Sentry vs CloudWatch: Error Tracking, Monitoring, and When to Use Both

Sentry and AWS CloudWatch solve different problems in the observability stack. Sentry is built for application-level debugging: errors, stack traces, performance traces, session replays, and developer-facing triage workflows. CloudWatch is AWS's native monitoring service for infrastructure metrics, logs, alarms, and operational automation across AWS resources.

The overlap between them is narrow. Teams running on AWS often end up using both, with CloudWatch handling infrastructure health and Sentry handling application-level incident response. This article breaks down where each tool fits and when a hybrid setup makes more sense than picking one.

This comparison evaluates both tools across six areas: ecosystem fit, telemetry coverage, investigation workflows, alerting, OpenTelemetry portability, and cost governance.

Fit and Ecosystem

The two tools are designed for different layers of the stack. Sentry starts from the application and works outward, while CloudWatch starts from the infrastructure and works inward.

Sentry

Sentry's model is issue-centric. When something breaks in your application, Sentry groups related events into issues, attaches stack traces, identifies regression points, and routes ownership to the right team. The workflow is built around developers who need to understand what failed, why, and in which release.

Sentry supports 100+ platforms and frameworks through language-specific SDKs. Onboarding is usually SDK installation and a few lines of initialization code. There is no infrastructure agent to deploy for the core error tracking and performance features.

Sentry is not an infrastructure monitoring tool, though. It does not replace CloudWatch for AWS resource metrics, service health checks, or infrastructure-level automation. If your Lambda function runs out of memory or your RDS instance hits CPU limits, CloudWatch is where that signal lives.

CloudWatch

CloudWatch is embedded in the AWS control plane. Most AWS services (EC2, Lambda, RDS, ECS, S3, and others) publish metrics to CloudWatch automatically. You get baseline infrastructure visibility without installing anything.

Beyond metrics collection, CloudWatch alarms integrate directly with AWS operational services like SNS for notifications, Auto Scaling for capacity adjustments, Lambda for custom remediation, and EventBridge for event-driven workflows. This makes CloudWatch a natural fit for automated infrastructure response.

CloudWatch logs can capture application output, but it does not group errors into issues, attach stack traces with source context, provide session replay, or route incidents to code owners. For developer-facing debugging workflows, teams typically add an application-level tool like Sentry.

Telemetry and Signal Coverage

Both tools collect telemetry, but the signals they prioritize and the depth they offer are quite different.

Sentry

Sentry covers application-layer telemetry across five signal types:

• Errors: Stack traces, exception grouping, regression detection, suspect commits, and code owner assignment.
• Performance traces: Distributed tracing with transaction-level latency breakdowns. Traces connect frontend interactions to backend service calls.
• Logs: Sentry Logs  correlates log data with errors and traces in the same project context.
• Session Replay: Records browser sessions tied to error events, so you can see exactly what the user did before and after a failure.
• Profiling: Captures function-level execution profiles to identify slow code paths in production.

Sentry gives you deep application context across all five signal types. It does not, however, collect infrastructure metrics (CPU, memory, disk, network) from your servers or managed cloud services. For that layer, you still need CloudWatch or another infrastructure monitoring tool.

CloudWatch

CloudWatch covers infrastructure and platform telemetry across AWS services:

• Metrics: Automatic collection from AWS services under service-specific namespaces. Custom metrics require publishing through the CloudWatch API or agent. Metric retention is tiered: <60-second (high-resolution) data is available for 3 hours, 1-minute data for 15 days, 5-minute data for 63 days, and 1-hour data for 455 days.
• Logs: CloudWatch Logs collects log data through log groups and log streams. Lambda logs flow automatically. For EC2 and on-premise servers, you install the CloudWatch agent.
• Tracing/APM: Application Signals  and X-Ray  provide application-level trace data. Application Signals can be enabled via ADOT + CloudWatch Agent, OpenTelemetry SDK + Collector, or X-Ray setups. X-Ray itself requires instrumentation (X-Ray SDK or OTel/ADOT).

CloudWatch's coverage is broad across AWS services, but the application-level debugging context is thinner than Sentry's. You get logs and traces, but not issue grouping, session replay, profiling, or suspect-commit identification.

Query and Investigation Workflow

During an incident, the path from alert to root cause depends on how quickly you can correlate signals and find the failing code. Sentry and CloudWatch take different approaches here.

Sentry

Sentry's investigation workflow follows a connected path: issue -> trace -> replay -> logs. When an error fires, you land on the issue detail page, which shows the stack trace, breadcrumbs (a timeline of events leading to the error), affected users, and the suspect commit. From there, you can jump to the distributed trace that captured the failing request, view the session replay to see what the user experienced, and check correlated logs.

Sentry also provides Discover queries  for ad-hoc analysis across event data. You can filter, aggregate, and visualize error trends, transaction performance, and user impact patterns.

