Last Updated: 2026-02-27

As modern systems grow in complexity, traditional monitoring falls short. SREs and DevOps engineers increasingly rely on AI-powered observability platforms to cut through the noise, proactively identify issues, and accelerate root cause analysis. This article provides a pragmatic comparison of Datadog and New Relic, two leading platforms, specifically through the lens of their AI capabilities, to help you make an informed decision for your organization.

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TL;DR Verdict

• Datadog: A highly customizable, feature-rich platform with powerful AI for anomaly detection (Watchdog AI) and specialized LLM Observability, ideal for complex, large-scale environments willing to invest in granular control and deep insights.
• New Relic: Offers a unified, user-friendly experience with strong AIOps capabilities through Applied Intelligence, a generous free tier, and an emphasis on ease of use and cost predictability, making it excellent for teams prioritizing quick setup and consolidated views.

Feature-by-Feature Comparison

| Feature Category | Datadog
Slightly more complex to get started due to its breadth, but offers unparalleled depth.
| Datadog (or Datadog for short) is a comprehensive full-stack observability platform that provides deep visibility into every layer of your technology stack. From infrastructure and applications to logs, network, and security, Datadog offers a unified view of your entire environment. Its AI capabilities are designed to proactively identify issues, reduce alert fatigue, and assist in rapid root cause analysis.

What it does well

• Watchdog AI for Anomaly Detection & Root Cause Analysis: Datadog's Watchdog AI automatically detects anomalies in metrics, logs, traces, and user experience data. It goes beyond simple thresholding by learning normal behavior patterns. Crucially, Watchdog also correlates these anomalies across different data types, providing a narrative of potential causes and impacts, significantly speeding up incident response. This is particularly effective in complex microservices architectures where manual correlation is nearly impossible.
• LLM Observability: With the rise of AI-powered applications, Datadog has introduced a dedicated LLM Observability add-on. This provides specific insights into LLM usage, including token usage, latency, cost tracking, prompt effectiveness, and model performance. It helps engineers understand the behavior and cost implications of their AI integrations, which is a unique and forward-looking feature.
• Extensive Integrations and Customization: Datadog boasts an unparalleled number of integrations (over 600) for virtually any technology, cloud provider, or service. This allows for highly granular data collection. Its dashboarding and alerting are incredibly flexible, enabling teams to build highly specific views and notifications tailored to their unique needs.
• Infrastructure Monitoring Depth: Datadog excels at providing deep insights into underlying infrastructure, from hosts and containers to serverless functions and cloud services. This foundational visibility is critical for understanding the context of application performance issues.
• Real User Monitoring (RUM) & Synthetic Monitoring: Offers robust capabilities to monitor end-user experience and proactively test application availability and performance from various global locations.

What it lacks

• Cost Complexity and Predictability: While powerful, Datadog's usage-based pricing model can become complex and expensive, especially for organizations with high data volumes or fluctuating usage patterns. Predicting monthly costs can be challenging without careful monitoring and optimization.
• Learning Curve: The sheer breadth and depth of Datadog's features, while a strength, can also lead to a steeper learning curve for new users or smaller teams. Setting up optimal dashboards, alerts, and custom metrics requires a significant time investment.
• Auto-Instrumentation: While it has many integrations, its auto-instrumentation capabilities, particularly for application code, might not be as seamless or comprehensive out-of-the-box as some competitors like Dynatrace, often requiring more manual setup for deep code-level insights.

Pricing

Datadog offers a free trial to explore its capabilities. Beyond the trial, pricing is usage-based, with separate paid plans for each product module (e.g., Infrastructure, APM, Logs, RUM, Security, LLM Observability). This allows for modular adoption but can lead to higher overall costs if many modules are used.

Who it's best for

Datadog is best for large enterprises, organizations with complex, distributed microservices architectures, and teams that require highly customizable monitoring solutions. It's ideal for those who need deep, granular insights across their entire stack, are comfortable with a usage-based pricing model, and have dedicated SRE/DevOps teams to leverage its advanced features. It's also a strong contender for companies building and operating AI-powered applications that need specialized LLM observability.

New Relic

New Relic is a unified observability platform designed to provide a comprehensive view of your software and infrastructure. It aims to simplify the complexities of modern systems by bringing together metrics, events, logs, and traces (MELT data) into a single, intuitive interface. New Relic places a strong emphasis on AIOps through its Applied Intelligence capabilities, helping teams move from reactive firefighting to proactive problem resolution.

