Datadog vs Sentry - Where Error Tracking Draws the Line and Patterns for Running Both

Not on the Same Footing - Application-Focused Sentry and Integrated-Monitoring Datadog

The first thing to grasp in a Sentry-versus-Datadog comparison is that the two are not products solving the same problem. Sentry, as its official getting-started documentation describes it, "captures errors, logs, traces, replays, profiles, and metrics from applications and connects them with distributed tracing", is a product specialized in delivering what happened inside application code to developers. The first setup step it guides you through is connecting a source code repository, and at its center are estimating the commit that caused an error (Suspect Commits) and linking from a stack trace to the relevant line in the repository (as of September 2026, Source: Getting Started With Sentry). Datadog is integrated monitoring that provides infrastructure monitoring, log management, APM, and RUM as a single product family, and error tracking is one feature within it (Error Tracking). Therefore, if "error tracking alone is all you need", Sentry suffices and the cost is lower. Datadog becomes meaningful when you want to see an error connected all the way to "on which host, after which deployment, and together with which logs it occurred". Having acknowledged this line, to state the conclusion first: teams that operate APM or RUM in Datadog get a single investigation path by consolidating Error Tracking onto Datadog, and for teams that do not, keeping Sentry and streaming events into Datadog is the cost-effective form of running both. Below, we lay out the material in the order of the overlapping areas, the billing differences, where Sentry wins, and the conditions under which Datadog pays off.

The Three Overlapping Areas: Error Tracking, Tracing, and RUM

Mapping the feature names of both exactly as written in their docs, the overlapping areas can be organized into three. The products listed on Sentry's pricing page are Error Monitoring, Tracing, Session Replay, Logs, Application Metrics, Uptime Monitoring, Cron Monitoring, and Profiling. On the Datadog side, these correspond to Error Tracking, APM traces, RUM Session Replay, Log Management, Metrics, and Synthetics (as of September 2026, Source: Sentry Pricing, Datadog Error Tracking).

Datadog's Error Tracking bundles thousands of similar errors into a single Issue and shows the number of affected users, the time of first occurrence, and the commit estimated to be the cause. Errors come from five sources, Browser SDK, Mobile SDK, logs, traces, and RUM events, and the available features vary by source. For example, for errors originating from APM traces, Exception Replay automatically captures the values of variables in the production environment (Source: Datadog Error Tracking). The first takeaway from this table is that the same "bundle into Issues" experience as Sentry also exists on the Datadog side; the second takeaway is the difference in depth of integration, namely whether you can descend from that Issue to the trace, the host, and the logs on the same screen.

Billing Differences - Sentry Charges by Seats and Event Volume, Datadog by the Volume of Monitored Targets

The difference in billing units is where the design philosophies of the two show up directly on the surface. As of September 2026, Sentry's pricing consists of three tiers, the free Developer (1 user) and Team and Business, which are paid as a monthly organization-wide fee (the unit price may differ by billing form, and the default prepaid data volume is included), plus a custom-quoted Enterprise. Each paid plan includes a fixed monthly volume of errors, spans, logs, application metrics, replays, Uptime monitors, and so on, while Developer includes a smaller volume of errors and spans. Anything beyond the included volume is consumed from either Reserved volume, which is prepaid at a discount, or a Pay-as-you-go (PAYG) budget shared across all categories, and once both are exhausted, subsequent data is dropped and not billed (Source: Sentry Pricing, Sentry Pricing & Billing).

Datadog's core monitoring SKUs (Infrastructure / APM / Logs / RUM) have no per-user billing (some products such as On-Call are seat-billed), and you pay by the volume of monitored targets. APM is a monthly price per host (added on top of the Infrastructure Monitoring per-host price), and RUM is priced per 1,000 sessions, with tiers of Measure, which is aggregation-centric, Investigate, which digs deeper into filtered sessions, and the Session Replay add-on (Source: Datadog Pricing). Because Error Tracking is a feature that uses these APM / RUM / Log Management as its pathways, this article does not treat a standalone unit price for it and compares on the assumption that the pathway SKUs are under contract. Since the units differ, "seats + event count" versus "hosts + sessions", there is little point in lining up the same amounts; what should be compared is "which unit grows in your own team". Even with 30 developers, Datadog's bill does not change, and even with 30 hosts, Sentry's bill does not change. The SKU-stacking structure covered in How to Decode the Pricing Structure and Sentry's included-volume + PAYG structure are exact opposites in this respect. This article explains how billing works. For current unit prices and plans, refer to each vendor's official pricing page.