Sentry's investigation depth is strongest for application-level incidents. If the root cause is an infrastructure issue (an overloaded host, a network partition, or a misconfigured security group), Sentry's telemetry may show the symptoms but not the underlying cause.

CloudWatch

CloudWatch provides separate query surfaces for different signal types. Logs Insights supports filtering, parsing, aggregation, and visualization using a purpose-built query syntax. Metrics Insights uses SQL-like queries for cross-namespace metric analysis.

The main friction during investigation is context switching. Metrics, logs, and traces live in separate views. During incident triage, you jump between Logs Insights, the Metrics explorer, and X-Ray trace views to assemble a complete picture. ServiceLens  helps by correlating traces with metrics in a service map, but it requires X-Ray instrumentation and does not cover the full investigation surface.

For infrastructure incidents (resource exhaustion, scaling failures, network issues), CloudWatch's query tools are well-suited. For application-level debugging where you need stack traces, user context, and code-level attribution, the investigation experience is less connected.

Alerting and Incident Response

Sentry

Sentry alerting is built around application events. You can configure alert rules for:

• Issue alerts: Trigger when new issues appear, existing issues regress, or error frequency crosses thresholds.
• Metric alerts: Trigger on transaction performance metrics like p95 latency, throughput, or failure rate.

Sentry's noise reduction comes from issue grouping. Instead of alerting on every individual error event, Sentry groups related events into issues and alerts on the issue-level signal. Combined with ownership rules and environment scoping, this reduces alert fatigue for development teams.

Sentry alerting does not cover infrastructure-level response automation. If you need an alarm to trigger Auto Scaling, reboot an EC2 instance, or invoke a Lambda function for remediation, that stays in CloudWatch.

CloudWatch

CloudWatch alarms evaluate metric conditions and trigger actions:

• Metric alarms fire when a metric crosses a threshold for a specified number of evaluation periods.
• Composite alarms combine multiple alarm states using boolean logic (AND, OR, NOT), reducing noise by requiring multiple conditions before triggering.
• Anomaly detection alarms use machine learning to set dynamic baselines.

Alarm actions integrate directly with AWS services like SNS, Auto Scaling, EC2 actions, and Lambda. This makes CloudWatch alarms a natural extension of AWS operational workflows.

At scale, alarm hygiene becomes a real problem. Teams that create alarms reactively, without regular cleanup, accumulate stale or orphaned alarms that add noise and cost. Community discussions on r/aws frequently mention surprise alarm costs and difficulty tracking which alarms are still useful.

OpenTelemetry and Portability

Both Sentry and CloudWatch support OpenTelemetry, but with different integration patterns.

Sentry

Sentry accepts OpenTelemetry data through its OTel ingestion endpoint. You can instrument applications using standard OTel SDKs and send OpenTelemetry traces (and logs via OTLP) to Sentry alongside or instead of using Sentry's native SDKs. Sentry maps OTel spans to its own transaction and span model, so traces collected via OTel still appear in Sentry's issue and performance views.

If you standardize on OTel instrumentation, you can route application telemetry to Sentry, another backend, or both without re-instrumenting your code. The lock-in surface is in Sentry's issue grouping logic, ownership workflows, and session replay, features that do not have direct OTel equivalents.

CloudWatch

AWS maintains the AWS Distro for OpenTelemetry (ADOT), which lets you instrument applications using OTel APIs and route telemetry to CloudWatch, X-Ray, or third-party backends. Application Signals supports multiple OpenTelemetry/X-Ray-based setups. ADOT is a common/recommended option, but you can also enable it with an OpenTelemetry SDK + Collector or X-Ray instrumentation.

If your instrumentation stays OTel-native, your application code remains portable. You can switch backends by changing the collector configuration. The lock-in risk, as with any platform, lives in the operations layer. Logs Insights queries, alarm configurations, dashboard layouts, and automation workflows are all built on CloudWatch-specific features.

For teams running both tools, OTel instrumentation provides a single collection layer that can feed application telemetry to Sentry for debugging and infrastructure telemetry to CloudWatch for operational monitoring.

Cost Model and Governance

Cost predictability is a recurring concern for both tools, though the cost drivers differ.

Sentry

Sentry pricing is usage-based across several dimensions depending on your plan:

• Errors: Billed by event volume.
• Performance: Billed by transaction/span volume.
• Replays: Billed by replay count.
• Profiling: Billed by profile hours.
• Logs: Billed by log volume.

Sentry offers a free tier (the Developer plan) with limited volume, and paid plans (Team, Business, Enterprise) with increasing volume allotments and features. Overage beyond your plan allotment incurs additional charges. For a detailed breakdown of how Sentry's pricing tiers work and where costs grow, see the complete Sentry pricing guide.

Event volume is the main cost lever to watch. High-traffic applications with noisy client-side errors, verbose logging, or broad replay capture can generate more events than expected. Sampling configuration, inbound data filters, and environment scoping are the primary levers for keeping costs predictable.

