Error monitoring is one of those categories where not having it is only obvious in hindsight. When a bug breaks a user flow and you find out from a support ticket three days later, that is the moment teams realize they needed monitoring. Here is how the three leading tools compare.
What each tool is built for
Sentry is an error tracking and performance monitoring platform. It captures exceptions, records the stack trace and request context, groups repeated errors together, and notifies you when new issues appear.
Datadog is a full-stack observability platform. It handles infrastructure monitoring (servers, databases, containers), application performance monitoring, log management, and error tracking. It is the comprehensive enterprise observability option.
LogRocket is a session replay and error tracking platform. Unlike Sentry, which captures errors from code, LogRocket records the user session as a video replay. When an error occurs, you can watch exactly what the user was doing before it happened.
Sentry: the developer's error tracker
Sentry is the most widely used error tracking tool in web development. The integration path is fast: add the Sentry SDK, configure your DSN, and within minutes your application is sending errors to the Sentry dashboard.
What makes Sentry strong:
Issue grouping. Sentry does not just show you individual errors. It groups related errors together, shows you how many users have encountered each issue, and tracks whether issues are new, recurring, or regressing after a fix.
Stack traces with code context. When a JavaScript error fires, Sentry shows you the exact line of code, the surrounding context, the user's browser and OS, and the sequence of breadcrumbs (events that happened before the error). This is the information you need to reproduce and fix the issue.
Release tracking. You can tell Sentry which version of your code is deployed. It then shows you which release introduced a new error and whether a fix you deployed resolved the issue.
Performance monitoring. Sentry tracks transaction times, identifies slow database queries, and flags performance regressions between releases.
Sentry's limitations:
For application performance monitoring at depth (understanding distributed system latency, database query analysis across services, infrastructure metrics), Sentry is less comprehensive than Datadog. It is an error tracker that added performance features, not an observability platform from the ground up.
Pricing: Sentry's free tier handles 5,000 errors per month, which is enough for early-stage products. The Team plan starts at $26 per month for higher volumes and additional features.
Datadog: the enterprise observability platform
Datadog monitors everything. Infrastructure metrics (CPU, memory, disk), application performance traces, logs from every service, network traffic, database query performance, and errors. If it runs in your infrastructure, Datadog can monitor it.
What makes Datadog strong:
Unified observability. When you are diagnosing a production incident, being able to correlate an application error with a spike in database query time with an infrastructure metric in a single view is the most effective way to find the root cause. Datadog does this.
APM (Application Performance Monitoring). Distributed tracing that shows you exactly how a request traveled through your microservices, where time was spent, and which downstream dependency is slow.
Log management. Ingesting, parsing, searching, and alerting on logs from all your services in one place. For teams running multiple services, centralized log management is essential.
Datadog's limitations:
Cost. Datadog is expensive. Per-host pricing for infrastructure, per-GB pricing for logs, per-trace pricing for APM. For a team running 20 production services with significant log volume, Datadog bills in the thousands per month. Many startups start with Datadog and get a pricing shock as they scale.
The platform is complex. Getting full value from Datadog requires investment in setup, learning the query language, and building dashboards. It is not the right choice for teams that need to be operational quickly.
Use Datadog when: You are operating infrastructure at a scale where unified observability is worth the cost, you are in an enterprise with existing Datadog contracts, or you need APM capabilities that Sentry does not provide.
LogRocket: session replay for UX debugging
LogRocket's model is different from both Sentry and Datadog. It records user sessions as video replays and attaches errors, console logs, and network requests to those replays. When a user reports a problem, you watch what they saw.
What makes LogRocket strong:
User experience debugging. When a bug is described vaguely ("it just stopped working"), watching the actual session shows you exactly what happened: what the user clicked, what the UI showed, what errors fired, and what network requests failed.
Identifying UX issues that are not errors. Users sometimes struggle with a flow not because of an error but because the UI is confusing. LogRocket replays surface these patterns without requiring error tracking to trigger.
Product analytics alongside session replay. LogRocket has built-in funnel analysis and cohort comparison, combining product analytics and session replay in one tool.
LogRocket's limitations:
Session recording creates privacy considerations. You are recording user behavior, which requires appropriate disclosure in your privacy policy and consent management. In regulated industries, this requires careful review.
LogRocket is not a replacement for error tracking. It supplements it. A bug that occurs in a specific environment the LogRocket session does not capture still requires Sentry-style error tracking to detect.
Pricing: LogRocket's free tier is 1,000 sessions per month. Teams plans start at $99 per month.
The combination approach
Most mature engineering teams use Sentry and either Datadog or LogRocket together.
Sentry for error tracking (wide coverage, immediate notification, issue grouping).
Datadog for infrastructure and APM if you run distributed services or have complex infrastructure.
LogRocket for UX debugging when you are iterating on product flows and need to understand user behavior alongside technical errors.
Starting point for small teams
If you are deploying your first production application and need something in place quickly, start with Sentry. The free tier covers most early-stage error volumes. The setup is fast. The value is immediate.
Add LogRocket when you are doing active product iteration and want to understand how users actually use the product. Add Datadog when you are at a scale where infrastructure visibility becomes operationally important.
Our custom software development team includes Sentry in production deployments by default. Get in touch to discuss observability as part of a broader application architecture.