CloudWatch vs Dynatrace: Cost, Setup, and AI Root-Cause Analysis Compared

CloudWatch and Dynatrace are both observability platforms, but they optimize for different operating models. CloudWatch is the native monitoring service for AWS, built to give you fast baseline visibility across AWS resources with minimal setup. Dynatrace is a full-stack observability and security platform designed to work across cloud providers, on-premises infrastructure, and hybrid environments, with AI-assisted root cause analysis built into the platform.

The choice between them comes down to how your team operates. An AWS-native team that needs quick metrics and alarms on their Lambda functions and EC2 instances faces a very different decision than an enterprise running workloads across AWS, Azure, and on-premises data centers with hundreds of microservices.

This comparison evaluates CloudWatch and Dynatrace across five aspects that matter most for this decision -- platform scope, setup effort, AI and root cause workflows, pricing predictability, and team fit.

Scope and Platform Fit

The first question is where your workloads run. CloudWatch is strongest inside AWS. Dynatrace is designed for broader coverage across environments.

CloudWatch

CloudWatch is built into the AWS control plane  and most AWS services (EC2, Lambda, RDS, ECS, S3, and others) publish baseline metrics to CloudWatch automatically. You do not need to install agents or configure exporters to get baseline infrastructure visibility for managed services.

The integrations go beyond metrics collection. CloudWatch alarms can trigger Auto Scaling policies, Lambda functions, EC2 actions, and SNS notifications directly. EventBridge routes CloudWatch alarm state changes into broader event-driven workflows. IAM controls who can view metrics, query logs, and modify alarms. CloudWatch also supports cross-account observability so a central monitoring account can view metrics, logs, and traces from linked source accounts across your AWS Organization.

Where CloudWatch is weaker is outside AWS. It can ingest custom metrics from external sources, but you lose the zero-setup automatic collection that AWS services get. External metrics require you to publish data through the CloudWatch API or agent, configure custom namespaces, and manage the ingestion pipeline yourself. You also lose most of the pre-built dashboards and automatic alarm recommendations that AWS-native services get. Teams with genuine multi-cloud or hybrid footprints typically need an additional layer to unify their monitoring.

Dynatrace

Dynatrace positions itself as a full-stack observability platform that spans cloud, on-premises, and hybrid environments. It uses OneAgent  for host-level discovery and instrumentation, automatically detecting processes, services, and their dependencies across whatever infrastructure you deploy it on.

What sets Dynatrace apart here is Smartscape , Dynatrace's real-time topology engine. Smartscape builds a live map of entity relationships across your infrastructure: which hosts run which processes, which processes expose which services, and how those services communicate. This topology data feeds into Grail , Dynatrace's data lakehouse, where it can be queried alongside metrics, logs, and traces. Together, Smartscape and Grail provide the context that powers Dynatrace's AI-assisted root cause analysis.

For multi-cloud or hybrid estates, Dynatrace provides a single control plane across environments. You can monitor AWS, Azure, GCP, Kubernetes, and on-premises workloads from one tenant. The tradeoff is that reaching full visibility requires deploying OneAgent across your infrastructure, which is more setup work than CloudWatch's zero-config approach for AWS-native services.

Setup and Time-to-Value

How quickly your team gets from "we need monitoring" to "we can actually investigate an incident" shapes the first few weeks with either tool. The onboarding curves are very different.

CloudWatch

For AWS-native workloads, CloudWatch offers a very short path to initial visibility. There is no setup step for most AWS service metrics. The moment you launch an EC2 instance, create a Lambda function, or provision an RDS database, metrics start flowing to CloudWatch automatically.

Logs require slightly more work. Lambda logs flow automatically, but EC2 and on-premises servers need the CloudWatch agent installed and configured. Application tracing requires instrumentation through ADOT  or AWS X-Ray SDKs.

