Pick Datadog if you want one vendor across infrastructure, logs, security and software delivery tooling, with a free trial to start. Pick New Relic if you want a single data layer built on OpenTelemetry and other open standards, with app performance tied to business results and a free plan. Neither is the clear overall winner: it depends on whether security sits in scope.
Before you compare dashboards, settle one thing: is your team shopping for monitoring, or for monitoring and security in the same contract? Answer that and most of this decision makes itself. One of these vendors now sells SIEM, code security and cloud posture checks next to its observability tools. The other stays focused on observability and leans hard on open standards.
To keep things readable, I'll call the first one the suite and the second one the data-layer platform, because that's the clearest way to describe how each one is put together.
The short verdict, up front
Choose Datadog if you'd like infrastructure, APM, logs, security and delivery tools such as feature flags from a single vendor, and you're happy working across many separately named products. Choose New Relic if your engineers care about OpenTelemetry, Prometheus and eBPF, want one unified data layer, and want app performance linked to business outcomes. There's no overall winner here. They're aimed at different shapes of team, so let your scope decide.
How far each menu now reaches
Most comparisons of this pair treat them as two monitoring tools. That's out of date. The suite's menu runs well past monitoring into SIEM, code security, app and API protection, feature flags, experiments and even product analytics. So you're not comparing like with like: it's a monitoring-plus-security bundle against a platform that leads with observability.
The data-layer platform has moved too, just in a different direction. Its home page headline is about AI that predicts and acts, and it now sells monitoring for AI models themselves, including agentic monitoring and token usage. If your product calls large language models, that's worth a close look, because it's a line of work the suite's home page doesn't headline.
Row by row
| Area | Datadog | New Relic |
|---|---|---|
| Breadth | Infrastructure, applications, data, logs, cloud security, SIEM, digital experience, software delivery and service management | Observability first: APM, digital experience, AI monitoring, infrastructure and logs |
| Security | Code security, CSPM, SIEM, app and API protection | No security product line shown on its site |
| AI agent | Bits AI agents that chat, investigate and remediate issues | SRE Agent for automated remediation, plus monitoring of AI models and token usage |
| Open standards | OpenTelemetry isn't headlined on its home page | OpenTelemetry, Prometheus, StatsD and eBPF named in its unified data layer |
| Data approach | Many separate named products, such as Database Monitoring, Observability Pipelines and Error Tracking | One unified data layer, queryable across warehouses and lakes without moving data (vendor claim) |
| Product analytics | Product Analytics, Experiments and Feature Flags inside its digital experience and delivery menus | Digital experience monitoring with session replay and AI friction detection |
| Getting started | Free trial stated | Free plan stated |
Open standards and where your data sits
This is the theme that should matter most to an engineering lead. The data-layer platform names OpenTelemetry, Prometheus, StatsD and eBPF as the open standards behind its unified layer, and its site says you can query data across warehouses and lakes without moving it. If you've already instrumented your services with OpenTelemetry, that message is aimed squarely at you.
The suite takes a different route. Its menu is a long list of separately named products, from Database Monitoring to Observability Pipelines to Error Tracking. That isn't a weakness in itself. Some teams like picking the exact module they need. But it's a different mental model from one layer that holds everything, and you'll want to know which one your team prefers before you commit. If databases are your main worry, our list of the best database monitoring tools goes deeper on that one job.
The AI agents on each side
Both vendors now sell an AI helper for incidents. The suite's Bits AI agents chat, investigate and remediate issues, and the vendor describes the whole offer as autonomous observability and security. The data-layer platform has an SRE Agent for automated remediation and pitches what it calls intelligent observability.
On the face of it these sound alike. The difference is what sits around them: one agent works inside a bundle that includes security tooling, the other works on a single data layer and comes with monitoring for your own AI models. For a wider view of where AI is turning up in engineering work, see our guide to AI tools for DevOps teams.
Session replay, flags and the product team
Here's a difference people miss. The suite lists Product Analytics, Experiments and Feature Flags inside its digital experience and software delivery menus, so a product manager could plausibly live in the same tool as the on-call engineer. The data-layer platform offers digital experience monitoring with session replay and AI friction detection, which is closer to watching where users get stuck than running experiments on them.
If your product and engineering teams want one shared tool, that tips things towards the suite. If you already run a separate analytics product and only need to see where users struggle, the data-layer platform's session replay may be all you need.
Four other observability platforms to weigh
If neither feels right, these four came up as the closest rivals. Our page on alternatives to Dynatrace covers more of the same ground.
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Dynatrace
Best for: Large enterprises wanting AI-powered observability with autonomous actions; the vendor offers a free trial.
Honeycomb
Best for: Engineers debugging complex distributed software and AI systems with production context, with tools to investigate with agents.
SigNoz
Best for: Teams that want open-source traces, metrics and logs in one place on open standards, either self-hosted or in the cloud.
Better Stack
Best for: Smaller teams after uptime monitoring, incident management, status pages, logs and eBPF-based tracing together, plus an AI SRE for root cause analysis.
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Frequently asked questions
Does New Relic sell security tools like Datadog does?
Its site doesn't show a security product line. The suite sells code security, CSPM, SIEM and app and API protection next to its monitoring.
Which one supports OpenTelemetry?
The data-layer platform names OpenTelemetry, Prometheus, StatsD and eBPF as open standards in its unified data layer. The suite doesn't headline OpenTelemetry on its home page.
Is there a free option on either?
One states a free trial and the other states a free plan. Check each sign-up page for what's included before you instrument anything.
Can either monitor AI models and LLM usage?
The data-layer platform sells AI and agentic monitoring of models and token usage. The suite's home page doesn't headline the same thing.
Which is better for a product team as well as engineers?
The suite lists Product Analytics, Experiments and Feature Flags. The other offers session replay and AI friction detection inside digital experience monitoring.