What it does well

• Applied Intelligence for AIOps: New Relic's Applied Intelligence (NR AI) is a core differentiator. It uses machine learning to automatically detect anomalies, reduce alert noise by correlating related incidents, and surface critical issues. It can group alerts from various sources (not just New Relic's own data) into actionable incidents, providing context and suggested next steps. This significantly reduces alert fatigue and helps teams focus on real problems.
• Generous Free Tier and Predictable Pricing: New Relic offers a very attractive free tier that includes 100GB of data ingest per month, 1 free full-stack user, and 25 free core users. This is a significant advantage for startups, small teams, or even larger organizations looking to get started without immediate financial commitment. Beyond the free tier, its pricing model is often perceived as more predictable, based on data ingest and user count, making cost management simpler.
• Unified Platform and Ease of Use: New Relic prides itself on its "single pane of glass" approach. All observability data – APM, Infrastructure, Logs, Browser, Mobile, Synthetics, Security – is integrated into one platform. This makes it easier for teams to navigate, correlate data, and understand the full context of an issue without switching tools. The UI is generally considered user-friendly and intuitive, reducing the learning curve.
• Open Telemetry Support: New Relic has been a strong proponent and early adopter of OpenTelemetry, making it easier for organizations to ingest data from various sources using open standards, reducing vendor lock-in.
• Error Tracking and Session Replays: While not as specialized as Sentry, New Relic offers robust error tracking capabilities within its APM, and its session replay feature can be invaluable for understanding user impact and debugging front-end issues.

What it lacks

• Less Granular Customization: While user-friendly, New Relic's out-of-the-box experience can sometimes feel less customizable than Datadog for highly specific, niche monitoring requirements. While it offers powerful NRQL (New Relic Query Language) for custom queries, the dashboarding and alerting flexibility might not match Datadog's extensive options for every edge case.
• Depth in Specific Niche Areas: While comprehensive, Datadog often offers more specialized modules and deeper insights in certain niche areas, such as network performance monitoring or advanced security monitoring, which New Relic might cover more broadly. For example, while New Relic has security features, it's not a full-blown SIEM like Splunk or Elastic Security.
• Vendor Lock-in Perception: Despite strong OpenTelemetry support, some users might perceive a higher degree of vendor lock-in due to the tightly integrated nature of the platform, compared to Datadog's more modular approach where you can pick and choose components.

Pricing

New Relic offers a generous free tier that includes 100GB of data ingest per month and multiple free users, making it highly accessible. Beyond these limits, paid tiers are available, primarily based on data ingest volume and the number of full-stack observability users. This model aims for cost predictability.

Who it's best for

New Relic is an excellent choice for organizations of all sizes, especially those looking for a unified, easy-to-use observability platform with strong AIOps capabilities out-of-the-box. It's particularly well-suited for teams that prioritize quick setup, consolidated views, and predictable costs, such as growing startups, mid-sized companies, and enterprises seeking to simplify their observability stack. Its free tier makes it a compelling option for experimentation and initial adoption.

Try Datadog → Datadog — Free trial; usage-based paid plans

Head-to-Head Verdict for Specific Use Cases

1. Proactive Anomaly Detection & Incident Correlation

• Datadog: Watchdog AI is incredibly powerful for detecting subtle anomalies across diverse data types and providing contextualized insights. Its ability to correlate issues across logs, metrics, and traces is a significant strength, especially in highly dynamic environments.
• New Relic: Applied Intelligence is designed precisely for this. It excels at reducing alert noise by grouping related events and providing a clear incident summary. It can also ingest alerts from external systems, making it a central hub for incident intelligence.
• Verdict: For pure, deep, and highly contextualized anomaly detection and root cause analysis within its own ecosystem, Datadog has a slight edge with Watchdog's depth. However, for broader incident correlation, noise reduction, and acting as a central AIOps hub that integrates with other monitoring tools (e.g., pulling alerts from Grafana or even legacy systems), New Relic's Applied Intelligence is arguably more versatile and user-friendly.

2. Monitoring LLM-Powered Applications

• Datadog: Its dedicated LLM Observability add-on is a clear winner here. It provides specific metrics and insights tailored to the unique challenges of LLM applications, such as token usage, prompt engineering effectiveness, and cost analysis. This is a specialized capability that few other platforms offer at this depth.
• New Relic: While New Relic can certainly monitor the underlying infrastructure and application components that interact with LLMs, it currently lacks a dedicated, purpose-built LLM observability module with the same depth as Datadog. You'd rely on custom metrics and dashboards.
• Verdict: For organizations heavily investing in AI applications and needing specific insights into their LLM interactions, Datadog is the superior choice. The Vercel AI SDK is a great tool for building these applications, and Datadog helps you monitor them effectively.