Where Sentry Wins: Developer Experience, Free Allowance, and Low Entry Price

As facts to acknowledge up front, there are three points where Sentry clearly wins. First is the developer experience. Sentry places connecting a source code repository as the first step, and makes estimating the causing commit, linking from a stack trace to the relevant line in the repository, and readable stack traces through applying source maps and debug symbols its standard path. AI-driven root cause analysis and automatic generation of fix PRs (Seer) are also available as an additional charge (as of September 2026, Source: Getting Started With Sentry, Sentry Pricing). A design that shortens the distance to "what a developer who saw the error fixes next" is the strength of an error tracking specialist. Second is the free allowance: the Developer plan, though limited to 1 user, includes a fixed volume of errors and spans, and lets you use Error Monitoring and Tracing for free. For solo development or small services, this alone completes the operation. Third is the entry price: the Team plan is a single monthly organization-wide fee with unlimited users. If a team of 20 developers wants only error tracking, Sentry's monthly cost starts at the Team base fee regardless of headcount, whereas holding the same range in Datadog puts the host / session billing of APM and RUM first (Source: Sentry Pricing). In addition, the design that drops subsequent data and stops billing once the PAYG budget is exhausted, and Spike Protection, which automatically drops events during a surge, enable an operation of "set the billing ceiling, then use it" (Source: Sentry Quota Management). It is a trade-off of protecting the bill at the cost of losing monitoring, but for organizations that want to set a budget ceiling first, this clarity is effective.

Where Datadog Pays Off: Connecting Errors with Infrastructure, Logs, and Traces on One Screen

The value on the Datadog side is hard to see in a feature comparison of Error Tracking alone, and appears in correlation scenarios. First is the path for isolating the cause. From a Datadog Issue you can descend to the span of the relevant trace, from the span to the metrics of that host, and to the logs at the same time, all within a single screen. Being able to determine whether an error is "a bug in the code", "memory exhaustion on that host", or "latency in a dependent DB" without switching tools shortens investigation time. The feature by which Exception Replay automatically captures the values of production variables for errors originating from APM traces also rides on this path (as of September 2026, Source: Datadog Error Tracking). Next is unified notification: Monitors can be set on Error Tracking events, so "a new Issue appeared" and "error volume exceeded a threshold" can be received through the same route and the same on-call schedule as infrastructure and SLO alerts. Finally, tag consistency: because the three tags env / service / version carry the same meaning across metrics, logs, traces, and RUM, you can write the question "which services saw more errors after this version was deployed" as a single query. On the cost side, in an environment that already contracts APM or RUM with Datadog, Error Tracking can be used on top of those pathways, so there is the practical simplicity of one fewer invoice compared with holding Sentry under a separate contract (check the billing conditions for Error Tracking on the pricing page). Conversely, for a team that has neither APM nor RUM in Datadog, contracting Datadog just for Error Tracking is not a good fit, because host or session billing comes first. It is safe to say outright that Datadog pays off in the scenario where "the center of monitoring is already in Datadog".

Patterns for Running Both, and the Conditions for Committing to Replacement

The realistic options are two, "replace" and "run both", and running both has established patterns. Datadog's Sentry integration forwards Sentry events to Datadog's log / event intake via the Webhook of Sentry's Internal Integrations, and the legacy Webhook integration has been deprecated (as of September 2026, Source: Sentry Integration). Forwarded events can be viewed on the Datadog side alongside deployment events and infrastructure metrics, and can also be the target of monitors, so a division of labor of "error details in Sentry, correlation and notification in Datadog" holds up.

The conditions for committing to replacement are when the following four are all met. First, backend APM is in Datadog. Without this, there is no error pathway on the Datadog side. Second, if you need to see frontend errors, RUM is also in Datadog. Third, replacements are ready on the Datadog side for the features developers rely on in Sentry's Issue screen (causing commit, link to repository, AI fixes). Datadog also has an Error Tracking integration for JetBrains IDEs, but judge only after actually trying it with the languages and IDEs your team uses. Fourth, the timing of Sentry's contract renewal is near. A monitoring switch requires a parallel running period, so starting Pattern B 2 to 3 months before renewal is realistic. If conditions 1 and 2 of the four cannot be met, staying with Pattern A, running both and gathering only correlation and notification in Datadog, is the choice where cost and effect balance.

Decide Between Replacement and Running Both with Five Questions

Finally, we summarize five questions for deciding between replacement and running both.

A simple criterion is sufficient: if 3 or more of the 5 questions lean toward Datadog, proceed to considering replacement; otherwise, stay with Pattern A of running both. Whichever you choose, the Webhook integration that streams Sentry events to Datadog is worth putting in first, because it lets you measure the effect of correlation before deciding the next step. The steps for trying for free are in Free Plan and Trial, and the overall cost-benefit of adoption is covered in the Adoption Checklist.