Dynatrace vs Datadog vs New Relic: Which APM Should You Choose in 2024?

Dynatrace, Datadog, and New Relic dominate the enterprise APM market. All three do observability — metrics, traces, logs — but they make different tradeoffs in AI capability, pricing model, ease of setup, and where they excel. This comparison is based on real-world usage, not marketing materials.

The short answer: Dynatrace wins on AI and automatic discovery; Datadog wins on integrations and developer experience; New Relic wins on pricing transparency and simplicity. The right choice depends on your team, your stack, and your budget.

At a Glance

Pricing: The Honest Comparison

Pricing in APM is notoriously opaque. Here's how each vendor structures it:

Dynatrace pricing

Dynatrace prices by host unit (HU). Each HU represents a certain amount of infrastructure — roughly one 4-core server. You purchase a bundle of HUs and get full-stack monitoring (metrics, traces, logs, synthetic, RUM) for those hosts.

Typical cost: ~$70-100/HU/month for full-stack. For a 50-host environment, expect $3,500-5,000/month.

What this means in practice: Predictable bills. As your host count grows, cost grows linearly. You're not surprised by a 3x bill because log volume spiked.

Gotchas: Dynatrace has a separate DPS (Dynatrace Platform Subscription) pricing model for Grail-based products. If you're using newer features (DQL, Grail log management), you're billed on data ingest volume, not just hosts. Understand which tier you're on before signing.

Datadog pricing

Datadog uses a SKU-based, usage-based model. Every product is priced separately:

• APM: per host per month
• Logs: per GB ingested + per GB retained (30 days, 90 days, etc.)
• Infrastructure: per host per month
• Synthetic: per test run
• RUM: per session or per GB
• Database Monitoring: per database host
• Network Monitoring: per host

Typical cost: A moderate environment with APM, logs (moderate volume), infrastructure monitoring, and synthetics can run $5,000-15,000/month. Large enterprises with high log volumes routinely see $50,000+/month bills.

What this means in practice: Datadog can be cheap if you use only one or two products. The moment you add log management or RUM, costs compound. Many teams get surprised by their first month's bill.

Gotchas: Log retention is separately priced. Retaining 30 days of logs costs more than 7 days. If your compliance requirements mandate 90-day retention, budget accordingly. Datadog's Flex Logs tier helps but adds architectural complexity.

New Relic pricing

New Relic switched to a consumption-based model: you pay for users (Full Platform Users at $99/user/month) plus data ingest ($0.30/GB after the first 100GB free/month).

Typical cost: For a 10-engineer team with moderate data ingest, $2,000-4,000/month. Scales with team size and data volume.

What this means in practice: New Relic is often the cheapest for smaller teams. The free tier (100GB/month + 1 free Full Platform User) is genuinely useful. The Core User tier ($0/user) has significant limitations — primarily metrics and basic dashboards, no full APM traces.

Gotchas: As teams scale, user costs become significant. A 50-person engineering org at $99/user = $4,950/month in user fees before data costs. Data ingest at scale with high-cardinality tracing can also surprise you.

Pricing verdict

For predictability: Dynatrace. You know what you're paying. For getting started cheap: New Relic. The free tier is real. For per-feature flexibility: Datadog. Pay only for what you use — but watch the bill.

AI and Anomaly Detection

This is where Dynatrace pulls ahead most clearly.

Dynatrace Davis AI

Davis is a deterministic causal analysis engine, not just anomaly detection. It:

• Learns baselines for every entity automatically
• Groups correlated anomalies into a single Problem
• Traces the causal chain from symptom to root cause
• Requires zero threshold configuration

Davis is opinionated. It makes a root cause decision. Sometimes it's wrong, but it's right often enough that teams report significant reduction in MTTR.

Best for: Teams that want answers, not just alerts. Complex microservices environments where manual correlation is impractical.

