cost of every issue—and prioritize accordingly

3. Investigate incidents faster with real user context

Catching an error is only the start. Session Replay and Journey Analysis  let you jump straight to the sessions where the error occurred, see what users saw in real time, and understand exactly how the issue affected their journey. 

Plus, if you're investigating complaints from Voice of Customer feedback or contact center tickets, you can use the Session Replay event stream to pinpoint technical errors and spot patterns across users. A few clicks show you how many others were affected, helping you validate the issue and prioritize accordingly.

Journey Analysis showing how an API error makes users bounce

4. Enable every team to act, not just engineering

APM dashboards are built for DevOps and SRE teams, which limits their impact across the org. 

By contrast, Contentsquare’s visual dashboards, AI alerts and automated insights make it easy for product, UX, marketing, and support teams to access and act on insights without technical knowledge.

🎥 See it in action: leading Australian insurance brand nib uses Contentsquare to give multiple teams direct access to granular digital experience insights, without relying on engineering.

Digital Growth & Performance, IT and UX, DevOps, Content, and Marketing teams all use Contentsquare to surface frontend errors, track customer journeys across landing pages and blogs, and investigate user behavior at the element level.

For Clare Powell, Senior Manager, Digital Growth and Performance at nib: “It would be hard to ever go back now that we’ve used Contentsquare!”

Read the full story

By connecting backend monitoring with experience analytics, Contentsquare enables teams to move faster, prioritize smarter, and fix what matters most to users and to the business.

Contentsquare vs. Datadog at a glance: why you need both

As we’re explained above, Datadog and Contentsquare solve different aspects of the same problem.

Datadog monitors your systems and infrastructure. It alerts you when performance breaks down. 

Contentsquare monitors user experience. It shows you how technical issues frustrate users, disrupt journeys, and impact revenue.

Used together (with our integration) they connect system health to business outcomes helping every team prioritize smarter, act faster, and deliver better experiences.

Here’s how they compare across common goals:

When you connect Datadog’s technical detection with Contentsquare’s experience insights, you move from spotting issues to understanding their real impact, and fixing what matters most.

Here’s how this might look in practice:

• Datadog flags a backend slowdown ➡️ Contentsquare Journey Analysis shows where users dropped off and how conversions dropped as a result

• Datadog catches a failed API call ➡️ Contentsquare Impact Quantification highlights how many users encountered the failure, and how much revenue was lost as a result

• Datadog detects an outage ➡️ Contentsquare surfaces frustrated sessions, rage clicks, and abandoned carts linked to the incident

Analyze any error in Contentsquare to quantify impact, see how it affects users, and prioritize fixes

Pro tip💡 Contentsquare integrates with Datadog and every major observability platform like New Relic, Splunk, and Dynatrace, so you can enrich your digital experience insights no matter which APM solution you use.

Discover our technology partners

Final take: pairing APM with experience analytics is the smarter move

You came here looking for Datadog alternatives. But if you swap Datadog for a similar tool, you’re only solving part of the problem.

Swapping one APM for another won’t fix the experience gap. Tools like Datadog, Dynatrace, and New Relic are built to monitor technical performance. But on their own, they can’t tell you how those issues impact your users or your bottom line.

Contentsquare fills that gap. It works alongside your existing APM to show where users are getting frustrated, what’s driving drop-offs, and which fixes will protect revenue and loyalty.

For teams that care about growth, customer experience, and making faster, smarter decisions, Contentsquare isn’t just a nice-to-have: it’s the strategic layer your APM stack is missing.

And you don’t have to take our word for it: according to Forrester, businesses using Contentsquare see a 602% ROI and recover over $3.2 million in revenue by fixing friction in the digital journey.

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