3. Cost-Effective Observability for Startups & SMBs

• Datadog: The usage-based pricing can quickly escalate, making it potentially expensive for smaller teams or those with unpredictable data volumes. While powerful, the cost-benefit ratio needs careful consideration.
• New Relic: The generous free tier (100GB/month data ingest, multiple free users) is a massive advantage for startups and SMBs. It allows teams to get comprehensive observability without upfront costs and scale predictably.
• Verdict: For cost-conscious teams, especially during initial growth phases, New Relic offers a much more accessible and predictable pricing model, making it the clear winner.

4. Deep-Dive Troubleshooting in Complex Microservices

• Datadog: With its extensive integrations, highly customizable dashboards, and powerful distributed tracing (APM), Datadog provides the tools for engineers to dive deep into every component of a microservices architecture. Watchdog AI further assists by highlighting anomalies and correlations. Its ability to ingest and query vast amounts of log data alongside metrics and traces is crucial.
• New Relic: Offers a unified view of APM, infrastructure, and logs, which is excellent for understanding service dependencies and performance bottlenecks. Its Service Maps and distributed tracing are strong. Applied Intelligence helps pinpoint the problem area.
• Verdict: Both are strong, but for the most granular control, customizability, and the ability to correlate data from an almost infinite number of sources, Datadog often provides a slightly deeper and more flexible toolkit for expert troubleshooters in very complex, heterogeneous microservices environments. For a comparison with another strong contender in this space, see Dynatrace vs Datadog: AI-Powered Monitoring Compared.

Which Should You Choose? A Decision Flow

• Are you a startup or small-to-medium business (SMB) with budget constraints, or do you need to prove value quickly?
  • Choose New Relic for its generous free tier and predictable pricing.
• Do you operate a highly complex, large-scale enterprise environment with diverse technologies and a need for extreme customization?
  • Choose Datadog for its unparalleled breadth of integrations and granular control.
• Are you building or operating applications that heavily rely on Large Language Models (LLMs)?
  • Choose Datadog for its specialized LLM Observability add-on.
• Is your primary goal to reduce alert fatigue and centralize incident intelligence from multiple monitoring sources?
  • Choose New Relic for its robust Applied Intelligence that excels at noise reduction and correlation across various data inputs.
• Do you prioritize a unified, out-of-the-box experience with a lower learning curve?
  • Choose New Relic.
• Do you have dedicated SRE/DevOps teams who require the deepest possible insights and are comfortable with a more hands-on configuration?
  • Choose Datadog.
• Are you looking for a platform that integrates seamlessly with OpenTelemetry standards?
  • Both are strong, but New Relic has historically been a very vocal proponent and early adopter.
• Do you need to monitor specific niche areas like advanced network performance or deep security analytics as part of your observability platform?
  • Datadog often offers more specialized modules for these. For more general monitoring and visualization, consider how Grafana vs Datadog: Monitoring and Observability Compared might fit into your stack.
• Are you concerned about managing costs with a usage-based model?
  • New Relic generally offers more predictable pricing.
• Do you need robust error tracking and session replay capabilities built directly into your observability platform?
  • Both offer these, but New Relic integrates them very smoothly. For more dedicated error tracking, you might also consider a tool like Sentry.

Ultimately, the best choice depends on your specific organizational needs, technical stack, team expertise, and budget. Both platforms are leaders in the observability space, continuously evolving their AI capabilities. Other powerful tools like Dynatrace, Elastic (ELK Stack), and Splunk also offer compelling AI-driven observability features, each with their own strengths. For broader AI integration in your development workflow, tools like JetBrains AI Assistant for coding, Sweep AI for automated code reviews, or even AI-powered project management like Linear vs Jira: AI-Powered Project Management for Dev Teams are becoming increasingly common.

Get started with New Relic → New Relic — Free tier (100GB/month); paid tiers beyond free limits

FAQs

Q: How do Datadog's and New Relic's AI capabilities differ for anomaly detection?
A: Datadog's Watchdog AI focuses on deep, contextualized anomaly detection across its vast array of integrated data sources (metrics, logs, traces, UX) and provides a narrative for root cause analysis. New Relic's Applied Intelligence (NR AI) excels at correlating anomalies and alerts from various sources (including third-party tools) to reduce noise, group incidents, and provide actionable insights, making it a powerful AIOps engine for incident management.