Datadog ML-based monitoring

Datadog's anomaly detection is ML-powered but threshold-configurable. You get:

• Anomaly monitors: detect deviations from normal using ML
• Outlier monitors: detect hosts behaving differently from peers
• Forecast monitors: alert when a metric is projected to breach a threshold
• Watchdog: automated anomaly surface in the Watchdog feed

Datadog's approach is more flexible — you configure monitors per metric — but requires more setup. There's no automatic root cause analysis engine equivalent to Davis. You see the anomalies; correlation is left to the engineer.

Best for: Teams that want fine-grained control over what triggers alerts. Good complement to strong engineering culture that prefers investigation over AI-driven conclusions.

New Relic Applied Intelligence

New Relic's AI capabilities sit between Dynatrace and Datadog:

• Alert correlation: groups related alerts into Issues, reducing noise
• Anomaly detection: automatic baseline learning per entity
• Incident Intelligence: correlates incidents across data sources

New Relic Applied Intelligence is less sophisticated than Davis for root cause analysis but significantly better than raw alert volume. It won't give you a named root cause, but it will group the 30 related alerts into 2 issues.

Best for: Teams currently drowning in alert noise who want noise reduction without the full complexity of Dynatrace.

Kubernetes Monitoring

All three have mature Kubernetes support. The differences are in depth and ease of setup.

Dynatrace Kubernetes

Strength: OneAgent DaemonSet with the Operator gives you automatic pod-level instrumentation, cluster metrics, and distributed tracing with no application code changes. Kubernetes events (evictions, OOMKills, probe failures) flow into Davis and get correlated with application anomalies.

Weakness: The Operator setup is more involved than Datadog's Helm chart. The DynaKube CRD has many options — good for power users, overwhelming for beginners.

Datadog Kubernetes

Strength: The datadog-agent Helm chart is simple and well-documented. The Kubernetes integration is comprehensive — node metrics, pod metrics, cluster-level metrics, Kubernetes events. The Datadog Cluster Agent reduces load on the Kubernetes API server.

Weakness: Distributed tracing in Kubernetes with Datadog requires configuring DD_TRACE_AGENT_PORT and ensuring the Agent DaemonSet is reachable from application pods. Auto-instrumentation (via the Admission Controller) requires additional configuration per language.

New Relic Kubernetes

Strength: The nri-bundle Helm chart is a one-liner deployment. New Relic's Kubernetes Cluster Explorer is visually excellent — probably the best K8s topology visualization of the three.

Weakness: Automatic distributed tracing across services in Kubernetes requires more manual SDK integration compared to Dynatrace. New Relic's K8s support has historically lagged behind Datadog and Dynatrace on depth, though it's improved significantly.

Kubernetes verdict: Dynatrace for best automatic instrumentation depth; Datadog for ease and breadth of integrations; New Relic for best visualization.

Synthetic Monitoring

Dynatrace Synthetic

• Browser monitors, HTTP monitors, clickpath monitors
• Private ActiveGate for internal environments
• Full visual waterfall, Core Web Vitals
• Synthetic failures surface as Problems in Davis (correlated with infrastructure)
• Recording via Chrome extension

Pricing: Included in full-stack license. 10 browser monitor hours per month free; additional hours billed per DEM unit.

Datadog Synthetics

• API tests, browser tests, multi-step API tests
• Private locations via a Docker-based private agent
• Continuous Testing for CI integration (record test, run in pipeline)
• Test recorder is more polished than Dynatrace's

Pricing: ~$5/1,000 API test runs, ~$12/1,000 browser test runs. Costs compound with high frequency.

New Relic Synthetics

• Simple browser, scripted browser (WebDriver-based), API monitors
• Private minions for internal testing
• No built-in recorder — scripts are written in Selenium/WebDriver style

Pricing: Included in New Relic One. 500 free simple checks/month; 10,000 scripted/API checks/month on paid plans.

Synthetic verdict: Datadog has the best recorder and CI integration. Dynatrace has the best correlation with APM data. New Relic is the most affordable.

Developer Experience and Dashboards

Dynatrace

Dynatrace's UI is powerful but dense. The learning curve is real — Smartscape, PurePaths, DEM units, management zones all need to be understood before you're productive. Once you are, the depth of data available is unmatched.