The catch is that while initial setup is fast, the work shifts over time. You need to configure retention policies on log groups (the default is "never expire," which accumulates storage costs), set up meaningful alarms, build dashboards, and establish querying patterns. For AWS-native teams, this gradual ramp is manageable. The signal hygiene and cost governance work grows with your usage.

Dynatrace

Dynatrace requires more upfront investment. You start by provisioning a Dynatrace tenant (either SaaS or Managed), then deploy OneAgent to your hosts. OneAgent auto-discovers the services running on each host and begins collecting metrics, traces, and topology data.

The onboarding involves several decisions: tenant architecture (single vs. multiple environments), agent deployment strategy (host-based, Kubernetes operator, or cloud-native integrations), data governance model, and user access controls. For teams already running Kubernetes, the Dynatrace Operator simplifies deployment across clusters.

Once OneAgent is deployed, the auto-discovery value compounds. You get infrastructure metrics, process-level visibility, distributed traces, and the Smartscape topology map without instrumenting each service individually. The upfront investment pays back when your team can immediately see how a slow database query in one service affects response times three hops away.

The upfront work is primarily tenant setup and OneAgent or Dynatrace Operator rollout. Once deployed, auto-discovery and dependency mapping accelerate triage because your team does not need to manually define service relationships or instrument each application individually.

AI and Root Cause Analysis Workflow

When something breaks, the speed from "we got an alert" to "we know what caused it" depends heavily on which tool your team uses. CloudWatch and Dynatrace take fundamentally different approaches here.

CloudWatch

CloudWatch provides the building blocks for incident investigation, but the workflow is investigator-led. Your team drives the root cause analysis process using CloudWatch's tools.

Anomaly detection uses machine learning to establish dynamic baselines for metrics, so you can alarm on unusual behavior without manually defining thresholds. This works well for metrics with variable patterns (like traffic that spikes during business hours). Composite alarms let you combine multiple alarm states with boolean logic, reducing noise by requiring multiple conditions before triggering -- for example, requiring both "CPU above 90%" and "error rate above 5%" before paging someone.

During an incident, the investigation typically involves querying CloudWatch Logs Insights for error patterns, checking related metrics in the Metrics Explorer, and examining traces in X-Ray. AWS has added correlation features like ServiceLens (which integrates metrics, logs, and traces in the X-Ray trace map) and Application Signals (which auto-collects application metrics and traces for services on EC2, ECS, EKS, and Lambda). These narrow some of the gap, but the broader investigation workflow still involves navigating between separate views and knowing which log group, metric namespace, and trace service to check.

For AWS-native teams with well-structured dashboards and runbooks, this approach works. The limitation shows up in complex distributed systems where the root cause may be several services away from the symptom. Manually tracing the causal chain through multiple CloudWatch views takes time and depends on the investigator's familiarity with the architecture.

Dynatrace

Dynatrace has consolidated its AI capabilities under Dynatrace Intelligence , which combines deterministic analysis (based on the Smartscape topology) with AI-driven pattern detection.

When Dynatrace detects an anomaly, it automatically correlates the event across the topology map. If a spike in response time for Service A is caused by a slow database query in Service C (which Service A calls through Service B), Dynatrace can trace that causal chain automatically because it already knows the dependency relationships from Smartscape.

This shows up in MTTR  reduction for teams with complex microservice architectures. Instead of manually navigating between log views, metric dashboards, and trace explorers, the platform presents a pre-correlated problem analysis that identifies the impacted entity, the root cause entity, and the dependency chain between them.

The value scales with the complexity of your environment. A simple three-service application running on AWS may not benefit enough from Dynatrace's AI to justify the platform cost. An estate with 200+ microservices across multiple environments is where the automated topology analysis saves the most time.

Pricing Model and Cost Predictability

Pricing predictability is one of the most discussed topics in practitioner communities for both tools. They use fundamentally different commercial models.

CloudWatch

CloudWatch pricing is usage-based and split across multiple dimensions, with no upfront platform fee. You pay for what you use across metrics, logs, alarms, dashboards, API calls, and advanced features.