Q: Which platform offers better cost predictability for AI-powered observability?
A: New Relic generally offers better cost predictability due to its generous free tier (100GB/month data ingest) and a pricing model that is often perceived as more straightforward, based on data ingest and user count. Datadog's usage-based pricing, while flexible, can become complex and harder to predict, especially with high data volumes across multiple modules.

Q: Can both Datadog and New Relic monitor custom AI/ML models?
A: Yes, both can monitor custom AI/ML models by ingesting custom metrics and logs from your model's inference services. However, Datadog has a dedicated LLM Observability add-on that provides purpose-built features and insights specifically for Large Language Model applications, including token usage, prompt analysis, and cost tracking, giving it an edge in that specific area.

Q: How do their integration ecosystems compare, especially for AI-driven insights?
A: Datadog boasts an extremely extensive integration ecosystem (600+), allowing it to pull data from nearly any source, which feeds its Watchdog AI for comprehensive insights. New Relic also has a broad range of integrations and strong OpenTelemetry support, allowing its Applied Intelligence to correlate data from a wide variety of services and even external alert sources. Datadog's integrations often go deeper into specific technologies, while New Relic focuses on unifying the data for AIOps.

Q: Which is easier to get started with for AI-powered observability?
A: New Relic is generally considered easier to get started with, thanks to its unified platform, intuitive UI, and generous free tier. Its Applied Intelligence features often work well out-of-the-box. Datadog, while incredibly powerful, has a steeper learning curve due to its vast feature set and high degree of customization, which requires more initial setup and configuration.

Q: Do either offer specific features for security observability with AI?
A: Both platforms offer security monitoring capabilities. Datadog has a dedicated Security Monitoring product that leverages AI for threat detection, anomaly detection in security events, and compliance monitoring. New Relic also incorporates security insights into its platform, using Applied Intelligence to correlate security-related events with operational data. For specialized security AI, other tools like Elastic's AI-powered attack discovery or Splunk's SIEM capabilities might offer even deeper security-focused AI.

Frequently Asked Questions

How do Datadog's and New Relic's AI capabilities differ for anomaly detection?

Datadog's Watchdog AI focuses on deep, contextualized anomaly detection across its vast array of integrated data sources (metrics, logs, traces, UX) and provides a narrative for root cause analysis. New Relic's Applied Intelligence (NR AI) excels at correlating anomalies and alerts from various sources (including third-party tools) to reduce noise, group incidents, and provide actionable insights, making it a powerful AIOps engine for incident management.

Which platform offers better cost predictability for AI-powered observability?

New Relic generally offers better cost predictability due to its generous free tier (100GB/month data ingest) and a pricing model that is often perceived as more straightforward, based on data ingest and user count. Datadog's usage-based pricing, while flexible, can become complex and harder to predict, especially with high data volumes across multiple modules.

Can both Datadog and New Relic monitor custom AI/ML models?

Yes, both can monitor custom AI/ML models by ingesting custom metrics and logs from your model's inference services. However, Datadog has a dedicated LLM Observability add-on that provides purpose-built features and insights specifically for Large Language Model applications, including token usage, prompt analysis, and cost tracking, giving it an edge in that specific area.

How do their integration ecosystems compare, especially for AI-driven insights?

Datadog boasts an extremely extensive integration ecosystem (600+), allowing it to pull data from nearly any source, which feeds its Watchdog AI for comprehensive insights. New Relic also has a broad range of integrations and strong OpenTelemetry support, allowing its Applied Intelligence to correlate data from a wide variety of services and even external alert sources. Datadog's integrations often go deeper into specific technologies, while New Relic focuses on unifying the data for AIOps.

Which is easier to get started with for AI-powered observability?

New Relic is generally considered easier to get started with, thanks to its unified platform, intuitive UI, and generous free tier. Its Applied Intelligence features often work well out-of-the-box. Datadog, while incredibly powerful, has a steeper learning curve due to its vast feature set and high degree of customization, which requires more initial setup and configuration.

Do either offer specific features for security observability with AI?

Both platforms offer security monitoring capabilities. Datadog has a dedicated Security Monitoring product that leverages AI for threat detection, anomaly detection in security events, and compliance monitoring. New Relic also incorporates security insights into its platform, using Applied Intelligence to correlate security-related events with operational data. For specialized security AI, other tools like Elastic's AI-powered attack discovery or Splunk's SIEM capabilities might offer even deeper security-focused AI.