Dashboard builder is functional but less flexible than Datadog's. Custom charts have limitations compared to Datadog's query language.

Dynatrace's notebook feature and DQL (Dynatrace Query Language) are catching up — DQL is genuinely powerful for ad-hoc analysis.

Datadog

Datadog has the best developer experience of the three. Documentation is excellent, onboarding is fast, and the dashboard builder is the most flexible. The Metrics Explorer, Log Explorer, and Trace Explorer are all well-designed for investigation workflows.

Datadog's notebook feature, dashboards, and monitors all use the same query language (based on Metrics DSL + LogsQL) — consistency that Dynatrace and New Relic lack.

For organizations with strong engineering culture that want to own their observability stack, Datadog is the best fit.

New Relic

New Relic's NRQL (New Relic Query Language) is SQL-like and approachable. The query console is good. The New Relic One platform consolidates everything in one UI.

New Relic's entity-centric model — everything is an entity, entities have relationships — is clean conceptually. The UI is less cluttered than Dynatrace.

Integrations and Ecosystem

Datadog wins outright: 800+ integrations, broad cloud coverage (AWS, Azure, GCP), every major database, queue, and framework supported. If you use it in production, Datadog probably has an integration.

Dynatrace has 600+ technologies supported via OneAgent, plus an open API for custom instrumentation. The key difference: Dynatrace's integrations are often deeper (automatic instrumentation at the bytecode level) vs Datadog's which are often metric-collection based.

New Relic has 500+ integrations. Coverage is good for major cloud providers and popular frameworks. Less comprehensive on niche databases and infrastructure tools.

Who Should Use Each

Choose Dynatrace if:

• You have a complex microservices architecture and need automatic root cause analysis
• Your team doesn't have the bandwidth to build and maintain custom monitoring configurations
• You're willing to pay more for less manual configuration
• You need GDPR-compliant data residency (Dynatrace Managed, self-hosted)
• Your core concern is MTTR — finding problems faster, not building dashboards

Choose Datadog if:

• Your team values developer experience and fast onboarding
• You have diverse technology stack with many different integrations needed
• You want fine-grained control over every alert and monitor
• CI/CD integration and synthetic testing as a deployment gate are priorities
• Your engineering culture favors building custom observability over buying a complete solution

Choose New Relic if:

• Cost is the primary constraint, especially at small-to-medium scale
• You want to get started with a free tier before committing
• Your team likes SQL and wants a familiar query experience (NRQL)
• You want good Kubernetes visualization
• You don't need advanced AI root cause analysis

Where APM Ends and Testing Begins

All three platforms — Dynatrace, Datadog, and New Relic — are observability tools. They tell you what's happening in your running systems. They don't validate that your application is functionally correct before it ships.

The gap is functional testing: does the login flow actually work? Does checkout complete successfully? Does the search return relevant results? APM monitors these things in production, but by then a broken flow has already affected users.

This is why observability and functional testing are complementary, not competing. Tools like HelpMeTest run continuous end-to-end tests against your application — scripted user journeys that validate business-critical flows before and after deployment — and can run as a CI/CD gate. Your APM platform then monitors what happens in production.

Whether you choose Dynatrace, Datadog, or New Relic, the combination of pre-deploy functional validation and post-deploy observability gives you the full reliability loop. APM alone catches problems after users are affected. Testing before deploy prevents them.

The Bottom Line

If you're a 50-500 engineer organization with complex infrastructure and want to minimize the time your ops team spends triaging incidents: Dynatrace. The upfront learning curve and higher price pay off in reduced MTTR.

If you're a 10-100 engineer startup that values velocity, has diverse tooling, and wants integrations with everything: Datadog. Budget carefully — log costs in particular.

If you're cost-sensitive, have a smaller team, or want to start free and grow into a paid plan: New Relic. The free tier is genuinely useful and the pricing is the most transparent.

All three are production-grade tools used by companies running critical infrastructure at scale. The difference is in where each spends its engineering effort — and that determines which one fits your team's way of working.