CloudWatch

CloudWatch pricing is multi-dimensional. Here are the major axes, based on US East region pricing as of February 2026.

• Custom metrics: $0.30 per metric per month (first 10,000), decreasing at higher volumes. Basic monitoring metrics from AWS services are free, but detailed monitoring and custom metrics are billed.
• Logs ingestion: Starts at $0.50 per GB after the first 5 GB/month free.
• Logs storage: $0.03 per GB-month. Logs stored with "never expire" retention accumulate cost indefinitely.
• Logs Insights queries: $0.005 per GB of data scanned.
• Alarms: $0.10 per standard-resolution alarm metric per month. Anomaly detection alarms cost more because each creates three underlying metrics.
• Dashboards: $3.00 per dashboard per month beyond the first 3 free.

Common cost surprises include log groups without retention policies that accumulate storage indefinitely, high-cardinality custom metrics that multiply metric counts, and heavy Logs Insights queries during incidents that scan large volumes. Community discussions on r/aws highlight billing confusion as a recurring theme.

Governance practices that help include enforcing retention policies on every log group, controlling metric cardinality through naming conventions and dimension limits, auditing alarms regularly, and monitoring CloudWatch spend as its own line item in AWS Cost Explorer. For a full walkthrough of every CloudWatch pricing axis and cost-reduction strategies, see the complete CloudWatch pricing guide.

Sentry vs CloudWatch at a Glance

When to Use Both Together

In practice, many AWS teams run Sentry and CloudWatch side by side. The two tools cover different layers with minimal overlap, and the combination often produces better incident response than either tool alone.

A common architecture pattern:

• CloudWatch handles infrastructure health: AWS service metrics, resource alarms, scaling triggers, and operational automation. It answers "is the infrastructure healthy?" and "are AWS resources behaving as expected?"
• Sentry handles application debugging: error tracking, performance traces, session replays, and developer triage. It answers "what broke in the application?" and "which code change caused it?"

During an incident, the investigation might start in CloudWatch (a Lambda error rate alarm fires), then move to Sentry (find the specific exception, view the stack trace, identify the suspect commit) for resolution.

This hybrid approach works well when:

• Your stack is AWS-heavy and you need native infrastructure automation.
• Your developers need fast error-to-code-fix cycles that CloudWatch alone does not provide.
• You want to keep infrastructure monitoring costs in AWS billing and application debugging costs in a separate, predictable budget.

Unified Alternative: SigNoz

If you are running both Sentry and CloudWatch and finding that the split creates investigation friction, an alternative is to consolidate application and infrastructure telemetry into a single platform.

SigNoz is an OpenTelemetry-native observability platform that unifies metrics, traces, and logs in one application. It covers both infrastructure monitoring and application performance, which reduces the context switching between separate tools during incident triage.

Four scenarios where teams evaluate SigNoz alongside or instead of the Sentry + CloudWatch combination:

1. Investigation context switching. Jumping between Sentry for errors, CloudWatch Logs Insights for infrastructure logs, and X-Ray for traces adds time during incidents. SigNoz correlates all three signal types in a single interface, with trace flamegraphs linked directly to related logs and metrics.

2. Exception monitoring with trace context. SigNoz includes a dedicated Exceptions view that captures exceptions from your OpenTelemetry-instrumented services, groups them by type and service, and links each exception directly to the trace where it occurred. You get the stack trace, the span context, and a one-click path to the full trace flamegraph within the same platform that also handles your infrastructure metrics and logs.

3. Cost simplification. Managing two separate cost models (Sentry event pricing + CloudWatch multi-axis billing) creates budget complexity. SigNoz Cloud Teams starts at $49/month (includes $49 worth of usage). Beyond that, usage is billed at $0.30 per GB for logs and traces, and $0.10 per million metric samples, with no per-host charges or separate alarm/dashboard fees.

4. Vendor portability. SigNoz is built on OpenTelemetry from the ground up, so your instrumentation stays vendor-neutral. If you already use OTel SDKs or ADOT, routing telemetry to SigNoz requires only a collector configuration change.

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

Sentry and CloudWatch serve different jobs. Sentry is strongest when your primary goal is fast application debugging, with errors, traces, replays, and logs connected in a single developer workflow. CloudWatch is strongest when you need native AWS infrastructure monitoring with direct alarm-to-action automation.

Most AWS teams benefit from using both. CloudWatch monitors the platform, Sentry monitors the application, and the combination covers more ground than either tool alone. The decision comes down to understanding which layer each tool serves in your stack.

If the two-tool split becomes an investigation bottleneck, evaluate whether a unified platform like SigNoz can reduce context switching while covering both layers. Run a short pilot (2-4 weeks) against real incidents and measure three things: time from alert to root cause, cost predictability at your telemetry volume, and how much context switching your team experiences during triage. Those signals will tell you more than any feature matrix.

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

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