Datadog vs Sentry Comparison in 2025

As software systems become distributed, scalable, complex and dynamic, observability tools become essential to ensure smooth functioning of the systems, early detection of the issues and quick resolution. Sentry and Datadog are two main observability tools which is used in the modern DevOps workflow. Let’s see the detailed comparison of Datadog and Sentry in this blog.

What is Sentry?

Sentry is an open-source , developer-focused tool built for error tracking and lightweight performance monitoring. It helps developers in track, diagnose, and resolve bugs in real-time by capturing exceptions, crashes, and performance bottlenecks. Originally built for Python and Django, Sentry has grown since to support many other languages, platforms, and frameworks.

Key Features of Sentry

1. Real-time error tracking

   Sentry reports and catches unhandled exceptions and application crashes in real time automatically. It provides more context like stack traces, user data, HTTP request information, and environment metadata like OS or browser details. It even has integration with version control systems like GitHub, GitLab, or Bitbucket such that it is capable of showing the actual source code with the error.

2. Performance Monitoring (APM-Lite)

   Sentry is a lightweight application performance monitoring tool which helps to track slow transactions such as API calls, database queries, and frontend rendering issues. It detects performance bottlenecks like N+1 queries in Django or slow component loading in React.js.

3. Debugging

   Sentry closely integrates with Git to offer rich debugging information. Sentry can attribute an error to a precise commit, pull request, or developer that last changed the problematic line (through git blame). Sentry can even display code diffs and which deployment introduced a bug, which significantly speeds up root cause analysis and enables quicker fixes.

4. Release and Deployment tracking

   With Git integration, Sentry ties errors to individual releases and deployments and enables teams to backtrace bugs to the precise version or change where they were introduced. It uses commit history and, if configured, the CODEOWNERS file to automatically assign errors to the right team or developer.

5. Open Source & Self-Hostable

   Sentry is open-source and provides a self-hosting option, making it best for organizations with high data governance or data privacy requirements. While the majority of teams want to utilize Sentry's cloud-hosted solution due to its simplicity, the on-premise deployment provides full control over the infrastructure, customizations, and data storage especially important to businesses or regulated industries.

What is Datadog?

Datadog is a cloud-native observability platform focused on monitoring the performance (like monitoring CPU and memory) and health of your applications, infrastructure, and networks (network usage of EC2 instances and Kubernetes pods). It is capable of error tracking and provides a overall view of your system through metrics, logs, traces, synthetic testing, and more.

Key Features of Datadog

1. Infrastructure Monitoring

   Datadog offers a robust, cloud-native platform to gather, analyze, and visualize infrastructure-level data in real-time. Datadog employs a lightweight agent run on servers, virtual machines, or containers. The agent collects System metrics, Network metrics, Process-level metrics, Container metrics, Cloud metrics.

2. Application Performance Monitoring (APM)

   Datadog Application Performance Monitoring provides deep application behavior visibility, microservice latency breakdown, flame graphs, and distributed traces. It records the journey of a request from frontend to backend entirely with performance bottlenecks and errors in real time.

3. Log Monitoring

   Datadog offers a centralized logging solution that collects logs from wide range of sources like Kubernetes clusters, cloud providers like AWS, Azure, GCP, and other custom softwares. These logs include rich metadata and can be easily searched with an optimized search language to allow quick filtering, pattern matching, and correlation with traces and metrics.

4. Dashboards and Visualizations

   Datadog provides real-time, customizable dashboards to help you visualize logs, metrics, traces, and events in overall system. Interactive views can be created using drag-and-drop widgets like time series charts, heat maps, status tables, top lists, and gauges.

5. Security Monitoring

   Datadog security monitoring utilizes logs and metrics to detect threats, suspicious activity, or cloud-native misconfiguration. It provides integration with security vendors and cloud providers to ingest security events and enforce compliance.

Sentry vs Datadog: A Detailed Comparison

Key Consideration: When to Choose Sentry or Datadog

Choose Sentry if

• Your group focuses on resolving code-level issues and improving application quality.

• You require quicker, real-time error reporting with complete stack traces and detailed debugging information.

• You need a lightweight performance monitoring tool for API calls, database queries, or slow React components.

• You are in need for a cost-efficient, developer-friendly tool that integrates with version control systems, CI/CD as well as project management tools.

• You are looking for a tool that can be self-hosted for privacy concerns or compliance reasons.

Choose Datadog if

• You require full-stack observability tool that brings log monitoring, metrics, traces, APM, security, and synthetic monitoring all in a single platform.

• Your objective involves infrastructure health monitoring, network performance, distributed tracing, and system-level anomalies.

• You need a tool with machine learning-based alerting, dashboards, and advanced incident management capabilities.

