Datadog excels in log management and multi-cloud visibility with a flexible pricing model, while Dynatrace specializes in application performance monitoring with superior AI-driven root cause analysis and lower pricing at enterprise scale.
Datadog vs Dynatrace — Side-by-side comparison on A Versus B
Choose Datadog if you need unmatched log management, multi-cloud flexibility, and prefer pay-per-use pricing for variable workloads. Choose Dynatrace if you prioritize AI-driven root cause analysis, require a unified observability platform with simpler deployment, and value predictable fixed pricing for enterprise environments.
Cloud-native observability platform with comprehensive log management, metrics, traces, and synthetics.
AI-powered application performance monitoring platform with automated root cause analysis and unified observability.
Structured comparison data for Datadog vs Dynatrace with 152 attributes.
APM (Application Performance Monitoring)
Default Data Retention (included in pricing)
Typical Enterprise Annual Cost (1000 hosts)
Supported Technologies (Languages/Frameworks)
Monthly Platform Cost (100GB/month ingestion)
Mean Time To Resolution (MTTR) Improvement
Setup Time for Multi-Service Environment
Datadog vs Dynatrace — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Datadog vs Dynatrace.
Which platform is more cost-effective for high log volumes?
Datadog's pay-per-use model ($0.70-$1.40/GB) is more cost-effective for organizations with unpredictable or high log volumes (>500GB/day). Dynatrace's fixed pricing becomes more attractive for stable workloads under 200 hosts where log analysis is secondary. At 2TB daily log ingestion, Datadog typically costs $42K-$84K/month while Dynatrace would be $18K-$26K/month for equivalent hosts, making Dynatrace cheaper overall for traditional APM-focused use cases.
How does deployment time differ between the two platforms?
Dynatrace's OneAgent achieves full observability (APM, infrastructure, logs, real user monitoring) in 15-20 minutes across all hosts via a single deployment. Datadog requires deploying the main agent (5 min), APM agent (10 min), and log agent (10 min) separately, totaling 25-35 minutes per infrastructure type. For environments with 100+ hosts, this results in Dynatrace saving approximately 8-12 hours of deployment and configuration work.
Which platform is better for Kubernetes-native environments?
Dynatrace has superior Kubernetes support with automatic service discovery (no manual instrumentation), native Prometheus ingestion, and automatic containerization tagging. Datadog requires additional configuration through DaemonSets and requires more manual annotation for full visibility. For pure Kubernetes environments, Dynatrace reduces setup time by 60% and provides out-of-the-box multi-environment visibility across staging and production.
Yes, but rarely recommended. Both platforms can read from the same sources (Prometheus, StatsD, OpenTelemetry), but running both agents on the same infrastructure creates 15-20% CPU overhead and doubles observability costs without proportional benefit. Most enterprises choose one platform per business unit, using Datadog for log-heavy workloads and Dynatrace for performance-critical applications.
Which platform has better customer support for troubleshooting?
Dynatrace's 2024 Gartner report shows 89% customer satisfaction with support (vs. 82% for Datadog). Dynatrace includes 24/7 premium support at enterprise tier with dedicated technical account managers; Datadog requires additional paid support tiers. However, Datadog's community (150,000+ organizations) offers faster crowdsourced solutions. Enterprise customers report Dynatrace support responds to critical issues in <1 hour; Datadog averages 2-3 hours.