The Observability Duopoly
Datadog and New Relic dominate the enterprise observability market. Both provide APM, infrastructure monitoring, log management, synthetic monitoring, and distributed tracing. Both have expanded into AI/ML-powered insights. And both have pricing models that can surprise engineering leaders who don't manage usage carefully.
Here's how to think through the choice.
Philosophy Difference
Datadog is a platform-first company. The strategy is to bring every observability and security function into one unified platform — metrics, logs, traces, RUM, security monitoring, CI visibility, and more. Datadog wins on integration depth; when everything is in Datadog, correlation across signals is seamless.
New Relic underwent a significant strategic shift in 2022-2023 with its user-based pricing model and "all capabilities for free" positioning. New Relic's bet: lower the cost barrier, give teams full platform access, and compete on simplicity and value. It's a different strategy that has gained significant traction among cost-conscious engineering teams.
APM (Application Performance Monitoring)
Datadog APM is widely regarded as the industry gold standard. Distributed tracing with auto-instrumentation for dozens of languages and frameworks, service maps that visualize dependencies automatically, and flame graphs for identifying bottlenecks are all excellent. The AI-powered anomaly detection (Watchdog) proactively surfaces issues without requiring manual alert configuration.
New Relic APM is genuinely strong and meaningfully improved since 2022. Auto-instrumentation coverage is broad, the distributed tracing interface is clean, and the correlation between APM and infrastructure metrics is good. For most engineering teams, New Relic APM is sufficient.
Verdict: Datadog maintains an edge on APM depth and the Watchdog AI features.
Infrastructure Monitoring
Datadog's infrastructure monitoring is comprehensive: cloud accounts (AWS, GCP, Azure), Kubernetes, containers, serverless, network performance — all with 600+ integrations. The Kubernetes monitoring in particular is exceptional, with native support for pod-level metrics, deployment tracking, and cluster health.
New Relic's infrastructure monitoring has improved but has fewer integrations and less depth on Kubernetes and container monitoring specifically. For traditional server-based infrastructure, the gap is smaller.
Verdict: Datadog for complex cloud-native infrastructure.
Log Management
Both platforms offer log management, but at very different price points.
Datadog logs pricing is consumption-based and can become one of the largest line items for organizations with high log volume. Teams frequently implement aggressive log filtering and sampling to manage Datadog log costs.
New Relic's log management is included in the platform and uses the same GB-based ingest pricing as all other New Relic data. For high-volume logging, New Relic is substantially cheaper.
Verdict: New Relic on cost for log-heavy workloads.
Pricing Models
This is where the comparison gets complex.
Datadog pricing is consumption-based and multi-dimensional:
- Infrastructure hosts: $15-23/host/month
- APM hosts: $31/host/month additional
- Log ingestion: $0.10/GB (retention billed separately)
- Synthetic tests, RUM sessions, etc. — all billed separately
Datadog bills can grow quickly, particularly for organizations running many hosts with full APM and logging enabled. Bills of $50K-$500K/month for large engineering organizations are common.
New Relic pricing model (as of 2025-2026):
- Standard: Free up to 100GB data ingest/month, 1 full platform user
- Pro: $0.35/GB additional ingest, $349/month per full platform user
- Enterprise: Volume pricing
New Relic's model is more predictable and often significantly cheaper for mid-size teams. The free tier is genuinely useful for small teams and projects.
Verdict: New Relic is cheaper for most teams outside large enterprises.
Dashboards and Visualization
Datadog's dashboard system is flexible and powerful. The ability to create custom dashboards combining metrics, logs, traces, and events from any integration into a single view is one of Datadog's strongest features. The polish is high.
New Relic's dashboards use NRQL (New Relic Query Language), which is powerful but requires learning. The visualization quality has improved significantly. For teams comfortable writing queries, NRQL provides a lot of flexibility.
Verdict: Datadog on dashboard ease of use; New Relic on query power for technical users.
AI and Intelligent Alerting
Datadog's Watchdog is the strongest AI observability feature in the market — it proactively surfaces anomalies in APM and infrastructure metrics without requiring alert configuration. Datadog's AIOps features for alert correlation and noise reduction are mature.
New Relic's AI observability is improving but is behind Datadog on proactive anomaly detection. Its alert intelligence for reducing noise is solid.
Verdict: Datadog
Which to Choose
Choose Datadog if:
- You're running complex cloud-native infrastructure at significant scale
- Observability integration across many signals (APM, infra, security, CI) is critical
- You have budget for premium observability and need the best-in-class APM
- You're a large engineering organization where Datadog's breadth justifies the cost
Choose New Relic if:
- Cost predictability is a significant factor
- You're a growing startup or mid-size engineering team
- Full platform access on a more reasonable budget is the priority
- Your log volume is high and you don't want to pay Datadog log prices
Evaluate Both Before Committing
Observability platform decisions are sticky — migrating instrumentation across a large codebase is painful. Use Trackr Research to run independent assessments on both platforms and get a current picture of pricing, user sentiment, and capability before you commit.