• You need extensive support for cloud integrations (AWS, Azure, GCP), Kubernetes, and service auto-discovery.

• You can handle a more expensive priced tool for your requirements like end-to-end visibility and scalability.

Pros and Cons of Sentry

Pros

• Offers real-time reporting of errors with rich in contextual information on errors (stack trace, user session, environment) which makes the debugging process way easier.

• Developer-focused tool with support for GitHub, GitLab, Bitbucket integrations for authorship of code and correlation of issues.

• Lightweight application performance monitoring tool that detects code bottlenecks in the frontend as well as the backend.

• Automatic correlation of errors to minimize alert duplication.

Cons

• Offers limited infrastructure observability features and not much effective on system-level monitoring (containers, memory, CPU).

• The performance monitoring is basic when compared to enterprise-grade APM tools like Datadog.

• Not preferable for DevOps or SRE use cases requiring metrics, logs, and distributed tracing on entire application layer.

• Datadog is less desirable for incident correlation on multiple services or infrastructure components.

Pros and Cons of Datadog

Pros

• Offers end-to-end monitoring capabilities for applications, infrastructure, logs, security, and user experience.

• Advanced APM and distributed tracing for latency detection, bottlenecks, and inter-level service dependencies.

• Provides machine learning-driven alerting, anomaly detection, and forecasting.

• Massive integration ecosystem (600+ tools) supporting AWS, Azure, GCP, Docker, Kubernetes, and more.

Cons

• Can become expensive at scale since it charges based on pricing per host, log volume, and capabilities.

• Hard on new users who lack expertise in infrastructure and cloud monitoring.

• Heavy configuration for organizations that don't have a dedicated DevOps team or SRE.

• Overhead in lightweight projects, especially where complete observability is not necessary.

How to Automate Tests Without Writing Them

In this blog, we explored how platforms such as Datadog and Sentry increase observability. Let's now focus on test automation. There are numerous tools for test automation but the majority of them still need you to code. That's where Keploy, a no-code testing platform, enters the scene simplifying test automation, making it quicker and more developer-friendly.

Keploy is an open-source tool created with developers in mind, making it easy to automate the creation of realistic test cases and mocks by capturing traffic from your running application. Unlike common testing tools where developers have to manually write test cases, Keploy records API calls and their responses automatically, turning them into test suites that can be replayed during CI runs.

How Keploy Works

Keploy functions as a test recorder. It records actual API calls issued to your application, including the request, response, and any external dependencies like database queries. It then automatically generates:

• Unit tests — Target isolated logic, with mocks imitating database or API responses.

• Integration Tests — These tests use real data and context from live traffic to ensure all components work together.

• API Tests — These tests validates endpoint behavior and ensure schema consistency.

This will enable you to execute your application as usual, either in local development or QA testing, and Keploy will automatically create test cases from actual interaction. No longer do you need to write tests from scratch.

To Know more about Keploy: Checkout here

Conclusion: Which tool is Right for You?

It's entirely your team's call whether to use Sentry or Datadog, depending on your team's goals, the complexity of your system, and your observability needs. If you're a development squad dedicated to catching bugs early on, debugging quickly, and tracking app performance at the code level, then Sentry is the perfect fit whereas if you're working with distributed systems, containers, cloud infrastructure, and want a single platform to monitor metrics, traces, logs, uptime, and security, then Datadog is the best choice.

FAQs

1. Can I use Sentry and Datadog together for increased observability?

   Yes, you can use both Sentry and Datadog together. Sentry takes care of error tracking and performance monitoring at the code level, while Datadog provides you a broader perspective of your infrastructure and entire system. You can configure Sentry to send alerts to Datadog or have them both part of a single observability workflow.

2. Is Sentry a full APM tool like Datadog?

   No, Sentry only provides APM-lite features focused around application-level performance, like slow API calls or frontend render problems. Datadog offers full APM features with distributed tracing, service maps, and advanced latency analysis and is hence geared towards microservices and higher-level architectures.

3. Does Datadog offer error tracking like Sentry?

   Datadog has the ability to track errors through logs and APM traces but lacks rich debugging capabilities like stack traces, code context, or commit linking. Since each tool is specialized for different purposes, one cannot replace the other.

4. Which is more cost-effective for small teams or startups?

   Sentry is the cheaper option compared to Datadog, with free tier and event volume-based pricing. Datadog pricing can escalate rapidly based on the number of hosts, metrics, and logs, and is best for mature teams with more extensive budgets.

5. Do both tools offer custom alerting and dashboards?

   Yes, both of the tools have custom dashboards and alerting. Sentry's features are mostly error-driven, whereas Datadog's features offer alerting based on metrics, anomalies, log patterns, and SLO (Service Level Objectives).