For a detailed breakdown of every CloudWatch pricing axis, see the complete CloudWatch pricing guide. Key pricing axes (US East, N. Virginia, as of February 2026; always verify against the official CloudWatch pricing page):

• Custom metrics: $0.30 per metric per month (first 10,000), decreasing at higher volumes. Most default AWS service metrics are free, but detailed monitoring and custom metrics are billed.
• Logs ingestion: $0.50 per GB for Standard log class after the first 5 GB/month free (Infrequent Access is roughly 50% lower for ingestion).
• Logs storage: $0.03 per GB-month. Logs without explicit retention policies accumulate costs indefinitely.
• Logs Insights queries: $0.005 per GB of data scanned.
• Alarms: $0.10 per standard-resolution alarm metric per month (prorated). Anomaly detection alarms are $0.30/month because they evaluate 3 standard-resolution metrics. Composite alarms are $0.50/alarm-month.
• Dashboards: $3.00 per dashboard per month beyond the first 3 free.

The advantage of this model is low entry friction. Small teams can start monitoring AWS workloads with minimal cost and scale up gradually. The challenge is predictability at scale. Multiple independent billing axes mean that cost estimation requires accounting for metrics volume, log volume, query patterns, alarm count, and API call frequency. Reddit discussions frequently mention surprise CloudWatch bills, particularly from high-cardinality  custom metrics, ungoverned log retention, and heavy Logs Insights querying during incidents.

Governance practices that reduce surprises include enforcing retention policies on every log group, controlling metric cardinality through naming conventions and dimension limits, auditing alarms regularly for staleness, and monitoring CloudWatch spend as its own cost category in Cost Explorer.

Dynatrace

Dynatrace uses a platform subscription model. For a detailed breakdown, see the Dynatrace pricing guide. A Dynatrace Platform Subscription (DPS)  is required, structured as an annual spend commitment with usage accruing toward that commitment throughout the year.

Under DPS, you make a minimum annual spend commitment. Your usage of each Dynatrace capability (infrastructure monitoring, application observability, log management, security, and automation) accrues against that commitment based on the rate card. If you exceed the commitment, you can continue on an on-demand basis, billed monthly at the same rates as pre-paid consumption.

The advantage of this model is unified billing. You are not juggling separate cost axes for metrics, logs, traces, and alarms. The commitment structure also gives enterprise procurement teams a predictable annual spend number.

The tradeoff is higher entry friction. The annual commitment and platform subscription minimum means Dynatrace is typically not the first choice for small teams or early-stage projects. Capacity planning matters because usage that exceeds the annual commitment shifts to on-demand billing at rate-card prices, while over-provisioning means paying for unused capacity. Practitioner discussions on Reddit cite the minimum commitment and the need for careful commercial governance as friction points, even among teams that value the platform's capabilities.

For teams evaluating cost, the question is which cost model fits your operating rhythm. CloudWatch favors teams that want incremental, pay-as-you-go spending with no minimum commitment. Dynatrace favors organizations that can commit to annual spend in exchange for consolidated billing across a broader platform.

Telemetry Correlation and OpenTelemetry Portability

Two secondary considerations often come up in evaluation: how smooth the cross-signal investigation experience is, and how portable your observability investment remains.

Correlation During Incidents

CloudWatch's investigation workflow spans several views: Logs Insights for log queries, Metrics Explorer for metric charts, X-Ray for distributed traces, and ServiceLens for a combined trace map. ServiceLens and Application Signals have improved cross-signal correlation, but for broader investigations that span multiple services and signal types, you still end up navigating between views and piecing together the timeline yourself.

Dynatrace integrates signals through the Smartscape topology. When you investigate an issue, the platform presents metrics, logs, traces, and entity relationships in context. You can navigate from a slow service to its underlying host metrics, to the specific trace that triggered the anomaly, to the related log entries, all within a connected workflow. This reduces the cognitive load during incidents, particularly in environments with many service dependencies.

OpenTelemetry Portability

Both platforms support OpenTelemetry for application instrumentation. If you use OTel SDKs and semantic conventions  for your application telemetry, your instrumentation code stays portable regardless of which backend you choose.

The lock-in risk for both tools lives in the operations layer. CloudWatch-specific constructs (Logs Insights query syntax, CloudWatch alarm actions, dashboard JSON definitions) do not transfer to other platforms. Similarly, Dynatrace-specific workflows (Smartscape topology, Dynatrace Intelligence alerts, DQL queries) are not portable.

For teams evaluating portability, the practical question is how much of your total observability investment is in application instrumentation (portable with OTel) versus operational workflows (platform-specific). The more your team invests in platform-specific query patterns, dashboards, and alert configurations, the higher the switching cost, regardless of which tool you adopt.

CloudWatch vs Dynatrace at a Glance

Where SigNoz Fits

If your team is AWS-first, CloudWatch is usually the fastest baseline. If you run a large multi-environment estate, Dynatrace can reduce triage time with deeper automation. But some teams find that neither model fits their needs exactly.

The patterns that push teams toward evaluating a third option:

1. Your workloads are mostly on AWS today, but you are expanding to another cloud or running some services on-premises. CloudWatch cannot unify that view, and adopting Dynatrace's full platform feels like over-investing for a partially multi-cloud setup.

2. CloudWatch's multi-axis billing is hard to predict, and Dynatrace's annual commitment is too high for your team's current scale. You want usage-based pricing that charges per GB of ingested data without separate axes for metrics, alarms, dashboards, and API calls.

3. During incidents, jumping between separate metric, log, and trace views (in CloudWatch) slows down root cause analysis.

SigNoz is an OpenTelemetry-native observability platform that unifies metrics, traces, and logs in a single application. It supports both cloud and self-hosted deployments.

For AWS-first teams, SigNoz provides one-click AWS integrations to collect CloudWatch metrics and logs. You can also use manual OpenTelemetry collection paths for finer control over what gets forwarded. If you are currently using Dynatrace or evaluating it, keeping your instrumentation OTel-native means you can route the same telemetry to SigNoz without re-instrumenting your applications.

One important caveat: cloud-native forwarding paths (like one-click integrations) still incur provider-side charges for CloudWatch API calls, log delivery, and similar usage. Teams optimizing for cost typically evaluate the manual collection approach alongside one-click setup.

Get Started with SigNoz

You can choose between various deployment options in SigNoz. The easiest way to get started with SigNoz is SigNoz cloud. We offer a 30-day free trial account with access to all features.

Those who have data privacy concerns and can't send their data outside their infrastructure can sign up for either enterprise self-hosted or BYOC offering.

Those who have the expertise to manage SigNoz themselves or just want to start with a free self-hosted option can use our community edition.

Conclusion

The choice between CloudWatch and Dynatrace comes down to your operating model. CloudWatch is the fastest path to monitoring AWS-native workloads, with zero-config metrics, tight AWS automation integration, and no minimum spend commitment. Dynatrace is the stronger choice when your environment spans multiple clouds or has enough microservice complexity that AI-assisted root cause analysis meaningfully reduces incident resolution time.

Both tools require operational discipline. CloudWatch needs proactive cost governance (retention policies, cardinality controls, alarm hygiene). Dynatrace needs careful capacity planning and commercial governance against your annual commitment. Neither is a "set and forget" platform at scale.

If you are evaluating both, run a time-boxed pilot (2-4 weeks) against real workloads. Measure three things: how quickly your team can go from alert to root cause, how predictable the costs are at your expected data volume, and how much operational overhead the tool adds to your existing workflows. Those signals will tell you more than any feature comparison matrix.

Hope we answered all your questions regarding CloudWatch vs Dynatrace. If you have more questions, feel free to use the SigNoz AI chatbot, or join our slack community.

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